Abstract

In this paper, a novel dynamic multi-swarm particle swarm optimizer (PSO) is introduced. Different from the existing multi-swarm PSOs and the local version of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the swarms. Experiments are conducted on a set of shifted rotated benchmark functions and results show its better performance when compared with some recent PSO variants.

Keywords:
Swarm behaviour Particle swarm optimization Benchmark (surveying) Computer science Multi-swarm optimization Set (abstract data type) Population Swarm intelligence Mathematical optimization Algorithm Artificial intelligence Mathematics Geography

Metrics

475
Cited By
11.12
FWCI (Field Weighted Citation Impact)
19
Refs
0.99
Citation Normalized Percentile
Is in top 1%
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Citation History

Topics

Metaheuristic Optimization Algorithms Research
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Algorithms and Applications
Physical Sciences →  Engineering →  Control and Systems Engineering
Artificial Immune Systems Applications
Physical Sciences →  Engineering →  Biomedical Engineering

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