Stochastic fractal based multiobjective fruit fly optimization

Zuo, C.; Wu, L.; Zeng, Z. F.; Wei, H. L.

doi:10.1515/amcs-2017-0029

Artykuł - szczegóły

Tytuł artykułu

Stochastic fractal based multiobjective fruit fly optimization

Autorzy

Zuo C. , Wu L. , Zeng Z. F. , Wei H. L.

Treść / Zawartość

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$14_zuo_wu_zeng_stochastic_fractal_2017_2.pdf$

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DOI

10.1515/amcs-2017-0029

Warianty tytułu

Języki publikacji

Abstrakty

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve the convergence performance of the fruit fly optimization algorithm. To deal with multiobjective optimization problems, the Pareto domination concept is integrated into the selection process of fruit fly optimization and a novel multiobjective fruit fly optimization algorithm is then developed. Similarly to most of other multiobjective evolutionary algorithms (MOEAs), an external elitist archive is utilized to preserve the nondominated solutions found so far during the evolution, and a normalized nearest neighbor distance based density estimation strategy is adopted to keep the diversity of the external elitist archive. Eighteen benchmarks are used to test the performance of the stochastic fractal based multiobjective fruit fly optimization algorithm (SFMOFOA). Numerical results show that the SFMOFOA is able to well converge to the Pareto fronts of the test benchmarks with good distributions. Compared with four state-of-the-art methods, namely, the non-dominated sorting generic algorithm (NSGA-II), the strength Pareto evolutionary algorithm (SPEA2), multi-objective particle swarm optimization (MOPSO), and multiobjective self-adaptive differential evolution (MOSADE), the proposed SFMOFOA has better or competitive multiobjective optimization performance.

Słowa kluczowe

multiobjective optimization fruit fly optimization algorithm stochastic fractal

optymalizacja wielokryterialna algorytm optymalizacji fraktal stochastyczny

Wydawca

Oficyna Wydawnicza Uniwersytetu Zielonogórskiego

Czasopismo

International Journal of Applied Mathematics and Computer Science

Rocznik

2017

Tom

Vol. 27, no. 2

Strony

417--433

Opis fizyczny

Bibliogr. 70 poz., rys., tab., wykr.

Twórcy

autor

Zuo C.

School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, PR China

autor

Wu L.

lhwu@hnust.edu.cn

School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, PR China

autor

Zeng Z. F.

School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, Hunan 411201, PR China

autor

Wei H. L.

Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK

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Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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