Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Co-evolutionary particle swarm optimization to solve min-max problems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Swarms in dynamic environments
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on 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
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Many real-world problems are dynamic, requiring an optimisation algorithm which is able to continuously track a changing optimum over time. In this paper, we present a new variant of particle swarm optimisation (PSO) specifically designed to work well in dynamic environments. The main idea is to divide the population of particles into a set of interacting swarms. These swarms interact locally by dynamic regrouping and dispersing. Cauchy mutation is applied to the global best particle when the swarm detects the environment of the change. The dynamic function [proposed by Morrison and De Jong (1999)] is used to test the performance of the proposed algorithm. The numerical experimental results are compared with other variant PSO from the literature, showing that the proposed algorithm is an excellent alternative to track dynamically changing optima.