基于分解的多目标花朵授粉算法

在过去几十年里,许多多目标进化算法被广泛应用于解决多目标优化问题,其中一种比较流行的多目标进化算法是基于分解的多目标进化算法(MOEA/D)。花朵授粉算法是一种启发式优化算法,但迄今为止,花朵授粉算法在基于分解的多目标进化算法领域的研究还非常少。本文在基于分解的多目标进化算法的框架下,将花朵授粉算法拓展至多目标优化领域,提出一种基于分解的多目标花朵授粉算法(MOFPA/D)。此外,为了保证非支配解的多样性,本文提出一种基于网格的目标空间分割法,该方法从找到的Pareto最优解集中筛选出一定数量且分布均匀的Pareto最优解。实验结果表明,基于分解的多目标花朵授粉算法在收敛性与多样性方面均优于基于分解的多目标进化算法。

A Multi-objective Flower Pollination Algorithm Based on Decomposition

During the past decades, a variety of multi-objective evolutionary algorithms (MOEAs) have been widely used to solve all kinds of multi-objective optimization problems (MOPs). One of the representative MOEAs is the multi-objective evolution algorithm based on decomposition, called MOEA/D. The flower pollination algorithm (FPA) is a meta-heuristic optimization algorithm. However, to our best knowledge, so far there are few papers studying on FPA based on decomposition in the multi-objective optimization field. In this paper, under the framework of MOEA/D, we extend the initial FPA to decomposed-based multi-objective optimization field and further present a multi-objective FPA based on decomposition, called MOFPA/D. In addition, in order to keep the diversity of nondominated solutions in the external archive, we address a novel strategy, named grid-based segmentation of objective space to select some Pareto optimal solutions for output. The simulation results indicate that MOFPA/D is highly competitive with or superior to the initial MOEA/D in terms of solution convergence and diversity.