Some issues of designing genetic algorithms for traveling salesman problems

This paper demonstrates that a robust genetic algorithm for the traveling salesman problem (TSP) should preserve and add good edges efficiently, and at the same time, maintain the population diversity well. We analyzed the strengths and limitations of several well-known genetic operators for TSPs by the experiments. To evaluate these factors, we propose a new genetic algorithm integrating two genetic operators and a heterogeneous pairing selection. The former can preserve and add good edges efficiently and the later will be able to keep the population diversity. The proposed approach was evaluated on 15 well-known TSPs whose numbers of cities range from 101 to 13509. Experimental results indicated that our approach, somewhat slower, performs very robustly and is very competitive with other approaches in our best surveys. We believe that a genetic algorithm can be a stable approach for TSPs if its operators can preserve and add edges efficiently and it maintains population diversity.     

一种改进的求解旅行商问题的单亲遗传算法

单亲遗传算法具有操作简单、收敛较快等优点,因此被用于求解各种组合优化问题。针对旅行商问题,在早期单亲遗传算法的基础上引入了基因段贪心替换、基于相似度的家族竞争等一些控制策略,提出了一种新型、高效的单亲遗传算法。实验表明,该算法不仅能够保留收敛较快等优点,而且具备了比现有的单亲遗传算法以及改进的GT算法更强的全局寻优能力。     

A hybrid heuristic for the traveling salesman problem

The combination of genetic and local search heuristics has been shown to be an effective approach to solving the traveling salesman problem (TSP). This paper describes a new hybrid algorithm that exploits a compact genetic algorithm in order to generate high-quality tours, which are then refined by means of the Lin-Kernighan (LK) local search. The local optima found by the LK local search are in turn exploited by the evolutionary part of the algorithm in order to improve the quality of its simulated population. The results of several experiments conducted on different TSP instances with up to 13,509 cities show the efficacy of the symbiosis between the two heuristics     

Co-evolution-based immune clonal algorithm for clustering

Clustering is an important tool in data mining process. Fuzzy \(c\)-means is one of the most classic methods. But it has been criticized that it is sensitive to the initial cluster centers and is easy to fall into a local optimum. Not depending on the selection of the initial population, evolutionary algorithm is used to solve the problems existed in original fuzzy \(c\)-means algorithm. However, evolutionary algorithm emphasizes the competition in the population. But in the real world, the evolution of biological population is not only the result of internal competition, but also the result of mutual competition and cooperation among different populations. Co-evolutionary algorithm is an emerging branch of evolutionary algorithm. It focuses on the internal competition, while on the cooperation among populations. This is more close to the process of natural biological evolution and co-evolutionary algorithm is a more excellent bionic algorithm. An immune clustering algorithm based on co-evolution is proposed in this paper. First, the clonal selection method is used to achieve the competition within population to reconstruct each population. The internal evolution of each population is completed during this process. Second, co-evolution operation is conducted to realize the information exchange among populations. Finally, the iteration results are compared with the global best individuals, with a strategy called elitist preservation, to find out the individual with a highest fitness value, that is, the result of clustering. Compared with four state-of-art algorithms, the experimental results indicate that the proposed algorithm outperforms other algorithms on the test data in the highest accuracy and average accuracy.     

求解作业车间调度问题的改进混合灰狼优化算法*

灰狼优化算法(GWO)是目前一种比较新颖的群智能优化算法,具有收敛速度快,寻优能力强等优点。本文将灰狼优化算法用于求解复杂的作业车间调度问题,与布谷鸟搜索算法进行比较研究,验证了标准GWO算法求解经典作业车间调度问题的可行性和有效性。在此基础上,针对复杂作业车间调度问题难以求解的特点,对标准GWO算法进行改进,通过进化种群动态、反向学习初始化种群,以及最优个体变异等三个方面的改进操作,测试结果表明改进后的混合灰狼优化算法能够有效跳出局部最优值,找到更好的解,并且结果鲁棒性更强。     

一种新的免疫进化算法在函数优化中的应用

针对克隆选择算法在求解高维函数优化问题时易陷入局部最优以及收敛速度较慢的弱点,本文基于生物免疫系统内部学习优化机制以及进化算法,提出了一种新的免疫进化算法,它包括正交交叉、单形交叉、克隆、多极变异和选择。新算法将进化计算的思想融入到克隆选择中,提出了一种新的变异算子,在保证种群多样性的同时提高了算法的全全局寻优能力。理论分析证明了算法的收敛性,并将算法应用于不同的测试函数进行仿真实验。结果表明,该算法是有效的。     

Implementation of an Effective Hybrid GA for Large-Scale Traveling Salesman Problems

This correspondence describes a hybrid genetic algorithm (GA) to find high-quality solutions for the traveling salesman problem (TSP). The proposed method is based on a parallel implementation of a multipopulation steady-state GA involving local search heuristics. It uses a variant of the maximal preservative crossover and the double-bridge move mutation. An effective implementation of the Lin-Kernighan heuristic (LK) is incorporated into the method to compensate for the GA's lack of local search ability. The method is validated by comparing it with the LK-Helsgaun method (LKH), which is one of the most effective methods for the TSP. Experimental results with benchmarks having up to 316 228 cities show that the proposed method works more effectively and efficiently than LKH when solving large-scale problems. Finally, the method is used together with the implementation of the iterated LK to find a new best tour (as of June 2, 2003) for a 1 904 711-city TSP challenge     

