DE/isolated/1: a new mutation operator for multimodal optimization with differential evolution

Authors:
Takahiro Otani;Reiji Suzuki;Takaya Arita
Affiliations:
Graduate School of Information Science, Nagoya University, Chikusa-ku, Nagoya, Japan;Graduate School of Information Science, Nagoya University, Chikusa-ku, Nagoya, Japan;Graduate School of Information Science, Nagoya University, Chikusa-ku, Nagoya, Japan
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

Citing 7
Cited 0

Niching methods for genetic algorithms

Niching methods for genetic algorithms
Locality-preserving hashing in multidimensional spaces

STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Multidimensional binary search trees used for associative searching

Communications of the ACM
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Efficient differential evolution using speciation for multimodal function optimization

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
An extended mutation concept for the local selection based differential evolution algorithm

Proceedings of the 9th annual conference on Genetic and evolutionary computation
Differential Evolution: A Survey of the State-of-the-Art

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new variant of differential evolution for multimodal optimization termed DE/isolated/1. It generates new individuals close to an isolated individual in a current population as a niching scheme. This mechanism will evenly allocate search resources for each optimum. The proposed method was evaluated along with the existing methods through computational experiments using eight two-dimensional multimodal functions as benchmarks. Experimental results show that the proposed method shows better performance for several functions which are not effectively solved by existing algorithms.