摘要
针对噪声环境下的函数优化问题提出一种混合粒子群优化算法UPSOOHT,并考察了最优计算量分配(OCBA)和噪声幅度对算法性能的影响.该算法将粒子群优化算法与假设检验及OCBA有效地结合,具有很好的全局搜索能力和局部精化能力.与其他优化算法比较的测试结果表明,UPSOOHT算法的性能和抗噪声能力都具有明显的优势.
A hybrid algorithm was proposed to solve function optimization problems in noisy environment which combined the Unified Particle Swarm Optimization Scheme, hypothesis test and optimal computing budget allocation technique together. The algorithm has good abilities of exploration and exploitation. Numerical simulations based on several representative benchmark problems were carried out in noisy environment and a comparison was made between UPSOOHT and several popular algorithms. Additionally, the influences of OCBA and noise magnitude were studied. The results show that UPSOOHT has a better performance.
出处
《吉林大学学报（理学版）》
CAS
CSCD
北大核心
2008年第5期891-896,共6页
Journal of Jilin University:Science Edition
基金
国家自然科学基金(批准号:6047300360773097)
教育部新世纪优秀人才计划项目基金(批准号:20050183065)
关键词
粒子群优化算法
噪声环境
函数优化
混合优化算法
unified particle swarm optimization
noisy environment
function optimization
hybrid optimiza-tion algorithm
