Mantis Search Algorithm: A novel bio-inspired algorithm for global optimization and engineering design problems

This study presents a new nature-inspired optimization algorithm, namely the Mantis Search Algorithm (MSA), inspired by the unique hunting behavior and sexual cannibalism of praying mantises. In brief, MSA consists of three optimization stages, including the search for prey (exploration), attack pre...

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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 415; p. 116200
Main Authors: Abdel-Basset, Mohamed, Mohamed, Reda, Zidan, Mahinda, Jameel, Mohammed, Abouhawwash, Mohamed
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2023
Subjects:
Constrained optimization
Global optimization
Mantis search algorithm
Swarm algorithms
Unconstrained optimization
Global optimization
Mantis search algorithm
Unconstrained optimization
Swarm algorithms
Constrained optimization
ISSN:0045-7825
Online Access:Get full text
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Summary:This study presents a new nature-inspired optimization algorithm, namely the Mantis Search Algorithm (MSA), inspired by the unique hunting behavior and sexual cannibalism of praying mantises. In brief, MSA consists of three optimization stages, including the search for prey (exploration), attack prey (exploitation), and sexual cannibalism. Those operators are simulated using various mathematical models to effectively tackle optimization challenges across diverse search spaces. The performance of MSA is rigorously tested on fifty-two optimization problems and three real-world applications (five engineering design problems, and the parameter estimation problem of photovoltaic modules and fuel cells) to show its versatility and adaptability to different scenarios. To disclose the MSA’s superiority, it is compared to two categories from the rival optimizers: the first category involves well-established and highly-cited optimizers, like Differential evolution; and the second category contains recently-published algorithms, like African Vultures Optimization Algorithm. This comparison is conducted using several performance metrics, the Wilcoxon rank-sum test and the Friedman mean rank to disclose the MSA’s effectiveness and efficiency. The results of this comparison highlight the effectiveness of this new approach and its potential for future optimization applications. The source codes of the MSA algorithm are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/131833-mantis-search-algorithm-msa. •A nature-inspired algorithm named mantis search algorithm (MSA) was proposed.•MSA simulates the hunting behavior and sexual cannibalism of praying mantises.•MSA was assessed using 52 test problems and 6 engineering design problems.•MSA is compared with nine published over different benchmarks problems.
ISSN:0045-7825
DOI:10.1016/j.cma.2023.116200