Swarm intelligence
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Rotated test problems for assessing the performance of multi-objective optimization algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Cooperative co-evolutionary differential evolution for function optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
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
Integral Particle Swarm Optimization with Dispersed Accelerator Information
Fundamenta Informaticae - Swarm Intelligence
Individual predicted integral-controlled particle swarm optimisation
International Journal of Innovative Computing and Applications
A modified particle swarm optimization for correlated phenomena
Applied Soft Computing
A cooperative particle swarm optimizer with statistical variable interdependence learning
Information Sciences: an International Journal
Integral Particle Swarm Optimization with Dispersed Accelerator Information
Fundamenta Informaticae - Swarm Intelligence
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
Large scale optimization based on co-ordinated bacterial dynamics and opposite numbers
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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This paper attempts to address the question of scaling up Particle Swarm Optimization (PSO) algorithms to high dimensional optimization problems. We present a cooperative coevolving PSO (CCPSO) algorithm incorporating random grouping and adaptive weighting, two techniques that have been shown to be effective for handling high dimensional nonseparable problems. The proposed CCPSO algorithms outperformed a previously developed coevolving PSO algorithm on nonseparable functions of 30 dimensions. Furthermore, the scalability of the proposed algorithm to high dimensional nonseparable problems (of up to 1000 dimensions) is examined and compared with two existing coevolving Differential Evolution (DE) algorithms, and new insights are obtained. Our experimental results show the proposed CCPSO algorithms can perform reasonably well with only a small number of evaluations. The results also suggest that both the random grouping and adaptive weighting schemes are viable approaches that can be generalized to other evolutionary optimization methods.