A new nonlinear optimization approach with discrete design variables based on linear estimation is proposed, in which the complex engineering structures optimization problems, such as 3-D space plate-system structures...A new nonlinear optimization approach with discrete design variables based on linear estimation is proposed, in which the complex engineering structures optimization problems, such as 3-D space plate-system structures optimization problems, can be solved. By avoided using point -by-point searching strategy, which is usually used in other discrete optimization algorithms, the times of function calculation is greatly reduced.展开更多
Local search methods are convenient alternatives for solving discrete optimization problems(DOPs).These easy-to-implement methods are able to find approximate optimal solutions within a tolerable time limit.It is know...Local search methods are convenient alternatives for solving discrete optimization problems(DOPs).These easy-to-implement methods are able to find approximate optimal solutions within a tolerable time limit.It is known that the quality of the initial solution greatly affects the quality of the approximated solution found by a local search method.In this paper,we propose to take the initial solution as a random variable and learn its preferable probability distribution.The aim is to sample a good initial solution from the learned distribution so that the local search can find a high-quality solution.We develop two different deep network models to deal with DOPs established on set(the knapsack problem)and graph(the maximum clique problem),respectively.The deep neural network learns the representation of an optimization problem instance and transforms the representation to its probability vector.Experimental results show that given the initial solution sampled from the learned probability distribution,a local search method can acquire much better approximate solutions than the randomly-sampled initial solution on the synthesized knapsack instances and the Erd?s-Rényi random graph instances.Furthermore,with sampled initial solutions,a classical genetic algorithm can achieve better solutions than a random initialized population in solving the maximum clique problems on DIMACS instances.Particularly,we emphasize that the developed models can generalize in dimensions and across graphs with various densities,which is an important advantage on generalizing deep-learning-based optimization algorithms.展开更多
Continuumtopology optimization considering the vibration response is of great value in the engineering structure design.The aimof this studyis toaddress the topological designoptimizationof harmonic excitationstructur...Continuumtopology optimization considering the vibration response is of great value in the engineering structure design.The aimof this studyis toaddress the topological designoptimizationof harmonic excitationstructureswith minimumlength scale control to facilitate structuralmanufacturing.Astructural topology design based on discrete variables is proposed to avoid localized vibration modes,gray regions and fuzzy boundaries in harmonic excitation topology optimization.The topological design model and sensitivity formulation are derived.The requirement of minimum size control is transformed into a geometric constraint using the discrete variables.Consequently,thin bars,small holes,and sharp corners,which are not conducive to the manufacturing process,can be eliminated from the design results.The present optimization design can efficiently achieve a 0–1 topology configuration with a significantly improved resonance frequency in a wide range of excitation frequencies.Additionally,the optimal solution for harmonic excitation topology optimization is not necessarily symmetric when the load and support are symmetric,which is a distinct difference fromthe static optimization design.Hence,one-half of the design domain cannot be selected according to the load and support symmetry.Numerical examples are presented to demonstrate the effectiveness of the discrete variable design for excitation frequency topology optimization,and to improve the design manufacturability.展开更多
A dynamic model of gear transmission system which includes time-varying meshing stiffness,meshing damp and transmission error is established.Next,the analytical solution of the vibration response would be obtained by ...A dynamic model of gear transmission system which includes time-varying meshing stiffness,meshing damp and transmission error is established.Next,the analytical solution of the vibration response would be obtained by solving the dynamic model based on the harmonic balance method.And on this basis,a dynamic performance discrete optimization model of transmission sub-system would be constructed,which sets objective function up from vibration acceleration and the total mass of transmission sub-system,treats the module,number of teeth and helix angle as design variables,and takes gear strength and assembly relationship as constraints.Last,the optimal solution of design variables could be obtained through the multivariable mixed discrete optimization program which based on the branch-bound algorithm.The results show that the vibration acceleration and the total mass of transmission sub-system reduce by 34.6%and 6.8%,respectively.展开更多
