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• W4225256819 abstract "Artificial intelligence techniques, such as particle swarm optimization, are used to solve problems throughout society. Optimization, in particular, seeks to identify the best possible decision within a search space. Problematically, particle swarm optimization will sometimes have particles that become trapped inside local minima, preventing them from identifying a global optimal solution. As a solution to this issue, this paper proposes a science-fiction inspired enhancement of particle swarm optimization where an impactful iteration is identified and the algorithm is rerun from this point, with a change made to the swarm. The proposed technique is tested using multiple variations on several different functions representing optimization problems and several standard test functions used to test various particle swarm optimization techniques." @default.
• W4225256819 created "2022-05-04" @default.
• W4225256819 creator A5054477317 @default.
• W4225256819 creator A5074333355 @default.
• W4225256819 date "2022-05-01" @default.
• W4225256819 modified "2023-09-25" @default.
• W4225256819 title "A Particle Swarm Optimization Backtracking Technique Inspired by Science-Fiction Time Travel" @default.
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• W4225256819 doi "https://doi.org/10.3390/ai3020024" @default.
• W4225256819 hasPublicationYear "2022" @default.
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