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 multimodal functions, as evidenced by superior mean results and robustness.Povzetek: Predstavljen je izboljšani algoritma "cesarskega pingvina", ki učinkovito rešuje kompleksne probleme zlasti v multimodalnih funkcijah.

A Modified Emperor Penguin Optimizer Algorithm for Solving Fixed-Charged Transshipment Problem

“…This section presents a modification of the EPO algorithm to adapt it for solving FCTP. The new modification of the algorithm considers adding an information vector (𝑃 𝐼𝑉 (𝑖)) [16]. Such information vector will be created during the relocating procedure of the algorithm.…”

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

“…The GitHub repository also includes the code of the 21 test optimization functions used in this comparative results. The comparative results are done to compare between the EOPWIV and the classical EPO [11], the weighted sum EPO (EPOW) that combines the classical EPO with the weighted sum procedure only, and the information vector EPO (EPOIV) that combines the classical EPO with the information vector process, where the EPOIV can be found in [21] and it is one of the algorithms listed in the literature summary of this paper. The boxplots show that the proposed EPOWIV algorithm robustly outperforms the other selected algorithms in this comparative results in terms of variability and median results.…”

“…Babu et al [20] developed EPO to optimize the location of AC transmission system devices in load frequency control. Serag et al [21] enhanced EPO by incorporating an information vector, enhancing its local search process compared to the standard version. This modification enables EPO to overcome stagnation present in certain multi-modal functions.…”

“…This modification enables EPO to overcome stagnation present in certain multi-modal functions. [11] Developed the first version of EPO Baliarsingh and Vipsita [12] Developed a chaotic EPO to optimize machine learning for classifying microarray cancer Cao et al [13] Used EPO to enhance the efficiency of power system Min et al [14] Developed a quantum EPO for optimizing the minimize the energy consumption of chiller loading Angel and Jaya [15] Solved load balancing and security enrichment in wireless sensor problem using EPO Xing [16] Enhanced a multi-threshold image segmentation using EPO Dhiman et al [17] Solved feature selection after adopting EPO to make it deal with discrete optimization problems Khan et al [18] Presented EPO to optimize the classification of recycling waste Cheena et al [19] proposed EPO algorithm to optimize self-heading for sensor network based smart grid system Babu et al [20] developed EPO to optimize the location of AC transmission system devices in load frequency control Serag et al [21] Developed a new modification for EPO that considers information vector to improve the local search procedure of the algorithm This paper is considered an extension for our previous work presented in [21] that solves the stagnation problem of the multi-modal functions. Therefore, we presented a new modification that utilizes weighted sum methodology along with generated information vector to update the relocating procedure of the algorithm, where the new modification, as shown in the comparative results, outperforms the algorithm proposed in [21].…”