For the purpose of solving the engineering constrained discrete optimization problem, a novel discrete particle swarm optimization(DPSO) is proposed. The proposed novel DPSO is based on the idea of normal particle s...For the purpose of solving the engineering constrained discrete optimization problem, a novel discrete particle swarm optimization(DPSO) is proposed. The proposed novel DPSO is based on the idea of normal particle swarm optimization(PSO), but deals with the variables as discrete type, the discrete optimum solution is found through updating the location of discrete variable. To avoid long calculation time and improve the efficiency of algorithm, scheme of constraint level and huge value penalty are proposed to deal with the constraints, the stratagem of reproducing the new particles and best keeping model of particle are employed to increase the diversity of particles. The validity of the proposed DPSO is examined by benchmark numerical examples, the results show that the novel DPSO has great advantages over current algorithm. The optimum designs of the 100-1 500 mm bellows under 0.25 MPa are fulfilled by DPSO. Comparing the optimization results with the bellows in-service, optimization results by discrete penalty particle swarm optimization(DPPSO) and theory solution, the comparison result shows that the global discrete optima of bellows are obtained by proposed DPSO, and confirms that the proposed novel DPSO and schemes can be used to solve the engineering constrained discrete problem successfully.展开更多
Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one...Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.展开更多
As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optim...As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optimization(DLSO) algorithm is proposed to solve the TSP. Firstly, we introduce discrete coding and order crossover operators in DLSO. Secondly, we use the complete 2-opt(C2-opt) algorithm to enhance the local search ability.Then in order to enhance the efficiency of the algorithm, a parallel discrete lion swarm optimization(PDLSO) algorithm is proposed.The PDLSO has multiple populations, and each sub-population independently runs the DLSO algorithm in parallel. We use the ring topology to transfer information between sub-populations. Experiments on some benchmarks TSP problems show that the DLSO algorithm has a better accuracy than other algorithms, and the PDLSO algorithm can effectively shorten the running time.展开更多
The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existi...The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.展开更多
This paper addresses the problem of service composition in military organization cloud cooperation(MOCC). Military service providers(MSP) cooperate together to provide military resources for military service users...This paper addresses the problem of service composition in military organization cloud cooperation(MOCC). Military service providers(MSP) cooperate together to provide military resources for military service users(MSU). A group of atom services, each of which has its level of quality of service(QoS), can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users' will. In this paper a method based on discrete particle swarm optimization(DPSO) is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU's requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization(PSO) and genetic algorithm(GA).展开更多
Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains ...Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO,but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.展开更多
The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies.Early diagnosis of many diseases will improve the patient ...The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies.Early diagnosis of many diseases will improve the patient life.The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation methods.The data generated through these technologies will demand the bandwidth,data rate,latency of the network.In this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the patient.The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO.Aggregation scheme will reduce the number of transmissions over the network.The aggregated data are preprocessed at edge node to remove the noise for better diagnosis.Edge node will reduce the overhead of cloud server.The aggregated data are forward to cloud server for central storage and diagnosis.This proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the system.Energy cost for proposed system for 300 nodes is 0.34μJ.Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ respectively.The optimization approaches and encryption method will ensure the data privacy.展开更多
Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzz...Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.展开更多
For the multi-parameter discrete exponential family,we construct an empirical Bayes(EB)estimator of the vector-valued parameterθ.under some conditions,this estimator is proved to be asymptotically optimal.
