A new approach for solving global optimization and engineering problems based on modified sea horse optimizer

• DOI: 10.1093/jcde/qwae001
• Publication Date: 2024-01-01T21:13:41Z
• Authors: Fatma A Hashim; Reham R Mostafa; Ruba Abu Khurma; Raneem Qaddoura; Pedro A Castillo
• Keywords: Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; Computer Science - Neural and Evolutionary Computing; Global optimization; Metaheuristics; 02 engineering and technology; Engineering problem

Abstract Sea horse optimizer (SHO) is a noteworthy metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male reproductive strategies, and intricate movement patterns. To mimic the nuanced locomotion of SHO integrates logarithmic helical equation Levy flight, effectively incorporating both random movements with substantial step sizes refined local exploitation. Additionally, utilization Brownian motion facilitates more comprehensive exploration search space. This study introduces robust high-performance variant named modified (mSHO). The enhancement primarily focuses on bolstering SHO’s exploitation capabilities replacing its original method an innovative strategy three distinct steps: neighborhood-based search, global non-neighbor-based involving circumnavigation existing region. These techniques improve mSHO algorithm’s capabilities, allowing it to navigate space converge toward optimal solutions efficiently. evaluate efficacy algorithm, assessments are conducted across CEC2020 benchmark functions nine engineering problems. A meticulous comparison drawn against algorithms validate achieved outcomes. Statistical tests, including Wilcoxon’s rank-sum Friedman’s aptly applied discern differences among compared algorithms. Empirical findings consistently underscore exceptional performance diverse functions, reinforcing prowess in solving complex optimization Furthermore, robustness endures even as dimensions challenges expand, signifying unwavering navigating spaces. results distinctly establish supremacy efficiency exemplary tool for tackling array quandaries. show proposed has total rank 1 test functions. In contrast, best value problems, recording 0.012 665, 2993.634, 0.01 266, 1.724 967, 263.8915, 0.032 255, 58 507.14, 1.339 956, 0.23 524 pressure vessel design, speed reducer tension/compression spring, welded beam three-bar truss industrial refrigeration system, multi-product batch plant, cantilever problem, multiple disc clutch brake respectively. Source codes publicly available at https://www.mathworks.com/matlabcentral/fileexchange/135882-improved-sea-horse-algorithm.