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Quasi-Affine Transformation Evolutionary with Double Excellent Guidance
Wireless Communications and Mobile Computing Pub Date : 2021-04-19 , DOI: 10.1155/2021/5591543
Tien-Wen Sung 1 , Baohua Zhao 1 , Xin Zhang 1
Affiliation  

The Quasi-Affine Transformation Evolutionary (QUATRE) algorithm is a swarm-based collaborative optimization algorithm, which has drawn attention from researchers due to its simple structure, easy implementation, and powerful performance. However, it needs to be improved regarding the exploration, especially in the late stage of evolution, and the problem of easy falling into a local optimal solution. This paper proposes an improved algorithm named Quasi-Affine Transformation Evolutionary with double excellent guidance (QUATRE-DEG). The algorithm uses not only the global optimal solution but also the global suboptimal solution to guide the individual evolution. We establish a model to determine the guiding force by the distance between the global optimal position and the suboptimal position and propose a new mutation strategy through the double population structure. The optimization of population structure and the improvement of operation mechanisms bring more exploration for the algorithm. To optimize the algorithm, the experiments on parameter settings were made to determine the size of the subpopulation and to achieve a balance between exploration and development. The performance of QUATRE-DEG algorithm is evaluated under CEC2013 and CEC2014 test suites. Through comparison and analysis with some ABC variants known for their strong exploration ability and advanced QUATRE variants, the competitiveness of the proposed QUATRE-DEG algorithm is validated.

中文翻译:

具有双重优良导引的拟仿射变换进化

拟仿射变换进化算法(QUATRE)是一种基于群体的协同优化算法,由于其结构简单,易于实现且性能强大而引起了研究人员的关注。但是,在勘探方面,尤其是在演化的后期,以及容易陷入局部最优解的问题,都需要加以改进。本文提出了一种具有双重优良指导的改进的拟仿射变换进化算法(QUATRE-DEG)。该算法不仅使用全局最优解,而且使用全局次优解来指导个体进化。我们建立了一个模型,通过全局最优位置和次优位置之间的距离来确定引导力,并通过双种群结构提出了一种新的变异策略。人口结构的优化和运行机制的完善为算法带来了更多的探索。为了优化算法,进行了参数设置实验以确定子种群的大小,并在探索与开发之间取得平衡。QUATRE-DEG算法的性能在CEC2013和CEC2014测试套件下进行了评估。通过与一些以其强大的勘探能力和先进的QUATRE变种而闻名的ABC变种进行比较和分析,验证了所提出的QUATRE-DEG算法的竞争力。人口结构的优化和运行机制的完善为算法带来了更多的探索。为了优化算法,进行了参数设置实验以确定子种群的大小,并在探索与开发之间取得平衡。QUATRE-DEG算法的性能在CEC2013和CEC2014测试套件下进行了评估。通过与一些以其强大的勘探能力和先进的QUATRE变种而闻名的ABC变种进行比较和分析,验证了所提出的QUATRE-DEG算法的竞争力。人口结构的优化和运行机制的完善为算法带来了更多的探索。为了优化算法,进行了参数设置实验以确定子种群的大小,并在探索与开发之间取得平衡。QUATRE-DEG算法的性能在CEC2013和CEC2014测试套件下进行了评估。通过与一些以其强大的勘探能力和先进的QUATRE变种而闻名的ABC变种进行比较和分析,验证了所提出的QUATRE-DEG算法的竞争力。进行了参数设置实验,以确定亚群的大小并在开发与开发之间取得平衡。QUATRE-DEG算法的性能在CEC2013和CEC2014测试套件下进行了评估。通过与一些以其强大的勘探能力和先进的QUATRE变种而闻名的ABC变种进行比较和分析,验证了所提出的QUATRE-DEG算法的竞争力。进行了参数设置实验,以确定亚群的大小并在开发与开发之间取得平衡。QUATRE-DEG算法的性能在CEC2013和CEC2014测试套件下进行了评估。通过与一些以其强大的勘探能力和先进的QUATRE变种而闻名的ABC变种进行比较和分析,验证了所提出的QUATRE-DEG算法的竞争力。
更新日期:2021-04-19
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