A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling

This work presents a novel parallel micro evolutionary algorithm for scheduling tasks in distributed heterogeneous computing and grid environments. The scheduling problem in heterogeneous environments is NP-hard, so a significant effort has been made in order to develop an efficient method to provide good schedules in reduced execution times. The parallel micro evolutionary algorithm is implemented using MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental analysis performed on both well-known problem instances and large instances that model medium-sized grid environments. The comparative study of traditional methods and evolutionary algorithms shows that the parallel micro evolutionary algorithm achieves a high problem solving efficacy, outperforming previous results already reported in the related literature, and also showing a good scalability behavior when facing high dimension problem instances.     

A novel hybrid DE–random search approach for unit commitment problem

Differential evolution (DE) is a population-based stochastic search algorithm, whose simple yet powerful and straightforward features make it very attractive for numerical optimization. DE uses a rather greedy and less stochastic approach to problem-solving than other evolutionary algorithms. DE combines simple arithmetic operators with the classical operators of recombination, mutation and selection to evolve from a randomly generated starting population to a final solution. Although global exploration ability of DE algorithm is adequate, its local exploitation ability is feeble and convergence velocity is too low and it suffers from the problem of untime convergence for multimodal objective function, in which search process may be trapped in local optima and it loses its diversity. Also, it suffers from the stagnation problem, where the search process may infrequently stop proceeding toward the global optimum even though the population has not converged to a local optimum or any other point. To improve the exploitation ability and global performance of DE algorithm, a novel and hybrid version of DE algorithm is presented in the proposed research. This research paper presents a hybrid version of DE algorithm combined with random search for the solution of single-area unit commitment problem. The hybrid DE–random search algorithm is tested with IEEE benchmark systems consisting of 4, 10, 20 and 40 generating units. The effectiveness of proposed hybrid algorithm is compared with other well-known evolutionary, heuristics and meta-heuristics search algorithms, and by experimental analysis, it has been found that proposed algorithm yields global results for the solution of unit commitment problem.

     

独立自适应调整参数的粒子群优化算法

针对传统粒子群优化算法在求解复杂优化问题时易陷入局部最优和依赖参数的取值等问题,提出了一种独立自适应参数调整的粒子群优化算法。算法重新定义了粒子进化能力、种群进化能力以及进化率,在此基础上给出了粒子群惯性权重及学习因子的独立调整策略,更好地平衡了算法局部搜索与全局搜索的能力。为保持种群多样性,提高粒子向全局最优位置的收敛速度,在算法迭代过程中,采用粒子重构策略使种群中进化能力较弱的粒子向进化能力较强的粒子进行学习,重新构造生成新粒子。最后通过CEC2013中的10个基准测试函数与4种改进粒子群算法在不同维度下进行测试对比,实验结果验证了该算法在求解复杂函数时具有高效性,通过收敛性分析说明了算法的有效性。     

Parallel mesh-partitioning algorithms for generating shape optimised partitions using evolutionary computing

In this paper, parallel mesh-partitioning algorithms are proposed for generating submeshes with optimal shape using evolutionary computing techniques. It is preferred to employ a formulation for mesh partitioning, which maintains constant number of design variables irrespective of the size of the mesh. Two distinct parallel computing models have been employed. The first model of parallel evolutionary algorithm uses the master–slave concept (single population model) and a new synchronous model is proposed to optimise the performance even on heterogeneous parallel hardware. Alternatively, a multiple population model is also developed which simulates it’s sequential counter part. The advantage of the second model is that it can fit in large size problems with large population even on moderate capacity parallel computing nodes. The performance of the evolutionary computing based mesh-partitioning algorithm is demonstrated first by solving several practical engineering problems and also several benchmark test problems available in the literature and comparing the results with the multilevel algorithms. Later the speedup of the parallel evolutionary algorithms on parallel hardware is evaluated by solving large scale practical engineering problems.     

引入基因簇求解TSP的P2P遗传算法

应用遗传算法求解旅行商问题时,极易破坏已经发现的较短线路片段.为此,引入基因簇以便保护较短的线路片段,基于P2P设计了TSP求解遗传算法P2PTSPGA.在交叉和变异操作的过程中,基因簇完整地遗传到下一代;在获得第一个近优解后粉碎基因簇,以避免算法陷入局部最优.使用CHN144找到了当前最优路径,并使用TSPLIB进行了串行和并行试验.TSP225实验获得了最短环路路径3859,优于目前已经公布的结果3916.实验表明,P2PTSPGA具有较高的求解性能,并具备5000左右城市的持续寻优能力.     

