Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

Evaluation and selection of the ship collaborative design resources based on AHP and genetic and simulated annealing algorithm

The characteristics of the design resources in the ship collaborative design is described and the hierarchical model for the evaluation of the design resources is established. The comprehensive evaluation of the co-designers for the collaborative design resources has been done from different aspects using Analytic Hierarchy Process （AHP） ,and according to the evaluation results,the candidates are determined. Meanwhile,based on the principle of minimum cost,and starting from the relations between the design tasks and the corresponding co-designers,the optimizing selection model of the collaborators is established and one novel genetic combined with simulated annealing algorithm is proposed to realize the optimization. It overcomes the defects of the genetic algorithm which may lead to the premature convergenee and local optimization if used individually. Through the application of this method in the ship collaborative design system,it proves the feasibility and provides a quantitative method for the optimizing selection of the design resources.

Improved Ant Colony-Genetic Algorithm for Information Transmission Path Optimization in Remanufacturing Service System

The information transmission path optimization(ITPO) can often a ect the e ciency and accuracy of remanufactur?ing service. However, there is a greater degree of uncertainty and complexity in information transmission of remanu?facturing service system, which leads to a critical need for designing planning models to deal with this added uncer?tainty and complexity. In this paper, a three?dimensional(3D) model of remanufacturing service information network for information transmission is developed, which combines the physic coordinate and the transmitted properties of all the devices in the remanufacturing service system. In order to solve the basic ITPO in the 3D model, an improved 3D ant colony algorithm(Improved AC) was put forward. Moreover, to further improve the operation e ciency of the algorithm, an improved ant colony?genetic algorithm(AC?GA) that combines the improved AC and genetic algorithm was developed. In addition, by taking the transmission of remanufacturing service demand information of certain roller as example, the e ectiveness of AC?GA algorithm was analyzed and compared with that of improved AC, and the results demonstrated that AC?GA algorithm was superior to AC algorithm in aspects of information transmission delay, information transmission cost, and rate of information loss.

Ant colony algorithm based on genetic method for continuous optimization problem

A new algorithm is presented by using the ant colony algorithm based on genetic method （ACG） to solve the continuous optimization problem. Each component has a seed set. The seed in the set has the value of component, trail information and fitness. The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed. The genetic method is used to form new solutions from the solutions got by the ants. Best solutions are selected to update the seeds in the sets and trail information of the seeds. In updating the trail information, a diffusion function is used to achieve the diffuseness of trail information. The new algorithm is tested with 8 different benchmark functions.

Electro-Hydraulic Servo System Identification of Continuous Rotary Motor Based on the Integration Algorithm of Genetic Algorithm and Ant Colony Optimization

In order to increase the robust performance of electro-hydraulic servo system, the system transfer function was identified by the intergration algorithm of genetic algorithm and ant colony optimization(GA-ACO), which was based on standard genetic algorithm and combined with positive feedback mechanism of ant colony algorithm. This method can obtain the precise mathematic model of continuous rotary motor which determines the order of servo system. Firstly, by constructing an appropriate fitness function, the problem of system parameters identification is converted into the problem of system parameter optimization. Secondly, in the given upper and lower bounds a set of optimal parameters are selected to meet the best approximation of the actual system. And the result shows that the identification output can trace the sampling output of actual system, and the error is very small. In addition, another set of experimental data are used to test the identification result. The result shows that the identification parameters can approach the actual system. The experimental results verify the feasibility of this method. And it is fit for the parameter identification of general complex system using the integration algorithm of GA-ACO.

Ant Colony Optimization Approach Based Genetic Algorithms for Multiobjective Optimal Power Flow Problem under Fuzziness

In this paper, a new optimization system based genetic algorithm is presented. Our approach integrates the merits of both ant colony optimization and genetic algorithm and it has two characteristic features. Firstly, since there is instabilities in the global market, implications of global financial crisis and the rapid fluctuations of prices, a fuzzy representation of the optimal power flow problem has been defined, where the input data involve many parameters whose possible values may be assigned by the expert. Secondly, by enhancing ant colony optimization through genetic algorithm, a strong robustness and more effectively algorithm was created. Also, stable Pareto set of solutions has been detected, where in a practical sense only Pareto optimal solutions that are stable are of interest since there are always uncertainties associated with efficiency data. The results on the standard IEEE systems demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective OPF.

