Call for Papers Editorial Board Content List Subscribe Submission Paper Status Paper Review
Grey Adaptive Particle Swarm Optimization
Author： Ming-Feng Yeh, Min-Shyang Leu and Ti-Hung Chen
Keyword： Evolutionary computation， Grey relational analysis， Parameter automation strategy， Particle swarm optimization
Summary： Based on grey relational analysis, this studyattempts to propose a grey-based evolutionary stateestimation (ESE) technique to improve the search abilityof particle swarm optimization (PSO). The proposedESE approach is performed to identify one of thefollowing four predefined evolutionary states, e.g.,exploration, exploitation, convergence and jumping out,in each generation. Then, the inertia weight and theacceleration coefficients are automatically adjustedaccording to the identified state. Such a PSO is termedthe grey adaptive PSO (GAPSO) in this study. Inaddition, the GAPSO is applied to solve the optimizationproblems of six benchmark functions for illustration.Simulation results show that the proposed GAPSOcould outperform the adaptive PSO, the grey PSO, andtwo well-known PSO variants on most of the testfunctions.
Journal of Grey System(Volume 16)