TY - JOUR
T1 - Improved multi-core arithmetic optimization algorithm-based ensemble mutation for multidisciplinary applications
AU - Abualigah, Laith
AU - Diabat, Ali
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022
Y1 - 2022
N2 - This paper proposes a new search method based on an augmented version of the Arithmetic Optimization Algorithm to solve various benchmark functions, engineering design cases, and feature selection problems. The proposed method is called MCAOA, combined with the Marine Predators Algorithm and a new proposed Ensemble Mutation Strategy. The Arithmetic Optimization Algorithm is a new meta-heuristic technique used to solve optimization problems. Sometimes, Arithmetic Optimization Algorithm faces convergence problems and falls into local optima for specific optimization problems, especially large-scale and multimodal problems. The Marine Predators Algorithm and Ensemble Mutation Strategy improve the Arithmetic Optimization Algorithm’s convergence rate and equilibrium in the exploration and exploitation search methods. The proposed method is tested on 23 different benchmark functions, seven common engineering design cases, and sixteen feature selection problems. The obtained results are compared with other well-known and state-of-the-art methods. The experimental results indicated that the proposed method found new best solutions for different complicated problems; the general performance is promising compared to other comparative methods.
AB - This paper proposes a new search method based on an augmented version of the Arithmetic Optimization Algorithm to solve various benchmark functions, engineering design cases, and feature selection problems. The proposed method is called MCAOA, combined with the Marine Predators Algorithm and a new proposed Ensemble Mutation Strategy. The Arithmetic Optimization Algorithm is a new meta-heuristic technique used to solve optimization problems. Sometimes, Arithmetic Optimization Algorithm faces convergence problems and falls into local optima for specific optimization problems, especially large-scale and multimodal problems. The Marine Predators Algorithm and Ensemble Mutation Strategy improve the Arithmetic Optimization Algorithm’s convergence rate and equilibrium in the exploration and exploitation search methods. The proposed method is tested on 23 different benchmark functions, seven common engineering design cases, and sixteen feature selection problems. The obtained results are compared with other well-known and state-of-the-art methods. The experimental results indicated that the proposed method found new best solutions for different complicated problems; the general performance is promising compared to other comparative methods.
KW - Arithmetic optimization algorithm (AOA)
KW - Engineering design problems
KW - Ensemble mutation
KW - Feature selection
KW - Global optimization
KW - Marine predators algorithm
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U2 - 10.1007/s10845-021-01877-x
DO - 10.1007/s10845-021-01877-x
M3 - Article
AN - SCOPUS:85123072994
JO - Journal of Intelligent Manufacturing
JF - Journal of Intelligent Manufacturing
SN - 0956-5515
ER -