Self-adaptive Equilibrium Optimizer for solving global, combinatorial, engineering, and Multi-Objective problems

This 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.

Automated summary

This paper proposes a self-adaptive Equilibrium Optimizer (self-EO) to address global, combinatorial, engineering, and multi-objective optimization problems. By integrating four effective exploring phases, the new self-EO algorithm overcomes the potential shortcomings of the original EO and achieves better results compared to other nine metaheuristic algorithms.