An Improved Soft Island Model of the Fish School Search Algorithm with Exponential Step Decay Using Cluster-Based Population Initialization

Optimization is a highly relevant area of research due to its widespread applications. The development of new optimization algorithms or the improvement of existing ones enhances the efficiency of various fields of activity. In this paper, an improved Soft Island Model (SIM) is considered for the Tent-map-based Fish School Search algorithm with Exponential step decay (ETFSS). The proposed model is based on a probabilistic approach to realize the migration process relying on the statistics of the overall achievement of each island. In order to generate the initial population of the algorithm, a new initialization method is proposed in which all islands are formed in separate regions of the search space, thus forming clusters. For the presented SIM-ETFSS algorithm, numerical experiments with the optimization of classical test functions, as well as checks for the presence of some known defects that lead to undesirable effects in problem solving, have been carried out. Tools, such as the Mann–Whitney U test, box plots and other statistical methods of data analysis, are used to evaluate the quality of the presented algorithm, using which the superiority of SIM-ETFSS over its original version is demonstrated. The results obtained are analyzed and discussed.