A random dynamic search algorithm research

Article type: Research Article

Authors: Li, Jun

Affiliations: School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, Hunan 410205, China | E-mail: 5622725@qq.com

Correspondence: [*] Corresponding author: School of Information and Mechanical Engineering, Hunan International Economics University, Changsha, Hunan 410205, China. E-mail: 5622725@qq.com.

Abstract: The core fireworks dynamic explosion radius strategy is used in dynamic search fireworks algorithm (dynFWA), it is an important algorithm to solve the optimization problem. DynFWA accuracy is low and is too early to fall into local optimal solutions. In order to improve the defects, the traditional dynamic search fireworks algorithm is improved by embedding two different learning factors, it is called as the improved dynFWA (IdynFWA). The learning factor of the algorithm makes full use of the individual fireworks information of each generation in the search process, the fireworks can search for information to the group’s excellent search information, its two different generating ways are helpful to balance the local search and global search ability. The improved algorithm is tested on 28 Benchmark functions of CEC2013. The experimental results show that IdynFWA is superior to dynFWA, it is better than particle swarm algorithm SPSO2011 and differential evolution algorithm DE optimize performance.

Keywords: Dynamic search fireworks algorithm, explosion amplitude, mutation operator, learning factor, optimal solution

DOI: 10.3233/JCM-193522

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 19, no. 3, pp. 659-672, 2019