Computational intelligence PC tools
Computational intelligence PC tools
The particle swarm: social adaptation in information-processing systems
New ideas in optimization
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Observing the swarm behaviour during its evolutionary design
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
What else is the evolution of PSO telling us?
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
An improved vector particle swarm optimization for constrained optimization problems
Information Sciences: an International Journal
A new fitness estimation strategy for particle swarm optimization
Information Sciences: an International Journal
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We introduce the PESO (Particle Evolutionary Swarm Optimization) algorithm for solving single objective constrained optimization problems. PESO algorithm proposes two new perturbation operators: "c-perturbation" and "mperturbation". The goal of these operators is to fight premature convergence and poor diversity issues observed in Particle Swarm Optimization (PSO) implementations. Constraint handling is based on simple feasibility rules. PESO is compared with respect to a highly competitive technique representative of the state-of-the-art in the area using a well-known benchmark for evolutionary constrained optimization. PESO matches mosts results and outperforms other PSO algorithms.