嵌入单纯形法的混合类电磁机制算法

针对类电磁机制算法存在局部搜索能力差的问题,提出一种基于单纯形法的混合类电磁机制算法。该混合算法首先利用反向学习策略构造初始种群以保证粒子均匀分布在搜索空间中。利用单纯形法对最优粒子进行局部搜索,增强了算法在最优点附近的局部搜索能力,以加快算法的收敛速度。四个基准测试函数的仿真实验结果表明,该算法具有更好的寻优性能。     

基于FPGAs的智能机器人导航系统

现场可编程门阵列(FPGAs)是超大规模可编程专用集成电路,进化算法是能够在线自适应的硬件,它包括进化系统、遗传算法和遗传编程,算法从生物学上求解待定问题的计算方法得到灵感。给出一种基于FPGAs的新的进化算法,算法中的种群由联想种群和改进种群两个子种群组成且可动态地可重配置,对改进种群中的每个染色体都使用复制、变异和选择操作,不对联想种群而只对改进种群进行变异操作,算法成功地导航机器人在复杂变化的环境中实现避碰。     

动态分级的并行约束优化进化算法

提出一种动态分级的并行进化算法用于求解约束优化问题。该算法首先利用佳点集方法初始化种群。在进化过程中,将种群个体分为两个子种群,分别用于全局和局部搜索,并根据不同的搜索阶段动态调整各种级别中并行变量的数目。标准测试问题的实验结果表明了该算法的可行性和有效性。     

A novel improved particle swarm optimization algorithm based on individual difference evolution

As a well-known stochastic optimization algorithm, the particle swarm optimization (PSO) algorithm has attracted the attention of many researchers all over the world, which has resulted in many variants of the basic algorithm, in addition to a vast number of parameter selection/control strategies. However, most of these algorithms evolve their population using a single fixed pattern, thereby reducing the intelligence of the entire swarm. Some PSO-variants adopt a multimode evolutionary strategy, but lack dynamic adaptability. Furthermore, competition among particles is ignored, with no consideration of individual thinking or decision-making ability. This paper introduces an evolution mechanism based on individual difference, and proposes a novel improved PSO algorithm based on individual difference evolution (IDE-PSO). This algorithm allocates a competition coefficient called the emotional status to each particle for quantifying individual differences, separates the entire swarm into three subgroups, and selects the specific evolutionary method for each particle according to its emotional status and current fitness. The value of the coefficient is adjusted dynamically according to the evolutionary performance of each particle. A modified restarting strategy is employed to regenerate corresponding particles and enhance the diversity of the population. For a series of benchmark functions, simulation results show the effectiveness of the proposed IDE-PSO, which outperforms many state-of-the-art evolutionary algorithms in terms of convergence, robustness, and scalability.     

具有新型遗传特征的蚁群算法

蚁群算法是一种新型的模拟进化算法,具有很好的通用性和鲁棒性,在解决组合优化问题方面有良好效果,但存在如计算时间较长、容易陷入局部最优等问题。本文在蚁群算法的基础上,引入了杂交及变异机制,提出了一种具有新型变异特征的蚁群新算法,在减少计算时间的同时可避免早熟现象。     

Arc-Guided Evolutionary Algorithm for the Vehicle Routing Problem With Time Windows

This paper presents an arc-guided evolutionary algorithm for solving the vehicle routing problem with time windows, which is a well-known combinatorial optimization problem that addresses the service of a set of customers using a homogeneous fleet of capacitated vehicles within fixed time intervals. The objective is to minimize the fleet size following routes of minimum distance. The proposed method evolves a population of ${mu}$ individuals on the basis of an $({{mu}+{lambda}})$ evolution strategy; at each generation, a new intermediate population of ${lambda}$ individuals is generated, using a discrete arc-based representation combined with a binary vector of strategy parameters. Each offspring is produced via mutation out of arcs extracted from parent individuals. The selection of arcs is dictated by the strategy parameters and is based on their frequency of appearance and the diversity of the population. A multiparent recombination operator enables the self-adaptation of the strategy parameters, while each offspring is further improved via novel memory-based trajectory local search algorithms. For the selection of survivors, a deterministic scheme is followed. Experimental results on well-known large-scale benchmark datasets of the literature demonstrate the competitiveness of the proposed method.      

一种基于最大最小策略和非均匀变异的萤火虫算法

针对多目标萤火虫算法勘探能力弱、求解精度差的问题,本文提出了一种基于最大最小策略和非均匀变异的萤火虫算法(HVFA-M).该算法首先引入Maximin策略,实现对外部档案的动态调整和对精英解的随机选择;其次,精英解结合当前最好解共同引导萤火虫进行全局搜索以扩大算法的搜索范围,提高算法的勘探能力,从而增加找寻全局最优解的...     

基于变异交叉方程与进化选择机制的回溯优化改进算法

针对回溯搜索优化算法存在的收敛速度慢,容易陷入局部最优等问题,提出了一种改进算法。首先利用t分布产生变异尺度系数,加快了算法收敛速度;接着完善交叉方程结构,引入最优个体控制种群搜索方向,有效提高了算法开发能力;最后提出进化选择机制,引入差分进化算法变异因子,一定概率下以较差解替换较优解,避免算法陷入局部最优。在数值实验中,选取了15个测试函数进行仿真测试,并与5种表现良好的算法进行了比较,结果表明,该算法在收敛速度及搜索精度方面有明显优势。