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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/23258
Title: An efficient equilibrium optimizer with mutation strategy for numerical optimization
Authors: Gupta S.
Deep, K.
Mirjalili S.
Published in: Applied Soft Computing Journal
Abstract: 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. © 2020 Elsevier B.V.
Citation: Applied Soft Computing Journal, 96
URI: https://doi.org/10.1016/j.asoc.2020.106542
http://repository.iitr.ac.in/handle/123456789/23258
Issue Date: 2020
Publisher: Elsevier Ltd
Keywords: Artificial Intelligence
Benchmark
Equilibrium optimizer
Exploration and exploitation
Gaussian mutation
Machine Learning
Metaheuristic algorithms
Optimization
Particle Swarm Optimization
ISSN: 15684946
Author Scopus IDs: 57209786185
8561208900
51461922300
Author Affiliations: Gupta, S., Department of Mathematics, Indian Institute of Technology Roorkee, RoorkeeUttarakhand 247667, India
Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, RoorkeeUttarakhand 247667, India
Mirjalili, S., Center for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, 4006 QLD, Australia
Corresponding Author: Gupta, S.; Department of Mathematics, Roorkee, India; email: g.shubh93@gmail.com
Appears in Collections:Journal Publications [MA]

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