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首页> 外文期刊>International journal of geotechnical earthquake engineering >Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment: Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA
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Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA Traveling Salesman Problems Based Assessment: Performance Evaluation of Population Seeding Techniques of Permutation-Coded GA

机译：基于置换编码GA旅行商问题的种群播种技术性能评估：基于置换编码GA的种群播种技术性能评估

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摘要

Genetic algorithms (GAs) are a population-based meta-heuristic global optimization technique for dealing with complex problems with a very large search space. The population initialization is a crucial task in GAs because it plays a vital role in the convergence speed, problem search space exploration, and also the quality of the final optimal solution. Though the importance of deciding problem-specific population initialization in GA is widely recognized, it is hardly addressed in the literature. In this article, different population seeding techniques for permutation-coded genetic algorithms such as random, nearest neighbor (NN), gene bank (GB), sorted population (SP), and selective initialization (SI), along with three newly proposed ordered-distance-vector-based initialization techniques have been extensively studied. The ability of each population seeding technique has been examined in terms of a set of performance criteria, such as computation time, convergence rate, error rate, average convergence, convergence diversity, nearest-neighbor ratio, average distinct solutions and distribution of individuals. One of the famous combinatorial hard problems of the traveling salesman problem (TSP) is being chosen as the testbed and the experiments are performed on large-sized benchmark TSP instances obtained from standard TSPLIB. The scope of the experiments in this article is limited to the initialization phase of the GA and this restricted scope helps to assess the performance of the population seeding techniques in their intended phase alone. The experimentation analyses are carried out using statistical tools to claim the unique performance characteristic of each population seeding techniques and best performing techniques are identified based on the assessment criteria defined and the nature of the application.
机译：遗传算法（GA）是一种基于种群的元启发式全局优化技术，用于处理具有很大搜索空间的复杂问题。种群初始化是遗传算法中的关键任务，因为它在收敛速度，问题搜索空间探索以及最终最优解的质量方面起着至关重要的作用。尽管在GA中确定特定于问题的总体初始化的重要性已广为人知，但文献中几乎没有解决这一问题。在本文中，针对置换编码遗传算法的不同种群播种技术，例如随机，最近邻居（NN），基因库（GB），排序种群（SP）和选择性初始化（SI），以及三个新提出的有序-基于距离矢量的初始化技术已被广泛研究。每种种群播种技术的能力已根据一组性能标准进行了检验，例如计算时间，收敛速度，错误率，平均收敛速度，收敛多样性，最近邻比率，平均不同解和个体分布。选择了著名的旅行推销员问题（TSP）组合难题之一作为测试平台，并在从标准TSPLIB获得的大型基准TSP实例上进行了实验。本文中的实验范围仅限于GA的初始化阶段，而此受限范围仅有助于评估种群播种技术在其预期阶段的性能。使用统计工具进行实验分析，以声明每种种群播种技术的独特性能特征，并根据定义的评估标准和应用程序的性质确定最佳性能的技术。

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