Performance Analysis of Whale Optimization Algorithm Based on Strategy Parameter

Abstract

The performance of heuristic algorithms highly depends on the parameter values of the algorithms. Whale optimization algorithm (WOA) is a newly developed heuristic algorithm which has strategy parameter a that decreases linearly from 2 to 0 as iteration increases. In this paper, two algorithms, modified whale optimization algorithm-1 (MWOA-1) and modified whale optimization algorithm-2 (MWOA-2), have been proposed based on the variation of the strategy parameter a. The experiments are performed on a set of 23 benchmark problems. Results are compared with original WOA, gravitational search algorithm and grasshopper optimization algorithm. Based on the analysis of results, it is concluded that the overall performance of MWOA-1 and MWOA-2 is better than others on scalable unimodal function with dim = 30, scalable multimodal functions with dim = 30 and low-dimensional multimodal functions. © 2020, Springer Nature Singapore Pte Ltd.