一种基于引导策略的自适应粒子群优化算法
为解决粒子群优化算法前期搜索盲目,后期搜索速度慢且易陷入局部极值的问题,对算法中粒子更新方式和惯性权重进行了改进,提出了一种基于引导策略的自适应粒子群优化算法（improvedparticleswarmoptimizationalgorithm,IPSO）。该算法在种群中引入四种粒子,即主体粒子、双中心粒子、协同粒子和混沌粒子对粒子位置更新进行引导,克服算法的随机性,从而提高搜索效率。为进一步克服粒子群优化算法进化后期易陷入早熟收敛的缺点,引入聚焦距离变化率的概念,通过聚焦距离变化率的大小动态调整惯性权重,以提高算法的收敛速度和精度,两者结合极大地提高了搜索到全局最优解的有效性。对四个标准测试...
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| Published in | 计算机应用研究 Vol. 34; no. 12; pp. 3599 - 3602 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
沈阳农业大学信息与电气工程学院,沈阳,110866
2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-3695 |
| DOI | 10.3969/j.issn.1001-3695.2017.12.018 |
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| Abstract | 为解决粒子群优化算法前期搜索盲目,后期搜索速度慢且易陷入局部极值的问题,对算法中粒子更新方式和惯性权重进行了改进,提出了一种基于引导策略的自适应粒子群优化算法（improvedparticleswarmoptimizationalgorithm,IPSO）。该算法在种群中引入四种粒子,即主体粒子、双中心粒子、协同粒子和混沌粒子对粒子位置更新进行引导,克服算法的随机性,从而提高搜索效率。为进一步克服粒子群优化算法进化后期易陷入早熟收敛的缺点,引入聚焦距离变化率的概念,通过聚焦距离变化率的大小动态调整惯性权重,以提高算法的收敛速度和精度,两者结合极大地提高了搜索到全局最优解的有效性。对四个标准测试函数进行仿真,实验结果表明IPSO算法在收敛速度、收敛精度以及成功率上都明显优于其他两种粒子群优化算法。 |
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| AbstractList | TP301.6; 为解决粒子群优化算法前期搜索盲目,后期搜索速度慢且易陷入局部极值的问题,对算法中粒子更新方式和惯性权重进行了改进,提出了一种基于引导策略的自适应粒子群优化算法(improved particle swarm optimization algorithm,IPSO).该算法在种群中引入四种粒子,即主体粒子、双中心粒子、协同粒子和混沌粒子对粒子位置更新进行引导,克服算法的随机性,从而提高搜索效率.为进一步克服粒子群优化算法进化后期易陷入早熟收敛的缺点,引入聚焦距离变化率的概念,通过聚焦距离变化率的大小动态调整惯性权重,以提高算法的收敛速度和精度,两者结合极大地提高了搜索到全局最优解的有效性.对四个标准测试函数进行仿真,实验结果表明IPSO算法在收敛速度、收敛精度以及成功率上都明显优于其他两种粒子群优化算法. 为解决粒子群优化算法前期搜索盲目,后期搜索速度慢且易陷入局部极值的问题,对算法中粒子更新方式和惯性权重进行了改进,提出了一种基于引导策略的自适应粒子群优化算法（improvedparticleswarmoptimizationalgorithm,IPSO）。该算法在种群中引入四种粒子,即主体粒子、双中心粒子、协同粒子和混沌粒子对粒子位置更新进行引导,克服算法的随机性,从而提高搜索效率。为进一步克服粒子群优化算法进化后期易陷入早熟收敛的缺点,引入聚焦距离变化率的概念,通过聚焦距离变化率的大小动态调整惯性权重,以提高算法的收敛速度和精度,两者结合极大地提高了搜索到全局最优解的有效性。对四个标准测试函数进行仿真,实验结果表明IPSO算法在收敛速度、收敛精度以及成功率上都明显优于其他两种粒子群优化算法。 |
| Abstract_FL | In order to solve the problems of blind search in the early stage and slow search speed as well as easily trapped in the local optimum in the later period,this paper proposed an adaptive particle swarm optimization algorithm based on guiding strategy(IPSO) by improving the particle updating way and inertia weight.The algorithm introduced four kinds of panicles in the population,which were the main panicles,double center particles,cooperative particles and chaos particles.The algorithm decreased the randomness and improved the search efficiency through guiding particle position updating.Moreover,the new algorithm introduced the focusing distance changing rate which adjusted the inertia weight dynamically by the size of the focusing distance changing rate to improve the convergence speed and accuracy.The combination of the both modes improved the effectiveness of the search for the global optimal solution greatly.The simulation experiments tested on the four benchmark functions.The resultsshow that IPSO has obviously higher convergence rate,convergence accuracy and success rate than the other two algorithms. |
| Author | 姜凤利;张宇;王永刚 |
| AuthorAffiliation | 沈阳农业大学信息与电气工程学院,沈阳110866 |
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| Author_FL | Wang Yonggang Zhang Yu Jiang Fengli |
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| Keywords | hybrid particles 混合粒子 panicle swarm optimization (PSO) 粒子群优化算法 inertia weight 惯性权重 |
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| Title | 一种基于引导策略的自适应粒子群优化算法 |
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