基于分解和向量的多目标鲨鱼优化算法  

Multi-objective Shark Smell Optimization Algorithm Based on Decomposition and Vector

作　　者：李宏伟[1] LI Hong-wei(Department of Information Engineering, Taiyuan Urban Vocational College, Taiyuan 030001, China)

机构地区：[1]太原城市职业技术学院信息工程系,山西太原030001

出　　处：《计算机与现代化》2021年第9期43-50,共8页Computer and Modernization

基　　金：国家自然科学青年基金资助项目(61503002)。

摘　　要：为了提高多目标鲨鱼算法在收敛速度和解集的分布性,提出一种基于分解和向量的多目标鲨鱼优化算法(DVMOSSO)。首先针对基本鲨鱼算法收敛性和多样性难以平衡的问题,通过在精英集采过程中,用参考向量计算角度惩罚距离标量值来平衡目标空间中解的收敛性和多样性。除此之外,针对基本鲨鱼算法在迭代后期易早熟收敛,陷入局部最优的缺陷,采用高斯变异策略重新初始化粒子,同时在精英解集中采用多项式变异来增加种群的多样性。最后,为了验证本文所提算法的有效性,将本文所提的DVMOSSO算法与NSGAII-DS、MOEA/D、MMOPSO、MOSSO和dMOSSO算法在标准测试函数上进行对比实验,实验结果表明本文所提算法具有良好的收敛性和分布性,算法收敛精度更高,寻优能力更强。In order to improve the convergence rate and the distribution of the solution set of the multi-objective shark algorithm,this paper proposes a multi-objective shark smell optimization algorithm based on decomposition and vector(DVMOSSO).Firstly,aiming at the problem that the convergence and diversity of the basic shark algorithm are difficult to balance,this paper uses the reference vector to calculate the angle penalty distance scalar value to balance the convergence and diversity of the solution in the target space in the process of elite centralized mining.In addition,the basic shark algorithm is easy to converge prematurely and fall into local optimum in the late iteration.In this paper,Gaussian mutation strategy is used to reinitialize the particles,and polynomial mutation is used to increase the diversity of the population in the elite solution set.Finally,in order to verify the effectiveness of the proposed algorithm,the proposed DVMOSSO algorithm is compared with NSGAII-DS,MOEA/D,MMOPSO,MOSSO and dMOSSO algorithm in the standard test function.The experimental results show that the proposed algorithm has good convergence and distribution,higher convergence accuracy and stronger optimization ability.

关 键 词：鲨鱼优化算法 精英解集 分解 向量 重新初始化 多项式变异 

分 类 号：TP306[自动化与计算机技术—计算机系统结构]