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Enhancing differential evolution utilizing proximity-based mutation operators

Research output: Contribution to journalJournal articlepeer-review

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Enhancing differential evolution utilizing proximity-based mutation operators. / Epitropakis, M; Tasoulis, D K; Pavlidis, N; Plagianakos, V P; Vrahatis, M N.

In: IEEE Transactions on Evolutionary Computation, Vol. 15, No. 1, 2011, p. 99-119.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Epitropakis, M, Tasoulis, DK, Pavlidis, N, Plagianakos, VP & Vrahatis, MN 2011, 'Enhancing differential evolution utilizing proximity-based mutation operators', IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 99-119. https://doi.org/10.1109/TEVC.2010.2083670

APA

Epitropakis, M., Tasoulis, D. K., Pavlidis, N., Plagianakos, V. P., & Vrahatis, M. N. (2011). Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Transactions on Evolutionary Computation, 15(1), 99-119. https://doi.org/10.1109/TEVC.2010.2083670

Vancouver

Epitropakis M, Tasoulis DK, Pavlidis N, Plagianakos VP, Vrahatis MN. Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Transactions on Evolutionary Computation. 2011;15(1):99-119. https://doi.org/10.1109/TEVC.2010.2083670

Author

Epitropakis, M ; Tasoulis, D K ; Pavlidis, N ; Plagianakos, V P ; Vrahatis, M N. / Enhancing differential evolution utilizing proximity-based mutation operators. In: IEEE Transactions on Evolutionary Computation. 2011 ; Vol. 15, No. 1. pp. 99-119.

Bibtex

@article{30e9cf625d12435694a83a87748d2058,
title = "Enhancing differential evolution utilizing proximity-based mutation operators",
abstract = "Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.",
author = "M Epitropakis and Tasoulis, {D K} and N Pavlidis and Plagianakos, {V P} and Vrahatis, {M N}",
year = "2011",
doi = "10.1109/TEVC.2010.2083670",
language = "English",
volume = "15",
pages = "99--119",
journal = "IEEE Transactions on Evolutionary Computation",
issn = "1089-778X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Enhancing differential evolution utilizing proximity-based mutation operators

AU - Epitropakis, M

AU - Tasoulis, D K

AU - Pavlidis, N

AU - Plagianakos, V P

AU - Vrahatis, M N

PY - 2011

Y1 - 2011

N2 - Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.

AB - Differential evolution is a very popular optimization algorithm and considerable research has been devoted to the development of efficient search operators. Motivated by the different manner in which various search operators behave, we propose a novel framework based on the proximity characteristics among the individual solutions as they evolve. Our framework incorporates information of neighboring individuals, in an attempt to efficiently guide the evolution of the population toward the global optimum, without sacrificing the search capabilities of the algorithm. More specifically, the random selection of parents during mutation is modified, by assigning to each individual a probability of selection that is inversely proportional to its distance from the mutated individual. The proposed framework can be applied to any mutation strategy with minimal changes. In this paper, we incorporate this framework in the original differential evolution algorithm, as well as other recently proposed differential evolution variants. Through an extensive experimental study, we show that the proposed framework results in enhanced performance for the majority of the benchmark problems studied.

U2 - 10.1109/TEVC.2010.2083670

DO - 10.1109/TEVC.2010.2083670

M3 - Journal article

VL - 15

SP - 99

EP - 119

JO - IEEE Transactions on Evolutionary Computation

JF - IEEE Transactions on Evolutionary Computation

SN - 1089-778X

IS - 1

ER -