Particle swarm optimization (PSO) is one of the most popular population-based stochastic algorithms for solving complex optimization problems. While PSO is simple and effective, it is originally defined in continuou...Particle swarm optimization (PSO) is one of the most popular population-based stochastic algorithms for solving complex optimization problems. While PSO is simple and effective, it is originally defined in continuous space. In order to take advantage of PSO to solve combinatorial optimization problems in discrete space, the set-based PSO (S-PSO) framework extends PSO for discrete optimization by redefining the operations in PSO utilizing the set operations. Since its proposal, S-PSO has attracted increasing research attention and has become a promising approach for discrete optimization problems. In this paper, we intend to provide a comprehensive survey on the concepts, development and applications of S-PSO. First, the classification of discrete PSO algorithms is presented. Then the S-PSO framework is given. In particular, we will give an insight into the solution construction strategies, constraint handling strategies, and alternative reinforcement strategies in S-PSO together with its different variants. Furthermore, the extensions and applications of S-PSO are also discussed systemically. Some potential directions for the research of S-PSO are also discussed in this paper.展开更多
In this paper we consider modeling techniques for the mathematical puzzle KenKen. It is an interesting puzzle from modeling point of view since it has different kind of mathematical restrictions that are not trivial t...In this paper we consider modeling techniques for the mathematical puzzle KenKen. It is an interesting puzzle from modeling point of view since it has different kind of mathematical restrictions that are not trivial to express as linear constraints. We give an integer program for solving KenKen and and its implementation on modeling language AMPL. Our integer program uses an innovative way for converting product restrictions into linear constraints. It can be also used for teaching various integer programming techniques in an Operations Research course.展开更多
Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatoria...Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data compression.Some approximation algorithms and heuristics algorithms have been proposed for the problem.Firefly algorithm is a new meta-heuristic algorithm.Because of its simplicity and easy implementation,it has been successfully applied in various fields.However,the basic firefly algorithm is not suitable for discrete problems.To this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete problems.Computational results show that the algorithm has good performance.The algorithm can be extended to solve other discrete optimization problems.展开更多
This paper presents a new multiobjective discrete optimization method for the engineering design of dynamic problems.A discrete combinatorial optimization problem is solved using a particle swarm optimization algorith...This paper presents a new multiobjective discrete optimization method for the engineering design of dynamic problems.A discrete combinatorial optimization problem is solved using a particle swarm optimization algorithm coupled with a stair‐form interpolation model.To address multiobjective optimization issues,a weighted average approach is implemented to convert the multiobjective optimization problem into an equivalent single‐objective optimization problem.Design con-straints are taken into consideration by using the penalty function strategy.The proposed method is first verified with a 10‐bar truss structure design problem,where the cross‐sectional area of each bar is optimized to minimize both volume and node displacement.Second,the dynamic issue for hybrid composite laminates is investigated by maximizing the fundamental frequency and minimizing the cost.The results reveal that the optimized results generated by the proposed method agree well with those from other approaches.展开更多
Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation dur...Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air combat.However,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios.As such,this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs.The weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement.An encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation model.The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function.The resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)problem.As such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a solution.The occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two engagement.Simulated results showed these solutions varied with initial conditions and weighting coefficients.This occupation process,based on formation switching,effectively demonstrates the viability of the proposed technique.These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.展开更多
Polarity optimization for mixed polarity Reed-Muller(MPRM) circuits is a combinatorial issue.Based on the study on discrete particle swarm optimization(DPSO) and mixed polarity,the corresponding relation between p...Polarity optimization for mixed polarity Reed-Muller(MPRM) circuits is a combinatorial issue.Based on the study on discrete particle swarm optimization(DPSO) and mixed polarity,the corresponding relation between particle and mixed polarity is established,and the delay-area trade-off of large-scale MPRM circuits is proposed. Firstly,mutation operation and elitist strategy in genetic algorithm are incorporated into DPSO to further develop a hybrid DPSO(HDPSO).Then the best polarity for delay and area trade-off is searched for large-scale MPRM circuits by combining the HDPSO and a delay estimation model.Finally,the proposed algorithm is testified by MCNC Benchmarks.Experimental results show that HDPSO achieves a better convergence than DPSO in terms of search capability for large-scale MPRM circuits.展开更多
This paper considers discrete global optimization problems.The traditional definition of the discrete filled function is modified in this paper.Based on the modified definition,a new discrete filled function is presen...This paper considers discrete global optimization problems.The traditional definition of the discrete filled function is modified in this paper.Based on the modified definition,a new discrete filled function is presented and an algorithm for discrete global optimization is developed from the discrete filled function.Numerical experiments reported in this paper on several test problems with up to 200 variables have demonstrated the efficiency of the algorithm.展开更多
文摘A new nonlinear optimization approach with discrete design variables based on linear estimation is proposed, in which the complex engineering structures optimization problems, such as 3-D space plate-system structures optimization problems, can be solved. By avoided using point -by-point searching strategy, which is usually used in other discrete optimization algorithms, the times of function calculation is greatly reduced.
