IEEE Trans Cybern. 2019 Jan;49(1):27-41. doi: 10.1109/TCYB.2017.2762701. Epub 2017 Nov 20.
This paper develops a decomposition-based coevolutionary algorithm for many-objective optimization, which evolves a number of subpopulations in parallel for approaching the set of Pareto optimal solutions. The many-objective problem is decomposed into a number of subproblems using a set of well-distributed weight vectors. Accordingly, each subpopulation of the algorithm is associated with a weight vector and is responsible for solving the corresponding subproblem. The exploration ability of the algorithm is improved by using a mating pool that collects elite individuals from the cooperative subpopulations for breeding the offspring. In the subsequent environmental selection, the top-ranked individuals in each subpopulation, which are appraised by aggregation functions, survive for the next iteration. Two new aggregation functions with distinct characteristics are designed in this paper to enhance the population diversity and accelerate the convergence speed. The proposed algorithm is compared with several state-of-the-art many-objective evolutionary algorithms on a large number of benchmark instances, as well as on a real-world design problem. Experimental results show that the proposed algorithm is very competitive.
本文提出了一种基于分解的多目标协同进化算法，该算法通过并行进化多个子种群来逼近帕累托最优解集。多目标问题通过一组分布良好的权重向量分解成多个子问题。相应地，算法的每个子种群都与一个权重向量相关联，负责解决相应的子问题。通过使用交配池收集来自合作子种群的精英个体来繁殖后代，提高了算法的探索能力。在随后的环境选择中，通过聚合函数评估每个子种群中排名最高的个体，这些个体在下一代迭代中生存下来。本文设计了两种具有不同特点的新聚合函数，以增强种群多样性并加快收敛速度。在大量基准实例以及一个真实设计问题上，将所提出的算法与几种最先进的多目标进化算法进行了比较。实验结果表明，所提出的算法具有很强的竞争力。
