Solution of second order Ackley function based on SAPSO algorithm
Solution of second order Ackley function based on SAPSO algorithm
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DOI：
10.1109/ccsse.2017.8088008
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Changjun Wen;Bo Xia;Xin Liu
中科院分区：
文献类型：
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作者：
Changjun Wen;Bo Xia;Xin Liu
Ackley函数是由指数函数的指数函数的余弦函数叠加而成的连续性检验函数，其特征是具有余弦波调制的近平坦区域形成孔洞或峰，使表面起伏。由于Ackley函数的唯一性，严格的局部最优解算法在爬坡过程中不可避免地福尔斯局部最优解陷阱，使得Ackley函数的搜索变得非常复杂。目前，Ackley函数比粒子群优化算法更常见。然而，在实际求解过程中，由于粒子群算法存在着精度低、易发散、平衡局部最优解和全局最大值在早期收敛过程中的好解能力差等缺点，给实际工程实践中的Ackley求解带来了很大的障碍。基于此，本文针对求解Ackley函数过程中存在的问题，综合考虑模拟退火算法（SA）和粒子群优化算法（PSO）的可能性，提出了一种退火粒子群优化算法（SAPSO），它不仅解决了粒子群算法在求解问题过程中精度不高的问题，也为解决类似情况提供了新思路。
The Ackley function is a continuity test function obtained by superimposing the cosine function of the exponential function of the exponential function, which is characterized by a nearly flat region with cosine wave modulation forming a hole or peak, which makes the surface undulating. Because of the unique function of Ackley function, the search of Ackley function is very complicated because a strict local optimal solution algorithm inevitably falls into the trap of local optimal solution in the process of climbing. At present, the Ackley function is more common than the particle swarm optimization algorithm. However, in the process of actual solution, due to the existence of low precision, easy divergence, balanced local optimal solution and global maximum in the early convergence process of particle swarm optimization Good solution of the poor ability and other shortcomings, to the actual engineering practice Ackley solution has brought great obstacles. Based on this, this paper proposes an annealing particle swarm optimization (SAPSO) algorithm based on the existing problems in the process of solving the Ackley function and considering the possibility of simulated annealing algorithm (SA) and particle swarm optimization (PSO) Which not only solves the problem that the particle swarm algorithm is not accurate in the process of solving the problem, but also provides a new idea for solving the similar situation.