免疫综合学习粒子群优化算法
针对综合学习粒子群算法后期收敛速度慢、一旦所有粒子陷入局部最优，则无法跳出等缺陷，提出免疫综合学习粒子群优化（ICLPSO）算法。ICLPSO算法引入人工免疫系统中的克隆选择机制，利用克隆复制、高频变异、克隆选择等操作，增加种群的多样性，提高算法的收敛速度，利用柯西分布较宽的两翼分布特性进行精英粒子学习以进一步增强粒子逃离局部极值及多峰函数优化问题全局寻优能力。针对标准测试函数的仿真结果表明，与其他改进粒子群算法相比，ICLPSO算法收敛速度快，求解精度更高。...
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| Published in | 计算机应用研究 Vol. 31; no. 11; pp. 3229 - 3233 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
湖南大学 电气与信息工程学院，长沙410082
2014
湖南工程学院电气信息学院，湖南湘潭411101%湖南大学 电气与信息工程学院,长沙,410082%湖南科技大学 信息与电气工程学院,湖南 湘潭,411021 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2014.11.007 |
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| Abstract | 针对综合学习粒子群算法后期收敛速度慢、一旦所有粒子陷入局部最优，则无法跳出等缺陷，提出免疫综合学习粒子群优化（ICLPSO）算法。ICLPSO算法引入人工免疫系统中的克隆选择机制，利用克隆复制、高频变异、克隆选择等操作，增加种群的多样性，提高算法的收敛速度，利用柯西分布较宽的两翼分布特性进行精英粒子学习以进一步增强粒子逃离局部极值及多峰函数优化问题全局寻优能力。针对标准测试函数的仿真结果表明，与其他改进粒子群算法相比，ICLPSO算法收敛速度快，求解精度更高。 |
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| AbstractList | 针对综合学习粒子群算法后期收敛速度慢、一旦所有粒子陷入局部最优，则无法跳出等缺陷，提出免疫综合学习粒子群优化（ICLPSO）算法。ICLPSO算法引入人工免疫系统中的克隆选择机制，利用克隆复制、高频变异、克隆选择等操作，增加种群的多样性，提高算法的收敛速度，利用柯西分布较宽的两翼分布特性进行精英粒子学习以进一步增强粒子逃离局部极值及多峰函数优化问题全局寻优能力。针对标准测试函数的仿真结果表明，与其他改进粒子群算法相比，ICLPSO算法收敛速度快，求解精度更高。 TP301.6; 针对综合学习粒子群算法后期收敛速度慢、一旦所有粒子陷入局部最优，则无法跳出等缺陷，提出免疫综合学习粒子群优化（ICLPSO）算法。ICLPSO算法引入人工免疫系统中的克隆选择机制，利用克隆复制、高频变异、克隆选择等操作，增加种群的多样性，提高算法的收敛速度，利用柯西分布较宽的两翼分布特性进行精英粒子学习以进一步增强粒子逃离局部极值及多峰函数优化问题全局寻优能力。针对标准测试函数的仿真结果表明，与其他改进粒子群算法相比，ICLPSO算法收敛速度快，求解精度更高。 |
| Abstract_FL | Convergence of the comprehensive learning particle swarm optimization(CLPSO)algorithm is relatively slow at the late stage of evolution.Once all particles trapped in local optimum,the algorithm can not jump out of the local optimum.This paper proposed immune comprehensive learning particle swarm optimization(ICLPSO)algorithms.The algorithm introduced clonal se-lection mechanism in artificial immune system.Using of clonal copy,hypermutation and clonal selection,it increased the diversi-ty of the population,improved the convergence rate and enhanced the ability of escape from the local optimum and multi-mode op-timization ability of global optimization.Using the elitist learning strategy,the ability to escape from local optimia is further en-hanced.Experiments on several benchmark functions verify the effective of the proposed algorithm. |
| Author | 林国汉 章兢 刘朝华 |
| AuthorAffiliation | 湖南大学电气与信息工程学院,长沙410082 湖南工程学院电气信息学院,湖南湘潭411101 湖南科技大学信息与电气工程学院,湖南湘潭411021 |
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| Author_FL | LIU Zhao-hua ZHANG Jing LIN Guo-han |
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| ClassificationCodes | TP301.6 |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| Keywords | elitist learning 人工免疫系统 精英学习 comprehensive learning particle swarm optimization algorithm 函数优化 function optimization 综合学习粒子群算法(CLPSO) artificial immune system |
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| Notes | LIN Guo-han, ZHANG Jing, LIU Zhao-hua （1. College of Electrical ＆ Information Engineering, Hunan University, Changsha 410082, China; 2. College of Electrical ＆ Information, Hunan Institute of Engineering, Xiangtan Hunan 411101, China; 3. School of Information ＆ Electrical Engineering, Hunan University of Science ＆ Tech- nology, Xiangtan Hunan 411021, China） 51-1196/TP Convergence of the comprehensive learning particle swarm optimization（CLPSO）algorithm is relatively slow at the late stage of evolution.Once all particles trapped in local optimum,the algorithm can not jump out of the local optimum.This paper proposed immune comprehensive learning particle swarm optimization（ICLPSO）algorithms.The algorithm introduced clonal se-lection mechanism in artificial immune system.Using of clonal copy,hypermutation and clonal selection,it increased the diversi-ty of the population,improved the convergence rate and enhanced the ability of escape from the local optimum and multi-mode op-timization ability of global optimizat |
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| Publisher | 湖南大学 电气与信息工程学院，长沙410082 湖南工程学院电气信息学院，湖南湘潭411101%湖南大学 电气与信息工程学院,长沙,410082%湖南科技大学 信息与电气工程学院,湖南 湘潭,411021 |
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| Snippet | 针对综合学习粒子群算法后期收敛速度慢、一旦所有粒子陷入局部最优，则无法跳出等缺陷，提出免疫综合学习粒子群优化（ICLPSO）算法。ICLPSO算法引入人工免疫系统中的克隆选... TP301.6; 针对综合学习粒子群算法后期收敛速度慢、一旦所有粒子陷入局部最优，则无法跳出等缺陷，提出免疫综合学习粒子群优化（ICLPSO）算法。ICLPSO算法引入人工免疫系统中... |
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| SubjectTerms | 人工免疫系统 函数优化 精英学习 综合学习粒子群算法（CLPSO） |
| Title | 免疫综合学习粒子群优化算法 |
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