基金supported by National Natural Science Foundation of China(Grant Nos.11991023 and 62076197)Key Research and Development Project of Shaanxi Province(Grant No.2022GXLH01-15)。
文摘Local search methods are convenient alternatives for solving discrete optimization problems(DOPs).These easy-to-implement methods are able to find approximate optimal solutions within a tolerable time limit.It is known that the quality of the initial solution greatly affects the quality of the approximated solution found by a local search method.In this paper,we propose to take the initial solution as a random variable and learn its preferable probability distribution.The aim is to sample a good initial solution from the learned distribution so that the local search can find a high-quality solution.We develop two different deep network models to deal with DOPs established on set(the knapsack problem)and graph(the maximum clique problem),respectively.The deep neural network learns the representation of an optimization problem instance and transforms the representation to its probability vector.Experimental results show that given the initial solution sampled from the learned probability distribution,a local search method can acquire much better approximate solutions than the randomly-sampled initial solution on the synthesized knapsack instances and the Erd?s-Rényi random graph instances.Furthermore,with sampled initial solutions,a classical genetic algorithm can achieve better solutions than a random initialized population in solving the maximum clique problems on DIMACS instances.Particularly,we emphasize that the developed models can generalize in dimensions and across graphs with various densities,which is an important advantage on generalizing deep-learning-based optimization algorithms.
基金supported by the National Natural Science Foundation of China (12002218 and 12032008)the Youth Foundation of Education Department of Liaoning Province (Grant No.JYT19034).
文摘Continuumtopology optimization considering the vibration response is of great value in the engineering structure design.The aimof this studyis toaddress the topological designoptimizationof harmonic excitationstructureswith minimumlength scale control to facilitate structuralmanufacturing.Astructural topology design based on discrete variables is proposed to avoid localized vibration modes,gray regions and fuzzy boundaries in harmonic excitation topology optimization.The topological design model and sensitivity formulation are derived.The requirement of minimum size control is transformed into a geometric constraint using the discrete variables.Consequently,thin bars,small holes,and sharp corners,which are not conducive to the manufacturing process,can be eliminated from the design results.The present optimization design can efficiently achieve a 0–1 topology configuration with a significantly improved resonance frequency in a wide range of excitation frequencies.Additionally,the optimal solution for harmonic excitation topology optimization is not necessarily symmetric when the load and support are symmetric,which is a distinct difference fromthe static optimization design.Hence,one-half of the design domain cannot be selected according to the load and support symmetry.Numerical examples are presented to demonstrate the effectiveness of the discrete variable design for excitation frequency topology optimization,and to improve the design manufacturability.
基金the Chongqing Research Program of Basic Research and Frontier technology No.cstc2016jcyjA0514 and No.cstc2015jcyjA70012Scientific and Technological Research Program of Chongqing Municipal Education Commission No.KJ1600503National Natural Science Foundation of P.R.China under Contract No.51375519 and No.51405048.
文摘A dynamic model of gear transmission system which includes time-varying meshing stiffness,meshing damp and transmission error is established.Next,the analytical solution of the vibration response would be obtained by solving the dynamic model based on the harmonic balance method.And on this basis,a dynamic performance discrete optimization model of transmission sub-system would be constructed,which sets objective function up from vibration acceleration and the total mass of transmission sub-system,treats the module,number of teeth and helix angle as design variables,and takes gear strength and assembly relationship as constraints.Last,the optimal solution of design variables could be obtained through the multivariable mixed discrete optimization program which based on the branch-bound algorithm.The results show that the vibration acceleration and the total mass of transmission sub-system reduce by 34.6%and 6.8%,respectively.
基金supported by National Hi-tech Research and Development Program of China (Grant No. 2006aa042439)
文摘For the purpose of solving the engineering constrained discrete optimization problem, a novel discrete particle swarm optimization(DPSO) is proposed. The proposed novel DPSO is based on the idea of normal particle swarm optimization(PSO), but deals with the variables as discrete type, the discrete optimum solution is found through updating the location of discrete variable. To avoid long calculation time and improve the efficiency of algorithm, scheme of constraint level and huge value penalty are proposed to deal with the constraints, the stratagem of reproducing the new particles and best keeping model of particle are employed to increase the diversity of particles. The validity of the proposed DPSO is examined by benchmark numerical examples, the results show that the novel DPSO has great advantages over current algorithm. The optimum designs of the 100-1 500 mm bellows under 0.25 MPa are fulfilled by DPSO. Comparing the optimization results with the bellows in-service, optimization results by discrete penalty particle swarm optimization(DPPSO) and theory solution, the comparison result shows that the global discrete optima of bellows are obtained by proposed DPSO, and confirms that the proposed novel DPSO and schemes can be used to solve the engineering constrained discrete problem successfully.
