Evaluating evolutionary algorithms
Artificial Intelligence - Special volume on empirical methods
Evolutionary computation
Swarm intelligence
An analysis of particle swarm optimizers
An analysis of particle swarm optimizers
Journal of Global Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
A novel particle swarm optimizer hybridized with extremal optimization
Applied Soft Computing
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A New Particle Swarm Algorithm and Its Globally Convergent Modifications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An heterogeneous particle swarm optimizer with predator and scout particles
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
Hi-index | 0.00 |
A new particle swarm optimization (PSO) that incorporates a hybrid mutation strategy is proposed. In this paper we first use the Monte Carlo method to investigate the behavior of the particle in PSO. The results reveal the essence of the particle's trajectory during executions and the reasons why PSO has relative poor global searching ability especially in the last stage of evolution. Then we present a new hybrid particle swarm optimization which incorporates Henon map mutation operation (HPSO) so as to enhance the achievement of PSO. The new mutation strategy divides the mutation operator into global and local mutation operators, then it enables the particles to have stronger exploration ability and fast convergence rate. Sixteen benchmark functions are used to test the performance of HPSO. The results show that the new PSO algorithm performs better than the other hybrid PSO algorithms for each of the test functions. Meanwhile, HPSO is applied to a practical problem (i.e., the economic dispatch problem in a power system) with a satisfying result.