Houssein, Essam H.Çelik, EmreMahdy, Mohamed A.Ghoniem, Rania M.2023-07-262023-07-2620220957-41741873-6793https://doi.org/10.1016/j.eswa.2022.116552https://hdl.handle.net/20.500.12684/12809This paper proposes a self-adaptive Equilibrium Optimizer (self-EO) to perform better global, combinatorial, engineering, and multi-objective optimization problems. The new self-EO algorithm integrates four effective exploring phases, which address the potential shortcomings of the original EO. We validate the performances of the proposed algorithm over a large spectrum of optimization problems, i.e., ten functions of the CEC'20 benchmark, three engineering optimization problems, two combinatorial optimization problems, and three multi-objective problems. We compare the self-EO results to those obtained with nine other metaheuristic algorithms (MAs), including the original EO. We employ different metrics to analyze the results thoroughly. The self-EO analyses suggest that the self-EO algorithm has a greater ability to locate the optimal region, a better trade-off between exploring and exploiting mechanisms, and a faster convergence rate to (near)-optimal solutions than other algorithms. Indeed, the self-EO algorithm reaches better results than the other algorithms for most of the tested functions.en10.1016/j.eswa.2022.116552info:eu-repo/semantics/closedAccessEquilibrium Optimizer; Enhanced Equilibrium Optimizer (Self-Eo); Multi-Objective Self-Eo (Mo-Self-Eo); Engineering Design Problems; Combinatorial Optimization Problems; Metaheuristic Algorithms (Mas)Colony Optimization; Algorithm; SearchArticle1952-s2.0-85124155587WOS:000761969600005Q1Q1