Improving the Emperor Penguin Optimizer Algorithm through Adapted Weighted Sum Mutation Strategy with Information Vector

Abstract

The Emperor Penguin Optimizer algorithm (EPO) is a recent addition to population-based metaheuristics. However, it has been observed that the algorithm occasionally gets trapped in local optima, particularly when dealing with multi-modal functions.  In this paper, we present a novel modification of the Emperor Penguin Optimizer algorithm, termed the Emperor Penguin Optimizer with Weighted Sum Procedure and Information Vector (EPOWIV). The EPOWIV algorithm combines two techniques, the weighted sum procedure and the information vector. To evaluate the effectiveness of the proposed EPOWIV algorithm, a comprehensive comparative study is conducted. This study includes a comparison with the classical EPO algorithm, the EPO algorithm with the weighted sum procedure only, and the EPO algorithm with the information vector. The comparison is carried out on 21 test optimization problems. The comparative results show superiority of the EPOWIV algorithm over its counterparts. The EPOWIV algorithm consistently exhibits superior optimization performance, effectively overcoming the stagnation issues previously associated with the EPO algorithm. It consistently delivers outstanding solutions across a diverse set of test problems.