An Elitist Promotion Quantum-Behaved Particle Swarm Optimization Algorithm
- Resource Type
- Conference
- Authors
- Yang, Zhenlun; Wu, Angus; Liao, Haihua; Xu, Jianxin
- Source
- 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) Intelligent Human-Machine Systems and Cybernetics, 2016 8th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2016 8th International Conference on. 01:347-350 Aug, 2016
- Subject
- Bioengineering
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Convergence
Particle swarm optimization
Algorithm design and analysis
Optimization
Search problems
Sun
Iron
quantum-behaved particle swarm optimization
differential evolution operators
elitist promotion
- Language
An elitist promotion quantum-behaved particle swarm optimization (EP-QPSO) based on differential evolution operators is proposed and studied empirically in this paper. The proposed EP-QPSO uses the differential evolution operators in the elitist promotion procedure to perform search on each particle's personal best of the swarm individually to enhance the global search ability of the algorithm. A comprehensive simulation study is conducted on the unimodal and multimodal benchmark functions. Comparing with the original differential evolution algorithm and three state-of-the-art quantum-behaved particle swarm optimization algorithms, the simulation results indicate that proposed EP-QPSO has better global search capability and faster convergence speed.