【摘 要】
：
粒子群优化算法(Particle Swarm Optimization,PSO)由于其算法简单高效,已被广泛用于各种寻优问题。种群规模是PSO算法的一个重要参数,表示粒子种群中粒子的个数,种群规模大小对算法特性和计算代价影响很大,种群太小,会造成局部收敛；而种群太大,计算代价太高而且收敛速度慢。经典PSO方法中,种群大小是固定的,随着优化迭代的更新,种群中的粒子向最优点收敛,此时过大种群规模会增加
【机 构】
：
华中科技大学土木工程与力学学院,武汉,湖北,430074
论文部分内容阅读
粒子群优化算法(Particle Swarm Optimization,PSO)由于其算法简单高效,已被广泛用于各种寻优问题。种群规模是PSO算法的一个重要参数,表示粒子种群中粒子的个数,种群规模大小对算法特性和计算代价影响很大,种群太小,会造成局部收敛；而种群太大,计算代价太高而且收敛速度慢。经典PSO方法中,种群大小是固定的,随着优化迭代的更新,种群中的粒子向最优点收敛,此时过大种群规模会增加计算负担,为此本文提出一种自适应种群PSO算法。首先依据海明距离和熵函数,定义种群离散度。离散度大小在区间[0,1]中,离散度大,说明种群空间分布多样性比较好,离散度小,说明种群空间分布比较差。将种群最大迭代次数分为N个周期,在每个周期根据上一周期种群离散度改变种群规模,如果种群离散度大于阀值,则增大种群规模,反之则减少种群规模,同时讨论离散度阀值的选取准则。采用自适应种群规模PSO算法对四个常用的测试函数和一个工程案例进行优化计算,仿真结果表明自适应种群规模PSO算法在维持较好的全局搜索能力的同时,能极大的减少适应度函数的调用从而提高了PSO算法的整体计算效率。
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