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

Abstract

Sea horse optimizer (SHO) is a note worth y metaheuristic algorithm that emulates various intelligent behaviors exhibited by sea horses, encompassing feeding patterns, male r e pr oducti v e str ate gies, and intricate movement patterns. To mimic the nuanced loco- motion of sea horses, SHO inte gr ates the logarithmic helical equation and Levy flight, effecti v el y incorporating both random move- ments with substantial step sizes and refined local exploitation. Additionally, the utilization of Brownian motion facilitates a more compr ehensi v e exploration of the search space. This study introduces a robust and high-performance variant of the SHO algorithm named modified sea horse optimizer (mSHO). The enhancement primarily focuses on bolstering SHO's exploitation capabilities by r e placing its original method with an innov ati v e local sear c h str ate gy encompassing thr ee distinct ste ps: a neighborhood-based local sear c h, a global non-neighbor-based sear c h, and a method involving circumnavigation of the existing sear c h re gion. These tec hniques impr ov e mSHO algorithm's sear c h capabilities, allowing it to navigate the sear c h space and converge toward optimal solutions effi- cientl y. To ev aluate the efficacy of the mSHO algorithm, compr ehensi v e assessments ar e conducted acr oss both the CEC2020 bench- mark functions and nine distinct engineering pr ob lems. A meticulous comparison is drawn against nine metaheuristic algorithms to validate the achieved outcomes. Statistical tests, including Wilcoxon's rank-sum and Friedman's tests, ar e aptl y applied to discern note worth y differences among the compared algorithms. Empirical findings consistently underscore the exceptional performance of mSHO across diverse benchmark functions, reinforcing its prowess in solving complex optimization problems. Furthermore, the robustness of mSHO endures even as the dimensions of optimization challenges expand, signifying its unwavering efficacy in navi- gating complex sear c h spaces. The compr ehensi v e r esults distinctl y esta b lish the supr emac y and efficienc y of the mSHO method as an exemplary tool for tackling an array of optimization quandaries. The results show that the proposed mSHO algorithm has a total rank of 1 for CEC2020 test functions. In contrast, the mSHO achieved the best value for the engineering problems, recording a value of 0.012 665, 2993.634, 0.01 266, 1.724 967, 263.8915, 0.032 255, 58 507.14, 1.339 956, and 0.23 524 for the pr essur e v essel design, speed r educer design, tension/compr ession spring, welded beam design, three-bar truss engineering design, industrial refrigeration system, m ulti-pr oduct batch plant, cantilever beam problem, and multiple disc clutch brake pr ob lems, r especti v el y. Source codes of mSHO are pub licl y av aila b le at https://www.mathworks.com/matla bcentral/fileexchange/135882-impr ov ed-sea-horse-algorithm.