多子群协同链式智能体遗传算法用于函数优化
首发时间:2007-10-25
摘要:普通遗传算法全局优化精度不高、运行时间长的不足,基于此,本文基于链式智能体网络结构提出了一种多子群协同链式智能体遗传算法(MPAGA)。该算法采用了多子群并行搜索的模式,链式智能体结构,动态邻域竞争和正交交叉的策略等,可实现多机并行优化,具有优化时间短、优化精度高的特点。为了验证本文算法的优越性,采用多个国际标准的测试函数对该算法性能进行测试。实验结果表明,该算法在全局优化结果的精度、优化收敛速度方面均优于MAGA。
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Multi-Population Co-Genetic Algorithm with Close Chain-Like Agents for Global Numerical Optimization
Abstract:Simple genetic algorithm has the low optimization precision and long optimization time, based on this, this paper proposed chain-like agents structure to construct a kind of multi-population agent co-genetic algorithm with chain-like agent structure (MPAGA). This algorithm adopted multi-population parallel searching mode, chain-like agent structure, dynamic neighborhood competition and orthogonal crossover strategy to realize parallel optimization, and has the characteristics of high optimization precision and short optimization time. In order to verify this algorithm, some popular benchmark functions were used for test this algorithm. The experimental results show that MPAGA has higher optimization precision and shorter optimization time than MAGA.
Keywords: genetic algorithm multi-population agent chain-like agent structure
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