Atomic orbital search: A novel metaheuristic algorithm

In this paper, the Atomic Orbital Search (AOS) is proposed as a novel metaheuristic algorithm for optimization purposes. The main concept of this algorithm is based on some principles of quantum mechanics and the quantum-based atomic model in which the general configuration of electrons around nucle...

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Veröffentlicht in:Applied Mathematical Modelling Jg. 93; S. 657
1. Verfasser: Azizi, Mahdi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier BV 01.05.2021
Schlagworte:
Algorithms
Constraints
Design engineering
Evolutionary algorithms
Evolutionary computation
Heuristic methods
Optimization
Performance evaluation
Quantum mechanics
Statistical analysis
ISSN:1088-8691, 0307-904X
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Zusammenfassung:In this paper, the Atomic Orbital Search (AOS) is proposed as a novel metaheuristic algorithm for optimization purposes. The main concept of this algorithm is based on some principles of quantum mechanics and the quantum-based atomic model in which the general configuration of electrons around nucleus is in perspective. In order to evaluate the performance of this algorithm, a total number of 20 unconstrained mathematical test functions are utilized with different dimensions of 2–100 while a maximum number of 150,000 function evaluations is considered with 100 independent optimization runs for statistical purposes. A complete statistical analysis is also conducted by utilization of the Kolmogorov Smirnov, Wilcoxon and the Kruskal Wallis tests while 8 metaheuristics are also utilized as alternatives for comparative purposes. The latest Competitions on Evolutionary Computation (CEC) regarding the single objective real-parameter numerical optimization (CEC 2017) including 30 benchmark test functions is also considered in which the capability of the proposed algorithm is compared to the most state−of-the−art algorithms in the optimization field. In addition, a total number of 5 constrained engineering design problems are utilized as design examples including some of the constrained optimization problems of the recent Competitions on Evolutionary Computation (CEC 2020). The obtained results of the AOS algorithm in dealing with the constraint problems are compared to the results of different standard, improved and hybrid metaheuristic algorithms from the literature. The obtained results demonstrate that the proposed AOS algorithm provides very outstanding results in dealing with the mathematical and engineering design problems.
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ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2020.12.021