An innovative hybrid multi-objective particle swarm optimization with or without constraints handling
An innovative hybrid multi-objective particle swarm optimization with or without constraints handling
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DOI：
10.1016/j.asoc.2016.06.012
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发表时间：
2016-10-01
影响因子：
8.7
通讯作者：
Shu, Zhaoxin
中科院分区：
文献类型：
--
作者：
Cheng, Shixin;Zhan, Hao;Shu, Zhaoxin
提出了一种新的混合优化算法，该算法将基于二次逼近边界优化（BOBYQA）算法和外部罚函数法的最优粒子局部搜索策略与粒子群优化（PSO）算法相结合。该方法的主要目标是提高粒子群算法的收敛性能，并保持非支配集的多样性。该算法根据拥挤距离的大小，选择外部文档中拥挤程度较低区域的非支配解，构造出一个领导粒子集，并充分利用最优粒子方法引导领导粒子快速逼近Pareto前沿。同时，在详细分析了全局最优粒子搜索方法的不足后，提出了一种局部最优粒子搜索策略，并将该算法命名为LOPMOPSO。此外，采用多维均匀变异算子防止算法陷入局部最优，并采用动态存档策略提高解的多样性。为了处理目标函数中存在的约束条件，我们采用了一种有效的不可行度评价准则来处理这些复杂的问题。各种基准函数的仿真结果表明，该方法在收敛速度方面具有很强的竞争力，并且容易生成分布良好且精确的非支配解集。二维气动优化问题的求解进一步验证了该方法的快速性和有效性。(C)© 2016 Elsevier B.V.版权所有。
This paper presents a new hybrid optimizer in which an innovative optimal particles local search strategy on basis of bound optimization by quadratic approximation (BOBYQA) algorithm and exterior penalty function method is integrated into particle swarm optimization (PSO). The main goal of the approach is to improve the convergence performance of PSO, and preserve the diversity of non-dominated set. Our algorithm selects some non-dominated solutions lied in less-crowded region of external archive based upon crowding distance value to construct a leader particles set, and make full use of optimal particles method to guide leader particles approach the Pareto front quickly. Meanwhile, a local optimal particles search strategy is proposed after particular analysis on disadvantage of global optimal particle search method, and names our algorithm as LOPMOPSO. Furthermore, the multi-dimensional uniform mutation operator is performed to prevent algorithm from trapping into local optimum, and a dynamic archive maintenance strategy is applied to improve the diversity of solutions. For coping with the constrained conditions consists in objective functions, we adopt an efficient infeasibility degree evaluation criterion to deal with these complex problems. Simulation results of various kinds of benchmark functions show that our approach is highly competitive in convergence speed and generates a well distributed and accurate set of non-dominated solutions easily. The solving of a 2-D aerodynamic optimization problem further validates its speed and effectiveness. (C) 2016 Elsevier B.V. All rights reserved.