[1]钱小宇,葛洪伟,蔡明.基于目标空间分解和连续变异的多目标粒子群算法[J].智能系统学报,2019,14(3):464-470.[doi:10.11992/tis.201711015]
QIAN Xiaoyu,GE Hongwei,CAI Ming.Decomposition and continuous mutation-based multi-objective particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2019,14(3):464-470.[doi:10.11992/tis.201711015]
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QIAN Xiaoyu,GE Hongwei,CAI Ming.Decomposition and continuous mutation-based multi-objective particle swarm optimization[J].CAAI Transactions on Intelligent Systems,2019,14(3):464-470.[doi:10.11992/tis.201711015]
基于目标空间分解和连续变异的多目标粒子群算法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
14
期数:
2019年第3期
页码:
464-470
栏目:
学术论文—机器学习
出版日期:
2019-05-05
- Title:
- Decomposition and continuous mutation-based multi-objective particle swarm optimization
- Keywords:
- multi-objective optimization; particle swarm optimization algorithm; decomposition; sub-region; mutation; differential; Gaussian mutation; Cauchy mutation
- 分类号:
- TP391.4
- 摘要:
- 针对当前多目标粒子群优化算法收敛性和多样性不佳等问题，提出了一种基于目标空间分解和连续变异的多目标粒子群优化算法。利用目标空间分解方法将粒子群分配到预先设定好的子区域中，在该过程中，通过一种新适应值公式来对每个子区域中的粒子进行择优筛选，该适应值公式融入了支配强度因素；在全局搜索过程中，使用差分变异、高斯变异和柯西变异对全局引导粒子的位置进行连续变异操作。将该算法与当前主流的一些多目标优化算法进行对比实验，结果表明，本文提出的算法在提高粒子收敛性的同时，多样性也得到了提升。
- Abstract:
- In light of the poor convergence problems and the diversity of current multi-objective optimization algorithms, in this paper, we propose an objective-space decomposition and continuous mutation-based multi-objective particle-swarm-optimization algorithm. Its innovations are as follows:we use a space decomposition method to distribute the particle swarm into a predefined sub-region. During this process, we apply a new adaptive value formula to select and filter the particles in each sub-region and incorporate a fitness formula into the dominance factor. In the global search process, we apply differential, Gaussian, and Cauchy mutations to continuously mutate the position of the global guide particle. We compare the performance of this algorithm with those of current multi-objective optimization algorithms, and the results show that the proposed algorithm improves the convergence and diversity of the particles.
- 参考文献/References:
-
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备注/Memo
收稿日期:2017-11-13。
基金项目:江苏省普通高校研究生科研创新计划项目（KYLX16_0781，KYLX16_0782）；江苏高校优势学科建设工程资助项目（PAPD）.
作者简介:钱小宇,男,1992年生,硕士研究生,主要研究方向为人工智能与模式识别;葛洪伟,男,1967年生,教授,博士,博士生导师,主要研究方向为人工智能与模式识别、机器学习、图像处理与分析等;蔡明,男,1962年生,高级工程师,主要研究方向为计算机软件、网络应用的研究。
通讯作者:葛洪伟.E-mail:ghw8601@163.com
更新日期/Last Update:
1900-01-01