Disturbance inspired equilibrium optimizer with application to constrained engineering design problems

This work proposes a novel adaptive global optimization algorithm called Disturbance Inspired Equilibrium Optimizer. The purpose of this study is to enhance the exploitation ability of the newly developed Equilibrium Optimizer, and to address the issue of getting trapped in local minima. The proposed algorithm is benefited from the novel disturbance-based hybrid initialization strategy, the new form of time factor, and the new update rule of particle's position. In addition, a novel boundary check strategy and an adaptive global position disturbance mechanism are proposed and installed into our algorithm. Based on the disturbance-inspired modifications, the exploration and exploitation ability of the standard Equilibrium Optimizer are significantly improved. The performance of the proposed algorithm is evaluated using representative different benchmark functions, consisting of three well-known mathematical benchmark functions, six complex composite functions, and four challenge functions proposed on 2017 IEEE Congress on Evolutionary Computation. Also, the proposed algorithm is conducted to optimize three engineering designs to examine its applicability in constrained real-world problems. In all experiments, the developed algorithm is compared with six other state-of-the-art metaheuristics. Experimental results and the average rank of Friedman test show that our algorithm provides promising results in solving mathematical problems and constrained real-world engineering optimization problems. Therefore, the proposed algorithm is competitive compared to the other state-of-the-art metaheuristic algorithms and is an effective solution to real-world engineering problems.