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Determinative Brain Storm Optimization

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Autor
Sovatzidi G., Iakovidis D.K.
Datum
2020
Language
en
DOI
10.1007/978-3-030-53956-6_24
Schlagwort
Merging
Storms
Swarm intelligence
Benchmark functions
Cluster-merging
Faster convergence
Optimal solutions
Selection operators
Similar solution
State-of-the-art algorithms
Swarm intelligence optimization algorithm
Iterative methods
Springer
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Zusammenfassung
Brain Storm Optimization (BSO) is a swarm intelligence optimization algorithm, based on the human brainstorming process. The ideas of a brainstorming process comprise the solutions of the algorithm, which iteratively applies solution grouping, generation and selection operators. Several modifications of BSO have been proposed to enhance its performance. In this paper, we propose a novel modification enabling faster convergence of BSO to optimal solutions, without requiring setting an upper bound of algorithm iterations. It considers a brainstorming scenario where participating groups with similar ideas recognize that their ideas are similar, and together, collaborate for the determination of a better solution. The proposed modification, called Determinative BSO (DBSO), implements this scenario by applying a cluster merging strategy for merging groups of similar solutions, while following elitist selection. Experimental results using eleven benchmark functions show that the proposed modified BSO performs better than both the original and a state-of-the-art algorithm. © 2020, Springer Nature Switzerland AG.
URI
http://hdl.handle.net/11615/79232
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