动态引入第三参考点的微粒群算法研究
Research on the Active Extended Particle Swarm Optimization
为了有效地加强微粒群优化算法的全局搜索能力，并提高搜索精度，本文在递减惯性权值和基于第三目标点的微粒群算法的基础上提出一种动态的引入第三参考点改进微粒群算法(Active Extended Particle Swarm Optimization, AEPSO)。并采用Sphere 、Rosenbrock 、Griewank 和Rastrigrin 这4 个标准测试函数测试对AEPSO和其他算法进行效果测试。试验结果表明，本文改进的算法在全局搜索能力和求解精度上有很大的改善。
In order to effectively strengthen the particle swarm optimization’s global search capability and improve the search accuracy, a new algorithm (Active Extended Particle Swarm Optimization, AEPSO) based on decreasing inertia is proposed, which introduces the third reference point. Sphere, Rosenbrock, Griewank and Rastrigrin functions are used to evaluate the strategies on the AEPSO and other algorithms. The experimental results show that AEPSO gains an advantage over the other algorithms in global searching ability and precision.
赖志俊
计算技术、计算机技术
微粒群算法PSO全局搜索
Particle Swarm OptimizationPSOGlobal Search
赖志俊.动态引入第三参考点的微粒群算法研究[EB/OL].(2010-04-29)[2025-08-06].http://www.paper.edu.cn/releasepaper/content/201004-1069.点此复制
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