基金National Natural Science Foundations of China(No. 61103175,No. 11141005)Technology Innovation Platform Project of Fujian Province,China (No. 2009J1007)+1 种基金Key Project Development Foundation of Education Committee of Fujian Province,China (No.JA11011)Project Development Foundations of Fuzhou University,China (No. 2010-XQ-21,No. XRC-1037)
文摘Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.
基金supported by the National Natural Science Foundation of China(61771293)the Key Project of Shangdong Province(2019JZZY010111)。
文摘As a typical representative of the NP-complete problem, the traveling salesman problem(TSP) is widely utilized in computer networks, logistics distribution, and other fields. In this paper, a discrete lion swarm optimization(DLSO) algorithm is proposed to solve the TSP. Firstly, we introduce discrete coding and order crossover operators in DLSO. Secondly, we use the complete 2-opt(C2-opt) algorithm to enhance the local search ability.Then in order to enhance the efficiency of the algorithm, a parallel discrete lion swarm optimization(PDLSO) algorithm is proposed.The PDLSO has multiple populations, and each sub-population independently runs the DLSO algorithm in parallel. We use the ring topology to transfer information between sub-populations. Experiments on some benchmarks TSP problems show that the DLSO algorithm has a better accuracy than other algorithms, and the PDLSO algorithm can effectively shorten the running time.
基金Supported by the National Natural Science Foundation of China (No. 90818007)
文摘The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS's usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO's recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.
基金supported by the National Natural Science Foundation of China(61573283)
文摘This paper addresses the problem of service composition in military organization cloud cooperation(MOCC). Military service providers(MSP) cooperate together to provide military resources for military service users(MSU). A group of atom services, each of which has its level of quality of service(QoS), can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users' will. In this paper a method based on discrete particle swarm optimization(DPSO) is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU's requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization(PSO) and genetic algorithm(GA).
文摘Deep deterministic policy gradient(DDPG)has been proved to be effective in optimizing particle swarm optimization(PSO),but whether DDPG can optimize multi-objective discrete particle swarm optimization(MODPSO)remains to be determined.The present work aims to probe into this topic.Experiments showed that the DDPG can not only quickly improve the convergence speed of MODPSO,but also overcome the problem of local optimal solution that MODPSO may suffer.The research findings are of great significance for the theoretical research and application of MODPSO.
基金This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea(grant number:HI21C1831)the Soonchunhyang University Research Fund.
文摘The conventional hospital environment is transformed into digital transformation that focuses on patient centric remote approach through advanced technologies.Early diagnosis of many diseases will improve the patient life.The cost of health care systems is reduced due to the use of advanced technologies such as Internet of Things(IoT),Wireless Sensor Networks(WSN),Embedded systems,Deep learning approaches and Optimization and aggregation methods.The data generated through these technologies will demand the bandwidth,data rate,latency of the network.In this proposed work,efficient discrete grey wolf optimization(DGWO)based data aggregation scheme using Elliptic curve Elgamal with Message Authentication code(ECEMAC)has been used to aggregate the parameters generated from the wearable sensor devices of the patient.The nodes that are far away from edge node will forward the data to its neighbor cluster head using DGWO.Aggregation scheme will reduce the number of transmissions over the network.The aggregated data are preprocessed at edge node to remove the noise for better diagnosis.Edge node will reduce the overhead of cloud server.The aggregated data are forward to cloud server for central storage and diagnosis.This proposed smart diagnosis will reduce the transmission cost through aggrega-tion scheme which will reduce the energy of the system.Energy cost for proposed system for 300 nodes is 0.34μJ.Various energy cost of existing approaches such as secure privacy preserving data aggregation scheme(SPPDA),concealed data aggregation scheme for multiple application(CDAMA)and secure aggregation scheme(ASAS)are 1.3μJ,0.81μJ and 0.51μJ respectively.The optimization approaches and encryption method will ensure the data privacy.
