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- Title
Focus and Shake Algorithm: A New Stochastic Optimization Employing Strict and Randomized Dimension Mappings.
- Authors
Kusuma, Purba Daru
- Abstract
The no-free-lunch (NFL) theory has become the main reason of developing new metaheuristics in decades. Besides, the strict dimension mapping has been implemented in many population-based metaheuristics, especially the swarm-based metaheuristics. Regarding this problem, this paper proposes a new swarm-based metaheuristics called focus and shake algorithm (FSA). FSA has novel approach in the dimension mapping between the agent and its reference during the directed motion. It combines the strict dimension mapping called as focus approach and the randomized dimension mapping called as shake approach to enhance its exploration ability. FSA deploys two directed motions based on two references. The first reference is constructed based on the balance mixture between two finer agents while the second reference is constructed based on the balance mixture of the finest agent and a randomly picked agent. In the competing assessment, FSA competes with five brand new swarm-based metaheuristics: migration algorithm (MA), total interaction algorithm (TIA), lyrebird optimization algorithm (LOA), osprey optimization algorithm (OOA), and kookaburra optimization algorithm (KOA). The result exhibits that FSA is finer than MA, TIA, LOA, OOA, and KOA in 19, 21, 21, 19, and 20 functions out of 23 functions respectively. The result also shows that the superiority of FSA takes place in both unimodal and multimodal problems. In the future, the cross-dimension mapping can be more explored to develop finer swarm-based metaheuristics.
- Subjects
METAHEURISTIC algorithms; OPTIMIZATION algorithms; ALGORITHMS; GENETIC techniques; SWARM intelligence; GENE mapping
- Publication
International Journal of Intelligent Engineering & Systems, 2024, Vol 17, Issue 3, p551
- ISSN
2185-310X
- Publication type
Article
- DOI
10.22266/ijies2024.0630.43