ArticlePloS one2022
An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction.PeerJ. Computer science · 2024Article
- Pair barracuda swarm optimization algorithm: a natural-inspired metaheuristic method for high dimensional optimization problems.Scientific reports · 2023Article
- A novel hermit crab optimization algorithm.Scientific reports · 2023Article
- A deep memory bare-bones particle swarm optimization algorithm for single-objective optimization problems.PloS one · 2023Article
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Authors and funding
5 authors.
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Abstract
An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.
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