基金supported by The National Natural Science Foundation of China under Grant Nos.61402517, 61573375The Foundation of State Key Laboratory of Astronautic Dynamics of China under Grant No. 2016ADL-DW0302+2 种基金The Postdoctoral Science Foundation of China under Grant Nos. 2013M542331, 2015M572778The Natural Science Foundation of Shaanxi Province of China under Grant No. 2013JQ8035The Aviation Science Foundation of China under Grant No. 20151996015
文摘Aiming at the problems of convergence-slow and convergence-free of Discrete Particle Swarm Optimization Algorithm(DPSO) in solving large scale or complicated discrete problem, this article proposes Intuitionistic Fuzzy Entropy of Discrete Particle Swarm Optimization(IFDPSO) and makes it applied to Dynamic Weapon Target Assignment(WTA). First, the strategy of choosing intuitionistic fuzzy parameters of particle swarm is defined, making intuitionistic fuzzy entropy as a basic parameter for measure and velocity mutation. Second, through analyzing the defects of DPSO, an adjusting parameter for balancing two cognition, velocity mutation mechanism and position mutation strategy are designed, and then two sets of improved and derivative algorithms for IFDPSO are put forward, which ensures the IFDPSO possibly search as much as possible sub-optimal positions and its neighborhood and the algorithm ability of searching global optimal value in solving large scale 0-1 knapsack problem is intensified. Third, focusing on the problem of WTA, some parameters including dynamic parameter for shifting firepower and constraints are designed to solve the problems of weapon target assignment. In addition, WTA Optimization Model with time and resource constraints is finally set up, which also intensifies the algorithm ability of searching global and local best value in the solution of WTA problem. Finally, the superiority of IFDPSO is proved by several simulation experiments. Particularly, IFDPSO, IFDPSO1~IFDPSO3 are respectively effective in solving large scale, medium scale or strict constraint problems such as 0-1 knapsack problem and WTA problem.
文摘For the multi-parameter discrete exponential family,we construct an empirical Bayes(EB)estimator of the vector-valued parameterθ.under some conditions,this estimator is proved to be asymptotically optimal.
基金Acknowledgements This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 61622206 and 61379061), in part by the Natural Science Foundation of Guangdong (2015A030306024), in part by the Guangdong Special Support Program (2014TQ01X550), and in part by the Guangzhou Pearl River New Star of Science and Technology (201506010002).
文摘Particle swarm optimization (PSO) is one of the most popular population-based stochastic algorithms for solving complex optimization problems. While PSO is simple and effective, it is originally defined in continuous space. In order to take advantage of PSO to solve combinatorial optimization problems in discrete space, the set-based PSO (S-PSO) framework extends PSO for discrete optimization by redefining the operations in PSO utilizing the set operations. Since its proposal, S-PSO has attracted increasing research attention and has become a promising approach for discrete optimization problems. In this paper, we intend to provide a comprehensive survey on the concepts, development and applications of S-PSO. First, the classification of discrete PSO algorithms is presented. Then the S-PSO framework is given. In particular, we will give an insight into the solution construction strategies, constraint handling strategies, and alternative reinforcement strategies in S-PSO together with its different variants. Furthermore, the extensions and applications of S-PSO are also discussed systemically. Some potential directions for the research of S-PSO are also discussed in this paper.
文摘In this paper we consider modeling techniques for the mathematical puzzle KenKen. It is an interesting puzzle from modeling point of view since it has different kind of mathematical restrictions that are not trivial to express as linear constraints. We give an integer program for solving KenKen and and its implementation on modeling language AMPL. Our integer program uses an innovative way for converting product restrictions into linear constraints. It can be also used for teaching various integer programming techniques in an Operations Research course.
基金This work is supported by the National Natural Science Foundation of China under Grant 61772179the Hunan Provincial Natural Science Foundation of China under Grant 2019JJ40005+3 种基金the Science and Technology Plan Project of Hunan Province under Grant 2016TP1020the Double First-Class University Project of Hunan Province under Grant Xiangjiaotong[2018]469the Open Fund Project of Hunan Provincial Key Laboratory of Intelligent Information Processing and Application for Hengyang Normal University under Grant IIPA19K02the Science Foundation of Hengyang Normal University under Grant 19QD13.
