Swarm intelligence
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A new collaborative evolutionary-swarm optimization technique
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Biological plausibility in optimisation: an ecosystemic view
International Journal of Bio-Inspired Computation
Dynamic evolutionary membrane algorithm in dynamic environments
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Three improved hybrid metaheuristic algorithms for engineering design optimization
Applied Soft Computing
A classification scheme for agent based approaches to dynamic optimization
Artificial Intelligence Review
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A hybrid approach called Evolutionary Swarm Cooperative Algorithm (ESCA) based on the collaboration between a particle swarm optimization algorithm and an evolutionary algorithm is presented. ESCA is designed to deal with moving optima of optimization problems in dynamic environments. ESCA uses three populations of individuals: two EA populations and one Particle Swarm Population. The EA populations evolve by the rules of an evolutionary multimodal optimization algorithm being used to maintain the diversity of the search. The particle swarm confers precision to the search process. The efficiency of ESCA is evaluated by means of numerical experiments.