Optimization of Fairhurst-Cook Model for 2-D Wing Cracks Using Ant Colony Optimization (ACO), Particle Swarm Intelligence (PSO), and Genetic Algorithm (GA)

The common failure mechanism for brittle rocks is known to be axial splitting which happens parallel to the direction of maximum compression. One of the mechanisms proposed for modelling of axial splitting is the sliding crack or so called, “wing crack” model. Fairhurst-Cook model explains this specific type of failure which starts by a pre-crack and finally breaks the rock by propagating 2-D cracks under uniaxial compression. In this paper, optimization of this model has been considered and the process has been done by a complete sensitivity analysis on the main parameters of the model and excluding the trends of their changes and also their limits and “peak points”. Later on this paper, three artificial intelligence algorithms including Particle Swarm Intelligence (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA) has been used and compared in order to achieve optimized sets of parameters resulting in near-maximum or near-minimum amounts of wedging forces creating a wing crack.

Power Line Communications Networking Method Based on Hybrid Ant Colony and Genetic Algorithm

When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.

Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design

An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information. The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. The results of function optimization show that the algorithm has good searching ability and high convergence speed. The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum. In order to avoid the combinatorial explosion of fuzzy rules due tσ multivariable inputs, a state variable synthesis scheme is employed to reduce the number of fuzzy rules greatly. The simulation results show that the designed controller can control the inverted pendulum successfully.

Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.

Joint Resource Allocation Using Evolutionary Algorithms in Heterogeneous Mobile Cloud Computing Networks

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

Genetic algorithm for short-term scheduling of make-and-pack batch production process

This paper considers a scheduling problem in industrial make-and-pack batch production process. This process equips with sequence-dependent changeover time, multipurpose storage units with limited capacity, storage time, batch splitting, partial equipment connectivity and transfer time. The objective is to make a production plan to satisfy all constraints while meeting demand requirement of packed products from various product families. This problem is NP-hard and the problem size is exponentially large for a realistic-sized problem. Therefore,we propose a genetic algorithm to handle this problem. Solutions to the problems are represented by chromosomes of product family sequences. These sequences are decoded to assign the resource for producing packed products according to forward assignment strategy and resource selection rules. These techniques greatly reduce unnecessary search space and improve search speed. In addition, design of experiment is carefully utilized to determine appropriate parameter settings. Ant colony optimization and Tabu search are also implemented for comparison. At the end of each heuristics, local search is applied for the packed product sequence to improve makespan. In an experimental analysis, all heuristics show the capability to solve large instances within reasonable computational time. In all problem instances, genetic algorithm averagely outperforms ant colony optimization and Tabu search with slightly longer computational time.

New Hybrid Algorithm Based on BicriterionAnt for Solving Multiobjective Green Vehicle Routing Problem

The main objective of this paper is to propose a new hybrid algorithm for solving the Bi objective green vehicle routing problem (BGVRP) from the BicriterionAnt metaheuristic. The methodology used is subdivided as follows: first, we introduce data from the GVRP or instances from the literature. Second, we use the first cluster route second technique using the k-means algorithm, then we apply the BicriterionAntAPE (BicriterionAnt Adjacent Pairwise Exchange) algorithm to each cluster obtained. And finally, we make a comparative analysis of the results obtained by the case study as well as instances from the literature with some existing metaheuristics NSGA, SPEA, BicriterionAnt in order to see the performance of the new hybrid algorithm. The results show that the routes which minimize the total distance traveled by the vehicles are different from those which minimize the CO2 pollution, which can be understood by the fact that the objectives are conflicting. In this study, we also find that the optimal route reduces product CO2 by almost 7.2% compared to the worst route.

