摘要
为了提高多目标粒子群算法(MOPSO)的收敛性和多样性,以及增加多目标粒子群算法的适用范围,提出了一种ε约束处理混合三点随机Gbest选择多目标粒子群(ε-TMOPSO)算法。采用一种全新的三点随机Gbest选择机制,用粒子与档案集中非支配解的欧氏距离最近、最远以及处于中间位置的3个粒子构建一个备选池,然后随机选择一个粒子作为Gbest,提高算法的收敛性和多样性;采用改进的带松弛阶段ε约束处理机制处理约束条件,在前期允许加入部分优秀的不可行解,提高算法跳出局部最优的能力;融入Sigmoid函数离散变量编码处理机制,使算法能够处理混合整数问题,增加算法的适用范围。通过测试函数仿真,与EM-MOPSO、NSGA2以及SNSGA算法进行对比,结果表明本文算法在收敛性和分布性上有一定的优势。将该算法应用于乙烯装置蒸汽动力系统优化中取得了较好的效果,进一步证明了该算法的有效性。
By adopting ε constraint to handle mixed average Gbest selection,this paper proposes a new multi-objective particle swarm algorithm for improving convergence and diversity, and increasing the applicable scope. The proposed algorithm adopts a new average Gbest selection mechanism, considers Euclidean distance of the particle and non-dominated solutions of archives and corresponding to the target function value such that the convergence and diversity of algorithm can be improved. Besides, an improved constraint handling mechanism with relaxation phase is utilized, in which some excellent infeasible solutions are allowed to join in early stage so as to improve the ability to jump out of local optimum. Moreover,the proposed algorithm blends the discrete variables coding mechanism of Sigmoid function such that this algorithm can handle mixed integer problem to increase the applicable scope of algorithm. Compared with the classical MOPSO, NSGA2 and SNSGA, the proposed algorithm has advantages in convergence and distribution of steam power system in ethylene plant, which further proves the effectiveness of the algorithm.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2016年第6期827-834,共8页
Journal of East China University of Science and Technology
基金
国家自然科学基金(61403141
61573141)
上海市教育委员会和上海市教育发展基金会"曙光计划"
关键词
多目标粒子群
三点随机Gbest选择
ε约束处理
离散变量编码
蒸汽动力系统
multi-objective particle swarm
three-points random Gbest selection
constrainthandling
ε discrete variables coding
steam power system
