An efficient equilibrium optimizer with mutation strategy for numerical optimization

• Published in: Applied soft computing Vol. 96; p. 106542
• Main Authors: Gupta, Shubham, Deep, Kusum, Mirjalili, Seyedali
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2020
• Subjects: Artificial Intelligence; Benchmark; Equilibrium optimizer; Exploration and exploitation; Gaussian mutation; Machine Learning; Metaheuristic algorithms; Optimization; Particle Swarm Optimization; Exploration and exploitation; Equilibrium optimizer; Artificial Intelligence; Benchmark; Machine Learning; Particle Swarm Optimization; Gaussian mutation; Optimization; Metaheuristic algorithms
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.106542

To alleviate the shortcomings of the standard Equilibrium Optimizer, a new improved algorithm called Modified Equilibrium Optimizer is proposed in this work. This algorithm utilizes the Gaussian mutation and an additional exploratory search mechanism based on the concept of population division and reconstruction. The population in each iteration of the proposed algorithm is constructed using these mechanisms and standard search procedure of the Equilibrium Optimizer. These strategies attempt to maintain the diversity of solutions during the search, so that the tendency of stagnation towards the sub-optimal solutions can be avoided and the convergence rate can be boosted to obtain more accurate optimal solutions. To validate and analyze the performance of the Modified Equilibrium Optimizer, a collection of 33 benchmark problems and four engineering design problems are adopted. Later, in the paper, the Modified Equilibrium Optimizer has been used to train multilayer perceptrons. The experimental results and comparison based on several metrics such as statistical analysis, scalability test, diversity analysis, performance index analysis and convergence analysis demonstrate that the proposed algorithm can be considered a better metaheuristic optimization approach than other compared algorithms. [Display omitted] •A new method has been proposed, known as m-EO, for global optimization.•The m-EO is developed using population division, mutation and novel exploration strategy.•A set of 33 benchmark problems is used to examine the m-EO.•The m-EO is used to solve engineering design problems and to train multilayer perceptron.•Comparisons illustrate the improvement in the performance of the m-EO.