Elite-guided equilibrium optimiser based on information enhancement:Algorithm and mobile edge computing applications

The Equilibrium Optimiser(EO)has been demonstrated to be one of the metaheuristic algorithms that can effectively solve global optimisation problems.Balancing the paradox between exploration and exploitation operations while enhancing the ability to jump out of the local optimum are two key points to be addressed in EO research.To alleviate these limitations,an EO variant named adaptive elite-guided Equilibrium Optimiser(AEEO)is introduced.Specifically,the adaptive elite-guided search mechanism enhances the balance between exploration and exploitation.The modified mutualism phase reinforces the information interaction among particles and local optima avoidance.The cooperation of these two mechanisms boosts the overall performance of the basic EO.The AEEO is subjected to competitive experiments with state-of-the-art algorithms and modified algorithms on 23 classical benchmark functions and IEE CEC 2017 function test suite.Experimental results demonstrate that AEEO outperforms several well-performing EO variants,DE variants,PSO variants,SSA variants,and GWO variants in terms of convergence speed and accuracy.In addition,the AEEO algorithm is used for the edge server(ES)placement problem in mobile edge computing(MEC)environments.The experimental results show that the author’s approach outperforms the representative approaches compared in terms of access latency and deployment cost.

融合AntNet与遗传算法的动态网络路由算法

提出了一种新的动态分布式网络路由算法。在AntNet算法中引入了路径遗传运算(GA),提出了新的信息素更新策略。对蚂蚁发现的路径进行染色体编码,并用适应度函数对其进行适应度评价,通过路径交叉和路径变异运算以及种群的不断进化,来提高解的质量。仿真结果表明,所提出的算法能快速收敛,且有效地提高了网络吞吐量、降低了平均延时。

面向区域配电网规划的源网荷储优化配置算法

针对区域配电网规划的源网荷储优化扩容问题,文中提出了一种规划解决方案。通过安装和加固高压或中压变电站、馈线段、分布式发电源和存储单元,扩大网络容量,并将系统的成本目标函数在技术约束下实现最小化。同时,对于解决配电网规划问题的常用优化算法进行了分析和比较,提出了改进的配电网规划算法。在实际应用中对算法进行比较,验证了所提改进算法的有效性,并对3个试验配电网进行了数值研究。数值分析结果表明,所提出的混合遗传/蚁群系统算法可以实现约21.74%的负载削减。

Application of GA, PSO, and ACO Algorithms to Path Planning of Autonomous Underwater Vehicles

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a nnmerical solution of a nonlinear optimal control problem （NOCP）. An energy performance index as a cost function, which should be minimized, was defmed. The resulting problem was a two-point boundary value problem （TPBVP）. A genetic algorithm （GA）, particle swarm optimization （PSO）, and ant colony optimization （ACO） algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

A Survey on the Vehicle Routing Problem and Its Variants

In this paper, we have conducted a literature review on the recent developments and publications involving the vehicle routing problem and its variants, namely vehicle routing problem with time windows (VRPTW) and the capacitated vehicle routing problem (CVRP) and also their variants. The VRP is classified as an NP-hard problem. Hence, the use of exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. The vehicle routing problem comes under combinatorial problem. Hence, to get solutions in determining routes which are realistic and very close to the optimal solution, we use heuristics and meta-heuristics. In this paper we discuss the various exact methods and the heuristics and meta-heuristics used to solve the VRP and its variants.

Evolutionary Algorithms in Software Defined Networks: Techniques, Applications, and Issues

A software defined networking(SDN) system has a logically centralized control plane that maintains a global network view and enables network-wide management, optimization, and innovation. Network-wide management and optimization problems are typicallyvery complex with a huge solution space, large number of variables, and multiple objectives. Heuristic algorithms can solve theseproblems in an acceptable time but are usually limited to some particular problem circumstances. On the other hand, evolutionaryalgorithms(EAs), which are general stochastic algorithms inspired by the natural biological evolution and/or social behavior of species, can theoretically be used to solve any complex optimization problems including those found in SDNs. This paper reviewsfour types of EAs that are widely applied in current SDNs: Genetic Algorithms(GAs), Particle Swarm Optimization(PSO), Ant Colony Optimization(ACO), and Simulated Annealing(SA) by discussing their techniques, summarizing their representative applications, and highlighting their issues and future works. To the best of our knowledge, our work is the first that compares the tech-niques and categorizes the applications of these four EAs in SDNs.