This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks （HMB）. Due to the limitation o...This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks （HMB）. Due to the limitation of its local search ability of genetic algorithm （GA） in solving a massive combinatorial optimization problem, simulated annealing （SA） is combined, the multi-parameter concatenated coding is adopted, and the memory function is added. Thus a hybrid genetic-simulated annealing with memory function is formed. Examples show that the modified algorithm can improve the local search ability in the solution space, and the solution quality.展开更多
Circles packing problem is an NP-hard problem and is di?cult to solve. In this paper, ahybrid search strategy for circles packing problem is discussed. A way of generating new configurationis presented by simulating t...Circles packing problem is an NP-hard problem and is di?cult to solve. In this paper, ahybrid search strategy for circles packing problem is discussed. A way of generating new configurationis presented by simulating the moving of elastic objects, which can avoid the blindness of simulatedannealing search and make iteration process converge fast. Inspired by the life experiences of people,an e?ective personified strategy to jump out of local minima is given. Based on the simulatedannealing idea and personification strategy, an e?ective personified annealing algorithm for circlespacking problem is developed. Numerical experiments on benchmark problem instances show thatthe proposed algorithm outperforms the best algorithm in the literature.展开更多
As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weigh...As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.展开更多
We propose a new constructive algorithm, called HAPE3 D, which is a heuristic algorithm based on the principle of minimum total potential energy for the 3D irregular packing problem, involving packing a set of irregul...We propose a new constructive algorithm, called HAPE3 D, which is a heuristic algorithm based on the principle of minimum total potential energy for the 3D irregular packing problem, involving packing a set of irregularly shaped polyhedrons into a box-shaped container with fixed width and length but unconstrained height. The objective is to allocate all the polyhedrons in the container, and thus minimize the waste or maximize profit. HAPE3 D can deal with arbitrarily shaped polyhedrons, which can be rotated around each coordinate axis at different angles. The most outstanding merit is that HAPE3 D does not need to calculate no-fit polyhedron(NFP), which is a huge obstacle for the 3D packing problem. HAPE3 D can also be hybridized with a meta-heuristic algorithm such as simulated annealing. Two groups of computational experiments demonstrate the good performance of HAPE3 D and prove that it can be hybridized quite well with a meta-heuristic algorithm to further improve the packing quality.展开更多
Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of th...Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.展开更多
Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many r...Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.展开更多
This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning(MRP)systems.Three evolutionary algo...This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning(MRP)systems.Three evolutionary algorithms(simulated annealing(SA),particle swarm optimization(PSO)and genetic algorithm(GA))are provided.For evaluating the performances of algorithms,the distribution of total cost(objective function)and the average computational time are compared.As a result,both GA and PSO have better cost performances with lower average total costs and smaller standard deviations.When the scale of the multilevel lot-sizing problem becomes larger,PSO is of a shorter computational time.展开更多
A design synthesis technique based on sensitivity for Micro-Electro-Mechanical Systems (MEMS) proposed. This new technique can be called Sensitivity-Based Direct Solution Algorithm (DSA) of design synthesis for MEMS w...A design synthesis technique based on sensitivity for Micro-Electro-Mechanical Systems (MEMS) proposed. This new technique can be called Sensitivity-Based Direct Solution Algorithm (DSA) of design synthesis for MEMS with expected performance. Design synthesis with expected performance is regarded as a reverse problem of MEMS analysis. Behavior equation group can be educed from analysis equations. Solving the behavior equation group only need L design variables, L is number of desired behaviors. This behavior equation group can be solved using any solution algorithm of non-linear equation group. Newton Iteration Method based on sensitivity is adopted. Comparing with Genetic Optimization Algorithm (GA) and Simulated Annealing Optimization Algorithm (SA), computational workload of DSA is greatly decreased. For instance, synthesis computation of a meandering resonator only needs 4 iterations (17 analyses);computational time is decreased from 7~8 hours to less than 30 seconds.展开更多
基金Project supported by the National Natural Science Foundation of China （Grant No.50375023）
文摘This paper establishes a mathematical model of multi-objective optimization with behavior constraints in solid space based on the problem of optimal design of hydraulic manifold blocks （HMB）. Due to the limitation of its local search ability of genetic algorithm （GA） in solving a massive combinatorial optimization problem, simulated annealing （SA） is combined, the multi-parameter concatenated coding is adopted, and the memory function is added. Thus a hybrid genetic-simulated annealing with memory function is formed. Examples show that the modified algorithm can improve the local search ability in the solution space, and the solution quality.
文摘Circles packing problem is an NP-hard problem and is di?cult to solve. In this paper, ahybrid search strategy for circles packing problem is discussed. A way of generating new configurationis presented by simulating the moving of elastic objects, which can avoid the blindness of simulatedannealing search and make iteration process converge fast. Inspired by the life experiences of people,an e?ective personified strategy to jump out of local minima is given. Based on the simulatedannealing idea and personification strategy, an e?ective personified annealing algorithm for circlespacking problem is developed. Numerical experiments on benchmark problem instances show thatthe proposed algorithm outperforms the best algorithm in the literature.
基金Project (Nos. 2006BAK04A02-02 and 2006BAK02B02-08) sup-ported by the National Key Technology R&D Program, China
文摘As the idea of simulated annealing (SA) is introduced into the fitness function, an improved genetic algorithm (GA) is proposed to perform the optimal design of a pressure vessel which aims to attain the minimum weight under burst pressure con- straint. The actual burst pressure is calculated using the arc-length and restart analysis in finite element analysis (FEA). A penalty function in the fitness function is proposed to deal with the constrained problem. The effects of the population size and the number of generations in the GA on the weight and burst pressure of the vessel are explored. The optimization results using the proposed GA are also compared with those using the simple GA and the conventional Monte Carlo method.
