| 引用本文: | 王宗山,丁洪伟,王杰,李波,侯鹏,杨志军.基于正交设计的折射反向学习樽海鞘群算法[J].哈尔滨工业大学学报,2022,54(11):122.DOI:10.11918/202111074 |
| WANG Zongshan,DING Hongwei,WANG Jie,LI Bo,HOU Peng,YANG Zhijun.Salp swarm algorithm based on orthogonal refracted opposition-based learning[J].Journal of Harbin Institute of Technology,2022,54(11):122.DOI:10.11918/202111074 |
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| 基于正交设计的折射反向学习樽海鞘群算法 |
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王宗山1,丁洪伟1,王杰2,李波1,侯鹏3,杨志军1,4
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(1.云南大学 信息学院,昆明 650500;2.郑州大学 机械与动力工程学院,郑州 450066; 3.复旦大学 计算机科学技术学院,上海 201203;4.云南省教育厅 教育科学研究院,昆明 650021)
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| 摘要: |
| 为克服基本樽海鞘群算法(SSA)存在的收敛速度慢、高维求解精度低等不足,提出正交折射反向学习机制和自适应惯性权重策略,嵌入SSA中,得到一种基于正交设计的折射反向学习樽海鞘群算法(OOSSA)。正交折射反向学习策略中,采用基于透镜成像原理的折射反向学习策略以加强对反向解空间的勘探,极大地降低了算法陷入局部最优的概率;采用正交试验设计构建若干部分维上取折射反向值的部分反向解,深度挖掘并保存当前个体和折射反向个体的优势维度信息。此外,在跟随者位置更新阶段引入惯性权重因子,有效地改善跟随者的搜索模式并增强算法的局部开采能力。采用CEC2017基准函数进行仿真实验,同时使用Wilcoxon秩和检验、Friedman检验等方法来评价OOSSA算法的优化性能,测试结果表明所提算法的寻优精度和收敛速度明显优于基本SSA算法、8种新近的改进SSA算法和9种前沿的群体智能优化算法。此外,将所提算法应用于一个工程设计问题,结果表明该算法在工程优化方面的性能优于对比算法。最后,针对求解自主移动机器人路径规划问题,提出一种基于OOSSA的路径规划算法。在3种环境设置下对所提算法进行仿真实验,并与PSO、ABC、GWO、FA和SSA等算法进行对比。仿真结果表明,本文算法能够规划出最优的无碰撞路径。系统的实验表明OOSSA算法可作为问题优化的有效工具。 |
| 关键词: 樽海鞘群算法 透镜折射学习 正交试验设计 自适应学习 基准函数 工程优化 路径规划 |
| DOI：10.11918/202111074 |
| 分类号:TP301.6 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(3,4,61562092)；云南省教育厅科学研究基金(2022Y008) |
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| Salp swarm algorithm based on orthogonal refracted opposition-based learning |
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WANG Zongshan1,DING Hongwei1,WANG Jie2,LI Bo1,HOU Peng3,YANG Zhijun1,4
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(1.School of Information Science and Engineering, Yunnan University, Kunming 650500, China; 2.School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450066, China; 3.School of Computer Science, Fudan University, Shanghai 201203, China; 4.Educational and Scientific Institute, Education Department of Yunnan Province, Kunming 650021, China)
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| Abstract: |
| The basic salp swarm algorithm (SSA) may suffer from the drawbacks of slow convergence and low accuracy of high-dimensional solutions. To solve these limitations, we proposed an improved SSA algorithm (OOSSA) which integrates orthogonal refracted opposition-based learning strategy and self-adaptive inertia weight strategy into SSA. In orthogonal refracted learning strategy, the refracted opposition-based learning based on the optical lens imaging principle was employed to enhance the exploration scope of the inverse solution space, which greatly reduced the probability of the algorithm falling into the local optimum. The orthogonal experimental design was used to construct several partial opposite solutions that take the refracted-based inverse values in part of the dimension, so as to deeply mine and preserve the dominant dimensional information of the current individual and the refracted-based opposite individual. In addition, an adaptive inertia weight was introduced in the follower position update phase to effectively improve the follower search pattern and enhance the local exploitation ability of the algorithm. The CEC2017 benchmark functions were employed for simulation experiments. Also, Wilcoxon’s rank-sum test and Friedman test were performed to verify the superiority of the proposed method. Experimental results show that the proposed OOSSA outperformed the basic SSA, eight improved SSA variants, and nine cutting-edge swarm-based intelligence algorithms. Moreover, the algorithm was applied to an engineering design problem, and results show that the algorithm had better performance than other algorithms in engineering optimization. Finally, an OOSSA-based robot path planning approach was developed for solving the path planning problem in autonomous mobile robots. The proposed algorithm was simulated in three environment settings and compared with other algorithms including particle swarm optimization (PSO), artificial bee colony (ABC), grey wolf optimizer (GWO), firefly algorithm (FA), and SSA. Simulation results show that the proposed algorithm could plan the optimal collision-free paths compared with its competitors. The systematic experiments indicate that the OOSSA algorithm can be an effective tool for problem optimization. |
| Key words: salp swarm algorithm refracted opposition-based learning orthogonal experimental design self-adaptive learning benchmark functions engineering design optimization path planning |
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