Multipopulation based differential evolution with self exploitation strategy

Authors:
Rupam Kundu;Rohan Mukherjee;Shantanab Debchoudhury
Affiliations:
Department of Electronics and Telecommunication Engineering, Jadavpur University, India;Department of Electronics and Telecommunication Engineering, Jadavpur University, India;Department of Electronics and Telecommunication Engineering, Jadavpur University, India
Venue:
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Year:
2012

Citing 6
Cited 0

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
A clustering particle swarm optimizer for dynamic optimization

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Compound particle swarm optimization in dynamic environments

Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
A clustering particle swarm optimizer for locating and tracking multiple optima in dynamic environments

IEEE Transactions on Evolutionary Computation
Particle swarm optimization with composite particles in dynamic environments

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Differential Evolution: A Survey of the State-of-the-Art

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this article a multi-population based DE-variant has been proposed to tackle DOPs. The algorithm, denoted as MPBDE-SES uses a self exploitative scheme along with classical DE. Moreover it also uses Brownian and Quantum individuals. An aging mechanism has been incorporated to get rid of stagnation. Apart from this exclusion principle, repulsion scheme and a recombination based mutation strategy causes uniform distribution of the subpopulation over the entire search space which enhances the tracking ability of the algorithm. Performance of MPBDE-SES has been tested over the suite of benchmark problems used in Competition on Evolutionary Computation in Dynamic and Uncertain Environments, held under the 2009 IEEE Congress on Evolutionary Computation (CEC) and compared with six state-of-the-art EAs. The results obtained clearly and statistically outperform the other algorithms.