基金supported by the Natural Science Foundation of Guangdong Province,China(No.S2013040016594)the Natural Science Foundation of Liaoning Province,China(No.201102164)the Fundamental Research Funds for the Central Universities,China(No.2013ZM0124)
文摘We propose a new constructive algorithm, called HAPE3 D, which is a heuristic algorithm based on the principle of minimum total potential energy for the 3D irregular packing problem, involving packing a set of irregularly shaped polyhedrons into a box-shaped container with fixed width and length but unconstrained height. The objective is to allocate all the polyhedrons in the container, and thus minimize the waste or maximize profit. HAPE3 D can deal with arbitrarily shaped polyhedrons, which can be rotated around each coordinate axis at different angles. The most outstanding merit is that HAPE3 D does not need to calculate no-fit polyhedron(NFP), which is a huge obstacle for the 3D packing problem. HAPE3 D can also be hybridized with a meta-heuristic algorithm such as simulated annealing. Two groups of computational experiments demonstrate the good performance of HAPE3 D and prove that it can be hybridized quite well with a meta-heuristic algorithm to further improve the packing quality.
基金supported by National Social Science Foundation of China under the project of 18BGL003.
文摘Purpose–Flexible job-shop scheduling is significant for different manufacturing industries nowadays.Moreover,consideration of transportation time during scheduling makes it more practical and useful.The purpose of this paper is to investigate multi-objective flexible job-shop scheduling problem(MOFJSP)considering transportation time.Design/methodology/approach–A hybrid genetic algorithm(GA)approach is integrated with simulated annealing to solve the MOFJSP considering transportation time,and an external elitism memory library is employed as a knowledge library to direct GA search into the region of better performance.Findings–The performance of the proposed algorithm is tested on different MOFJSP taken from literature.Experimental results show that proposed algorithm performs better than the original GA in terms of quality of solution and distribution of the solution,especially when the number of jobs and the flexibility of the machine increase.Originality/value–Most of existing studies have not considered the transportation time during scheduling of jobs.The transportation time is significantly desired to be included in the FJSP when the time of transportation of jobs has significant impact on the completion time of jobs.Meanwhile,GA is one of primary algorithms extensively used to address MOFJSP in literature.However,to solve the MOFJSP,the original GA has a possibility to get a premature convergence and it has a slow convergence speed.To overcome these problems,a new hybrid GA is developed in this paper.
文摘Team Formation(TF)is considered one of the most significant problems in computer science and optimization.TF is defined as forming the best team of experts in a social network to complete a task with least cost.Many real-world problems,such as task assignment,vehicle routing,nurse scheduling,resource allocation,and airline crew scheduling,are based on the TF problem.TF has been shown to be a Nondeterministic Polynomial time(NP)problem,and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms.This paper proposes two improved swarm-based algorithms for solving team formation problem.The first algorithm,entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm(HBOSA),uses a single crossover operator to improve the performance of a standard heap-based optimizer(HBO)algorithm.It also employs the simulated annealing(SA)approach to improve model convergence and avoid local minima trapping.The second algorithm is the Chaotic Heap-based Optimizer Algorithm(CHBO).CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space.During HBO’s optimization process,a logistic chaotic map is used.The performance of the two proposed algorithms(HBOSA)and(CHBO)is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills.Furthermore,the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer(HBO),Developed Simulated Annealing(DSA),Particle SwarmOptimization(PSO),GreyWolfOptimization(GWO),and Genetic Algorithm(GA).Finally,the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database(IMDB).The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance,with fast convergence to the global minimum.
基金the National Natural Science Foundation of China(No.70971017)the Humanities and Social Sciences Project of Ministry of Education(No.10YJC630009)+1 种基金the Social Science Fund of Zhejiang Province(No.10CGGL21YBQ)the Natural Science Foundation of Zhejiang Province(No.Y1100854)
文摘This paper presents a comparative study of evolutionary algorithms which are considered to be effective in solving the multilevel lot-sizing problem in material requirement planning(MRP)systems.Three evolutionary algorithms(simulated annealing(SA),particle swarm optimization(PSO)and genetic algorithm(GA))are provided.For evaluating the performances of algorithms,the distribution of total cost(objective function)and the average computational time are compared.As a result,both GA and PSO have better cost performances with lower average total costs and smaller standard deviations.When the scale of the multilevel lot-sizing problem becomes larger,PSO is of a shorter computational time.
文摘A design synthesis technique based on sensitivity for Micro-Electro-Mechanical Systems (MEMS) proposed. This new technique can be called Sensitivity-Based Direct Solution Algorithm (DSA) of design synthesis for MEMS with expected performance. Design synthesis with expected performance is regarded as a reverse problem of MEMS analysis. Behavior equation group can be educed from analysis equations. Solving the behavior equation group only need L design variables, L is number of desired behaviors. This behavior equation group can be solved using any solution algorithm of non-linear equation group. Newton Iteration Method based on sensitivity is adopted. Comparing with Genetic Optimization Algorithm (GA) and Simulated Annealing Optimization Algorithm (SA), computational workload of DSA is greatly decreased. For instance, synthesis computation of a meandering resonator only needs 4 iterations (17 analyses);computational time is decreased from 7~8 hours to less than 30 seconds.
