Method of Handling Constraints in Differential Evolution Using Fletcher’s Filter

Rafajłowicz, Wojciech

doi:10.1007/978-3-642-38610-7_5

Cited by 4 publications

(1 citation statement)

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“…The common forms of preference rules are the following: (i) any feasible solution is preferred over an infeasible solution, (ii) among two feasible solutions, the one with a better objective is preferred, and (iii) among two infeasible solutions, the one with a lowest constraint violation is preferred (Deb et al, 2002;Deb, 2000). Moreover, Fletcher's filter has been recently used as a general methodology to handle constraints in EAs (Rafajłowicz and Rafajłowicz, 2012;Rafajłowicz, 2013).…”

Section: Constraint Handlingmentioning

confidence: 99%

Stochastic Fractal Based Multiobjective Fruit Fly Optimization

Zuo

Zhao-fu

et al. 2017

International Journal of Applied Mathematics and Computer Science

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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.

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