IEEE/ACM Trans Comput Biol Bioinform. 2018 Nov-Dec;15(6):1877-1890. doi: 10.1109/TCBB.2017.2652453. Epub 2017 Jan 16.
Dynamic multi-objective optimization problems (DMOPs) not only involve multiple conflicting objectives, but these objectives may also vary with time, raising a challenge for researchers to solve them. This paper presents a cooperative co-evolutionary strategy based on environment sensitivities for solving DMOPs. In this strategy, a new method that groups decision variables is first proposed, in which all the decision variables are partitioned into two subcomponents according to their interrelation with environment. Adopting two populations to cooperatively optimize the two subcomponents, two prediction methods, i.e., differential prediction and Cauchy mutation, are then employed respectively to speed up their responses on the change of the environment. Furthermore, two improved dynamic multi-objective optimization algorithms, i.e., DNSGAII-CO and DMOPSO-CO, are proposed by incorporating the above strategy into NSGA-II and multi-objective particle swarm optimization, respectively. The proposed algorithms are compared with three state-of-the-art algorithms by applying to seven benchmark DMOPs. Experimental results reveal that the proposed algorithms significantly outperform the compared algorithms in terms of convergence and distribution on most DMOPs.
动态多目标优化问题（DMOPs）不仅涉及多个冲突的目标，而且这些目标还可能随时间变化，这给研究人员提出了一个解决它们的挑战。本文提出了一种基于环境敏感性的协同进化策略来解决 DMOPs。在该策略中，首先提出了一种新的分组决策变量的方法，其中所有决策变量根据其与环境的相互关系分为两个子组件。采用两个种群协同优化两个子组件，然后分别采用两种预测方法，即微分预测和柯西突变，以加快它们对环境变化的响应。此外，通过将上述策略分别纳入 NSGA-II 和多目标粒子群优化中，提出了两种改进的动态多目标优化算法，即 DNSGAII-CO 和 DMOPSO-CO。通过在七个基准 DMOPs 上应用，将所提出的算法与三种最先进的算法进行了比较。实验结果表明，在所提出的算法在大多数 DMOPs 上的收敛性和分布性方面明显优于比较算法。
