The non-monopolize search (NO): a novel single-based local search optimization algorithm

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought...

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Veröffentlicht in:Neural computing & applications Jg. 36; H. 10; S. 5305 - 5332
Hauptverfasser: Abualigah, Laith, Al-qaness, Mohammed A. A., Abd Elaziz, Mohamed, Ewees, Ahmed A., Oliva, Diego, Cuong-Le, Thanh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.04.2024
Springer Nature B.V
Schlagworte:
Algorithms
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Exploitation
Image Processing and Computer Vision
Operators
Optimization
Optimization algorithms
Optimization techniques
Original Article
Probability and Statistics in Computer Science
Search methods
Optimization problems
Non-monopolize search (NO)
Local search optimization
Metaheuristic algorithms
ISSN:0941-0643, 1433-3058
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Zusammenfassung:Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms’ performance and help avoid local search problems. Source codes of NO are publicly available at  https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no .
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-09120-9