| 融合随机逼近算法的粒子群优化算法 |
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| 引用本文： | 罗金炎. 融合随机逼近算法的粒子群优化算法[J]. 计算机系统应用, 2015, 24(6): 108-113 |
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| 作者姓名： | 罗金炎 |
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| 作者单位： | 闽江学院数学系,福州,350108 |
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| 基金项目： | 国家自然科学基金(11301255);福建省中青年教师教育科研项目(JK2013040) |
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| 摘 要： | 针对粒子群优化算法(PSO)在寻优进程中的缺陷，提出一种融合随机逼近算法的粒子群优化算法，该算法选择合适时机将随机逼近算法融入粒子群优化算法维持种群的多样性，并且在算法寻优进程中充分利用已有的计算资源提高算法寻优效率，最后通过典型标准函数数值实验表明，改进后的粒子群优化算法寻优速度快、精度高、具较好的稳定性。
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| 关 键 词： | 粒子群优化算法 随机逼近 全局最优位置 函数优化 |
| 收稿时间： | 2014-10-14 |
| 修稿时间： | 2014-12-08 |
Improved Particle Swarm Optimizer with Stochastic Approximation |
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| LUO Jin-Yan. Improved Particle Swarm Optimizer with Stochastic Approximation[J]. Computer Systems& Applications, 2015, 24(6): 108-113 |
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| Authors: | LUO Jin-Yan |
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| Affiliation: | Department of Mathematics, Minjiang University, Fuzhou 350108, China |
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| Abstract: | In order to overcome the shortcomings of the particle swarm optimization(PSO), an improved particle swarm optimization based on simultaneous perturbation stochastic approximation(SPSA) method is proposed. It embeds SPSA into PSO as a local search operator in the proper time, and makes use of the computing resources available in the optimization process. Numerical experiments for benchmark functions have been done, The results indicate that the proposed algorithm performs better than the existing ones in terms of efficiency, accuracy and stability. |
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| Keywords: | particle swarm optimization stochastic approximation global best position function optimization |
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