online read us now
Paper details
Number 2 - June 2017
Volume 27 - 2017
Stochastic fractal based multiobjective fruit fly optimization
Cili Zuo, Lianghong Wu, Zhao-Fu Zeng, Hua-Liang Wei
Abstract
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.
Keywords
multiobjective optimization, fruit fly optimization algorithm, stochastic fractal