IEEE Trans Cybern. 2017 Sep;47(9):2794-2808. doi: 10.1109/TCYB.2017.2710133. Epub 2017 Jun 12.
The selection of swarm leaders (i.e., the personal best and global best), is important in the design of a multiobjective particle swarm optimization (MOPSO) algorithm. Such leaders are expected to effectively guide the swarm to approach the true Pareto optimal front. In this paper, we present a novel external archive-guided MOPSO algorithm (AgMOPSO), where the leaders for velocity update are all selected from the external archive. In our algorithm, multiobjective optimization problems (MOPs) are transformed into a set of subproblems using a decomposition approach, and then each particle is assigned accordingly to optimize each subproblem. A novel archive-guided velocity update method is designed to guide the swarm for exploration, and the external archive is also evolved using an immune-based evolutionary strategy. These proposed approaches speed up the convergence of AgMOPSO. The experimental results fully demonstrate the superiority of our proposed AgMOPSO in solving most of the test problems adopted, in terms of two commonly used performance measures. Moreover, the effectiveness of our proposed archive-guided velocity update method and immune-based evolutionary strategy is also experimentally validated on more than 30 test MOPs.
群体领袖的选择（即个人最佳和全局最佳）对于多目标粒子群优化（MOPSO）算法的设计非常重要。这些领袖应该能够有效地引导群体接近真正的帕累托最优前沿。在本文中，我们提出了一种新颖的基于外部档案的 MOPSO 算法（AgMOPSO），其中用于速度更新的领袖都从外部档案中选择。在我们的算法中，使用分解方法将多目标优化问题（MOPs）转换为一组子问题，然后相应地为每个粒子分配任务来优化每个子问题。设计了一种新颖的档案引导速度更新方法来引导群体进行探索，并且还使用基于免疫的进化策略来进化外部档案。这些方法可以加快 AgMOPSO 的收敛速度。实验结果充分证明了我们提出的 AgMOPSO 在解决大多数采用的测试问题方面的优越性，这体现在两个常用的性能指标上。此外，还在 30 多个测试 MOPs 上验证了我们提出的档案引导速度更新方法和基于免疫的进化策略的有效性。
