摘要
针对传统大规模优化算法维数过高、过度稀疏、难以平衡等问题,文中提出基于动态自适应的双档案大规模稀疏优化算法,平衡维数和稀疏性对算法的影响,提高算法在解决大规模优化问题上的多样性和收敛性.首先,改变种群分数生成策略,加入自适应参数和惯性权重,增加分数的动态性,改善种群的多样性,使搜索不易陷入局部最优.然后,改变算法的环境选择策略,引入角度截断的思想,有效生成子代.同时引入双档案,分开真实决策变量和二进制决策变量,减少算法的运行时间.在大规模优化问题、稀疏优化问题及实际应用上的测试表明,文中算法保持原有的稀疏性质,同时稳定提升多样性和收敛性,具有较强的竞争性.
The traditional large-scale optimization algorithms generate high dimensionality and sparseness problems.A dual-archive large-scale sparse optimization algorithm based on dynamic adaptation is proposed to keep the balance of dimensionality and sparseness in the algorithm and improve the diversity and convergence performance of the algorithm in solving large-scale optimization problems.Firstly,the scores strategy for generating population is changed.By adding adaptive parameter and inertia weight,the dynamics of scores is increased,the diversity of the population is improved,and it is not easy to fall into the local optimum.Secondly,the environment selection strategy of the algorithm is changed by introducing the concept of angle truncation,and the offspring is generated effectively.Meanwhile,a double-archive strategy is introduced to separate the real decision variables from the binary decision variables and thus the running time of the algorithm is reduced.The experimental results on problems of large-scale optimization,sparse optimization and practical application show that the proposed algorithm maintains the original sparsity with steadily improved diversity and convergence and strong competitiveness.
作者
顾清华
王楚豪
江松
陈露
GU Qinghua;WANG Chuhao;JIANG Song;CHEN Lu(School of Management,Xi'an University of Architecture and Technology,Xi'an 710055;Institute of Mine Systems Engineering,Xi'an University of Architecture and Technology,Xi'an 710055;School of Resources Engineering,Xi'an University of Architecture and Technology,Xi'an 710055)
出处
《模式识别与人工智能》
CSCD
北大核心
2021年第7期592-604,共13页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金面上项目(No.51974223)
陕西省自然科学基金杰出青年项目(No.2020JC-44)
陕西省自然科学基础研究计划联合基金项目(No.2019JLP-16)资助~~
关键词
大规模
稀疏优化算法
动态自适应
惯性权重
双档案
Large-Scale
Sparse Optimization Algorithm
Dynamic Adaptation
Inertial Weight
Dual-Archive
