Equilibrium optimizer: A novel optimization algorithm

This paper presents a novel, optimization algorithm called Equilibrium Optimizer (EO), inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each particle (solution) with its concentration (position) acts as a search agent. The search agents rand...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 191; p. 105190
Main Authors: Faramarzi, Afshin, Heidarinejad, Mohammad, Stephens, Brent, Mirjalili, Seyedali
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 05.03.2020
Elsevier Science Ltd
Subjects:
Algorithms
Candidates
Equilibrium
Exploitation
Function words
Genetic algorithm
Genetic algorithms
Genetics
Heuristic
Heuristic methods
Mass balance
Mathematical functions
Metaheuristic
Novels
Optimization
Optimization algorithms
Particle Swarm Optimization
Physics-based
Portfolios
Search algorithms
Source code
Metaheuristic
Genetic algorithm
Optimization
Particle Swarm Optimization
Physics-based
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:This paper presents a novel, optimization algorithm called Equilibrium Optimizer (EO), inspired by control volume mass balance models used to estimate both dynamic and equilibrium states. In EO, each particle (solution) with its concentration (position) acts as a search agent. The search agents randomly update their concentration with respect to best-so-far solutions, namely equilibrium candidates, to finally reach to the equilibrium state (optimal result). A well-defined “generation rate” term is proved to invigorate EO’s ability in exploration, exploitation, and local minima avoidance. The proposed algorithm is benchmarked with 58 unimodal, multimodal, and composition functions and three engineering application problems. Results of EO are compared to three categories of existing optimization methods, including: (i) the most well-known meta-heuristics, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO); (ii) recently developed algorithms, including Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), and Salp Swarm Algorithm (SSA); and (iii) high performance optimizers, including CMA-ES, SHADE, and LSHADE-SPACMA. Using average rank of Friedman test, for all 58 mathematical functions EO is able to outperform PSO, GWO, GA, GSA, SSA, and CMA-ES by 60%, 69%, 94%, 96%, 77%, and 64%, respectively, while it is outperformed by SHADE and LSHADE-SPACMA by 24% and 27%, respectively. The Bonferroni–Dunnand Holm’s tests for all functions showed that EO is significantly a better algorithm than PSO, GWO, GA, GSA, SSA and CMA-ES while its performance is statistically similar to SHADE and LSHADE-SPACMA. The source code of EO is publicly availabe at https://github.com/afshinfaramarzi/Equilibrium-Optimizer, http://built-envi.com/portfolio/equilibrium-optimizer/ and http://www.alimirjalili.com/SourceCodes/EOcode.zip. •Developed a novel optimization algorithm inspired by mass balance models.•Tested EO against well-studied mathematical and engineering benchmarks.•Compared the algorithm to other well-known meta-heuristics.•Demonstrated effectiveness and superiority of the proposed method.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2019.105190