Multiparent recombination in evolutionary computing
Advances in evolutionary computing
An Improved PSO with Time-Varying Accelerator Coefficients
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 02
Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 02
Particle Swarm Optimization with Group Decision Making
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Agent-based evolutionary optimisation of trading strategies
International Journal of Intelligent Information and Database Systems
Evolutionary bandwidth allocation in reservation-based networks with Vickrey auctions
International Journal of Intelligent Information and Database Systems
Tackling magnetoencephalography with particle swarm optimization
International Journal of Bio-Inspired Computation
The forecasting residual life of underground pipeline based on particle swarm optimisation algorithm
International Journal of Bio-Inspired Computation
Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimisation
International Journal of Bio-Inspired Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
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Particle swarm optimiser (PSO) has shown good performance in lots of optimisation problems. However, it easily suffers from premature convergence when solving complex optimisation problems. In order to improve the performance of PSO, this paper presents an enhanced evolutionary algorithm named as PSO with hybrid multi-parent crossover and discrete recombination (PSOHMCDR), which is based on the characteristics of PSO, multi-parent crossover algorithm and differential evolution (DE). Experimental results show that PSOHMCDR outperforms other nine algorithms, including six PSO variants and three typical and effective DE variants.