文摘Given a connected undirected graph G whose edges are labeled,the minimumlabeling spanning tree(MLST)problemis to find a spanning tree of G with the smallest number of different labels.TheMLST is anNP-hard combinatorial optimization problem,which is widely applied in communication networks,multimodal transportation networks,and data compression.Some approximation algorithms and heuristics algorithms have been proposed for the problem.Firefly algorithm is a new meta-heuristic algorithm.Because of its simplicity and easy implementation,it has been successfully applied in various fields.However,the basic firefly algorithm is not suitable for discrete problems.To this end,a novel discrete firefly algorithm for the MLST problem is proposed in this paper.A binary operation method to update firefly positions and a local feasible handling method are introduced,which correct unfeasible solutions,eliminate redundant labels,and make the algorithm more suitable for discrete problems.Computational results show that the algorithm has good performance.The algorithm can be extended to solve other discrete optimization problems.
基金The National Natural Science Foundation of China,Grant/Award Number:11972082supported by the National Science Foundation of China(No.11972082)the Beijing Institute of Technology Research Fund Program for Young Scholars,China.
文摘This paper presents a new multiobjective discrete optimization method for the engineering design of dynamic problems.A discrete combinatorial optimization problem is solved using a particle swarm optimization algorithm coupled with a stair‐form interpolation model.To address multiobjective optimization issues,a weighted average approach is implemented to convert the multiobjective optimization problem into an equivalent single‐objective optimization problem.Design con-straints are taken into consideration by using the penalty function strategy.The proposed method is first verified with a 10‐bar truss structure design problem,where the cross‐sectional area of each bar is optimized to minimize both volume and node displacement.Second,the dynamic issue for hybrid composite laminates is investigated by maximizing the fundamental frequency and minimizing the cost.The results reveal that the optimized results generated by the proposed method agree well with those from other approaches.
基金supported by the National Natural Science Foundation of China(No.61573286)the Aeronautical Science Foundation of China(No.20180753006)+2 种基金the Fundamental Research Funds for the Central Universities(3102019ZDHKY07)the Natural Science Foundation of Shaanxi Province(2020JQ-218)the Shaanxi Province Key Laboratory of Flight Control and Simulation Technology。
文摘Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air combat.However,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios.As such,this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs.The weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement.An encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation model.The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function.The resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)problem.As such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a solution.The occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two engagement.Simulated results showed these solutions varied with initial conditions and weighting coefficients.This occupation process,based on formation switching,effectively demonstrates the viability of the proposed technique.These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.
基金supported by the National Natural Science Foundation of China(No.61076032)the Natural Science Foundation of Zhejiang Province,China(Nos.Z1111219,LY13F040003,LY 12D06002)+1 种基金the Ningbo Natural Science Fund,China(No.2010A610175)the K. C.Wong Magna Fund in Ningbo University,China
文摘Polarity optimization for mixed polarity Reed-Muller(MPRM) circuits is a combinatorial issue.Based on the study on discrete particle swarm optimization(DPSO) and mixed polarity,the corresponding relation between particle and mixed polarity is established,and the delay-area trade-off of large-scale MPRM circuits is proposed. Firstly,mutation operation and elitist strategy in genetic algorithm are incorporated into DPSO to further develop a hybrid DPSO(HDPSO).Then the best polarity for delay and area trade-off is searched for large-scale MPRM circuits by combining the HDPSO and a delay estimation model.Finally,the proposed algorithm is testified by MCNC Benchmarks.Experimental results show that HDPSO achieves a better convergence than DPSO in terms of search capability for large-scale MPRM circuits.
基金This work was supported by the National Natural Science Foundation of China(No.11471062)Ningxia Foundation for Key Disciplines of Computational Mathematics.
文摘This paper considers discrete global optimization problems.The traditional definition of the discrete filled function is modified in this paper.Based on the modified definition,a new discrete filled function is presented and an algorithm for discrete global optimization is developed from the discrete filled function.Numerical experiments reported in this paper on several test problems with up to 200 variables have demonstrated the efficiency of the algorithm.