Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
An improved multi-objective particle swarm optimizer for multi-objective problems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Adaptive fuzzy particle swarm optimization for global optimization of multimodal functions
Information Sciences: an International Journal
Self-adaptive learning based particle swarm optimization
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Feedback learning particle swarm optimization
Applied Soft Computing
A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization
Computational Optimization and Applications
Diversity enhanced particle swarm optimization with neighborhood search
Information Sciences: an International Journal
Swine Influenza Models Based Optimization (SIMBO)
Applied Soft Computing
The improved particle swarm optimization based on swarm distribution characteristics
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
PSO for reservoir computing optimization
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
EA'11 Proceedings of the 10th international conference on Artificial Evolution
A New Cooperative PSO Approach with Landscape Estimation, Dimension Partition, and Velocity Control
International Journal of Organizational and Collective Intelligence
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
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The particle swarm optimizer (PSO) is a population-based optimization technique that can be applied to a wide range of problems. This paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for PSO (EPUS-PSO), adopting a population manager to significantly improve the efficiency of PSO. This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on unimodal and multimodal test functions such as Quadric, Griewanks, Rastrigin, Ackley, and Weierstrass, with and without coordinate rotation. The results show good performance of the EPUS-PSO in solving most benchmark problems as compared to other recent variants of the PSO.