Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Chaotic Inertia Weight in Particle Swarm Optimization
ICICIC '07 Proceedings of the Second International Conference on Innovative Computing, Informatio and Control
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Allocation of local and global search capabilities of particle in canonical PSO
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization
Computers and Operations Research
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers
Expert Systems with Applications: An International Journal
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This study proposes an adaptive staged particle swarm optimization (ASPSO) algorithm based on analyses of particles' search capabilities First, the search processes of the standard PSO (SPSO) and the linear decreasing inertia weight PSO (LDWPSO) are analyzed based on our previous definition of exploitation Second, three stages of the search process in PSO are defined Each stage has its own search preference, which is represented by the exploitation capability of swarm Third, the mapping between inertia weight, learning factor (w-c) and the exploitation capability is given At last, the ASPSO is proposed By setting different values of w-c in three stages, one can make swarm search the space with particular strategy in each stage, and the particles can be directed to find the solution more effectively The experimental results show that the proposed ASPSO has better performance than SPSO and LDWPSO on most of test functions.