A Modified Emperor Penguin Algorithm for Solving Stagnation in Multi-Model Functions

Abstract

Metaheuristic algorithms have gained attention in recent years for their ability to solve complex problems that cannot be solved using classical mathematical techniques. This paper proposes an improvement to the Emperor Penguin Optimizer algorithm, a population-based metaheuristic. The original algorithm often gets stuck in local optima for multi-modal functions. To address this issue, this paper presents a modification in the relocating procedures that allows the algorithm to utilize information gained from the previous positions of each penguin. To demonstrate the effectiveness of the modified algorithm, 20 test optimization functions from well-known benchmarks were selected. The implemented comparative analysis assesses the proposed algorithm against both the traditional Emperor Penguin Optimizer algorithm and one of the most recent algorithm modifications in current research. The findings indicate that the proposed algorithm demonstrates significant efficiency, particularly in addressing multi-modal functions, as evidenced by superior mean results and robustness.