| 引用本文: | 陈得宝, 赵春霞.基于内分泌调节机制的粒子群算法[J].控制理论与应用,2007,24(6):1005~1009.[点击复制] |
| CHEN De-bao, ZHAO Chun-xia .Particle swarm optimization based on endocrine regulation mechanism[J].Control Theory and Technology,2007,24(6):1005~1009.[点击复制] |
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| 基于内分泌调节机制的粒子群算法 |
| Particle swarm optimization based on endocrine regulation mechanism |
| 摘要点击 1803 全文点击 1289 投稿时间：2005-09-25 修订日期：2006-12-20 |
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| DOI编号 10.7641/j.issn.1000-8152.2007.6.028 |
| 2007,24(6):1005-1009 |
| 中文关键词 内分泌系统 神经系统 粒子群算法 函数优化 |
| 英文关键词 endocrine system neural system particle swarm algorithm(PSO) function optimization |
| 基金项目 部委跨行业重点预研项目; 安徽省教育厅自然科学基金资助项目(2006KJ090B) |
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| 中文摘要 |
| 借鉴内分泌系统的高级调节机制, 提出一种基于内分泌调节机理的粒子群算法. 首先设计一种结合当前粒子群的最好适应度、平均适应度和局部适应度的情感评价方法, 对下一代粒子群进行情感评价, 然后用神经系统和内分泌系统共同作用, 对粒子群的行为进行更新, 在更新过程中, 引入动量项减少局部收敛的发生. 文中同时分析了算法的收敛性, 并对几个典型函数优化问题和机器人路径规划进行实验, 验证方法的有效性. |
| 英文摘要 |
| Motivated by high-level regulation principle of endocrine system, a particle swarm optimization based on endocrine regulation mechanism is put forward. First, emotional evaluation method is designed combining with the best fitness, average fitness and local fitness of particles in current generation, and emotion in next generation is evaluated. Then, the behaviors of particles in next generation are updated by interaction of neural and endocrine systems, and momentum factor is used to reduce the probability of local convergence. The convergence of algorithm is analyzed, and the effectiveness is demonstrated by optimization experiments of typical functions and path planning. |
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