Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.
Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.
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DOI：
10.1109/tevc.2016.2592185
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发表时间：
2017-04
期刊：
影响因子：
--
通讯作者：
Chen CH
中科院分区：
文献类型：
--
作者：
Zhang S;Xu J;Lee LH;Chew EP;Wong WP;Chen CH
粒子群优化算法（PSO）是一种流行的确定性优化算法。粒子群优化算法起源于对鸟群或鱼群中个体运动的解释，它引入了个体最优和全局最优的概念来模拟群体觅食的模式，成功地将自然现象转化为复杂函数的优化问题。PSO的许多实际应用科普随机问题。为了使用PSO解决随机问题，一种简单的方法是在所有粒子之间平均分配计算工作量，并获得相同数量的适应度值样本。这不是计算预算的有效使用，并且留下相当大的改进空间。提出了一种将最优计算预算分配（OCBA）的概念无缝集成到粒子群优化算法（PSO）中的方法，以提高PSO算法求解随机优化问题的计算效率。我们推导出一个渐近最优的分配规则，智能地确定所有粒子的样本数，使粒子群算法可以有效地选择个人最好的和全局最好的，当有随机估计噪声的适应值。我们还提出了一个易于实现的顺序过程。数值试验表明，我们的新方法可以获得更好的结果，使用相同的计算量。
Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.