Chaos based optics inspired optimization algorithms as global solution search approach

•The first work on performance improvement of OIO method is performed.•Different ergodic chaotic systems are used for the first time to generate chaotic values instead of random values in OIO processes in order to enhance performance.•Three new enhanced OIO methods are proposed.•A new application ar...

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Veröffentlicht in:Chaos, solitons and fractals Jg. 141; S. 110434
Hauptverfasser: Bingol, Harun, Alatas, Bilal
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.12.2020
Schlagworte:
Chaotic maps
Metaheuristic algorithms
Optics inspired optimization
Optics inspired optimization
Metaheuristic algorithms
Chaotic maps
ISSN:0960-0779, 1873-2887
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Zusammenfassung:•The first work on performance improvement of OIO method is performed.•Different ergodic chaotic systems are used for the first time to generate chaotic values instead of random values in OIO processes in order to enhance performance.•Three new enhanced OIO methods are proposed.•A new application area for chaos is proposed. Metaheuristic optimization algorithms are efficiently used in many large-scale complex problems. Recently, a physics-based metaheuristic search and optimization method entitled Optics Inspired Optimization (OIO) has been proposed. OIO treats the search field of the interested problem to be optimized as a wavy mirror in which the concave mirror is represented as a valley and the convex mirror is represented as a peak. Each candidate solution represents an artificial light point. OIO is a very new metaheuristic method and different approaches should be integrated to obtain a faster convergence with high accuracy by balancing the exploitation and exploration. This paper is the first work on performance improvement of this method by preventing the falling into local optimum solutions and slow convergence speed. In this article, different ergodic chaotic systems are used for the first time to generate chaotic values instead of random values in OIO processes in order to enhance the global convergence speed and prevent stuck on local solutions of classical OIO algorithm. For this purpose, three new enhanced OIO methods are proposed. Furthermore, a new application area for chaos is proposed. The chaotic OIO algorithms proposed in this study are tested in unconstrained benchmark problems and constrained real-world engineering problems. Promising results are obtained from the detailed simulations.
ISSN:0960-0779
1873-2887
DOI:10.1016/j.chaos.2020.110434