Golden Search Optimization Algorithm

This study introduces an effective population-based optimization algorithm, namely the Golden Search Optimization Algorithm (GSO), for numerical function optimization. The new algorithm has a simple but effective strategy for solving complex problems. GSO starts with random possible solutions called...

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Veröffentlicht in:IEEE access Jg. 10; S. 37515 - 37532
Hauptverfasser: Noroozi, Mohammad, Mohammadi, Hamed, Efatinasab, Emad, Lashgari, Ali, Eslami, Mahdiyeh, Khan, Baseem
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
benchmark function
Comparative studies
Genetic algorithms
Global optimization
golden search
Heuristic algorithms
metaheuristic
Metaheuristics
Operators (mathematics)
Optimization
Optimization algorithms
Particle swarm optimization
population-based
Search algorithms
Search problems
Statistics
Trigonometric functions
ISSN:2169-3536, 2169-3536
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Zusammenfassung:This study introduces an effective population-based optimization algorithm, namely the Golden Search Optimization Algorithm (GSO), for numerical function optimization. The new algorithm has a simple but effective strategy for solving complex problems. GSO starts with random possible solutions called objects, which interact with each other based on a simple mathematical model to reach the global optimum. To provide a fine balance between the explorative and exploitative behavior of a search, the proposed method utilizes a transfer operator in the adaptive step size adjustment scheme. The proposed algorithm is benchmarked with 23 unimodal, multimodal, and fixed dimensional functions and the results are verified by a comparative study with the well-known Gravitational Search Algorithm (GSA), Sine-Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Tunicate Swarm Algorithm (TSA). In addition, the nonparametric Wilcoxon's rank sum test is performed to measure the pair-wise statistical performance of the GSO and provide a valid judgment about the performance of the algorithm. The simulation results demonstrate that GSO is superior and could generate better optimal solutions when compared with other competitive algorithms.
Bibliographie:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3162853