Adjustable mode ratio and focus boost search strategy for cat swarm optimization

Pei-wei Tsai, Xingsi Xue, Jing Zhang, V. Istanda
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Abstract

Evolutionary algorithm is one of the optimization techniques. Cat swarm optimization (CSO)-based algorithm is frequently used in many applications for solving challenging optimization problems. In this paper, the tracing mode in CSO is modified to reduce the number of user-defined parameters and weaken the sensitivity to the parameter values. In addition, a mode ratio control scheme for switching individuals between different movement modes and a search boosting strategy are proposed. The obtained results from our method are compared with the modified CSO without the proposed strategy, the original CSO, the particle swarm optimization (PSO) and differential evolution (DE) with three commonly-used DE search schemes. Six test functions from IEEE congress on evolutionary competition (CEC) are used to evaluate the proposed methods. The overall performance is evaluated by the average ranking over all test results. The ranking result indicates that our proposed method outperforms the other methods compared.
猫群优化的可调模式比和聚焦增强搜索策略
进化算法是一种优化技术。基于Cat群优化(CSO)的算法在许多应用中经常用于解决具有挑战性的优化问题。本文对CSO中的跟踪模式进行了改进,减少了自定义参数的数量,减弱了对参数值的敏感性。此外,提出了一种用于个体在不同运动模式之间切换的模式比控制方案和搜索增强策略。将该方法得到的结果与不采用该策略的改进CSO、原始CSO、粒子群优化(PSO)和差分进化(DE)三种常用DE搜索方案进行了比较。采用IEEE进化竞争大会(CEC)的六个测试函数对所提出的方法进行了评价。总体性能通过对所有测试结果的平均排名来评估。排序结果表明,本文提出的方法优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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