Choi Tae Jong, Ahn Chang Wook, An Jinung
Department of Computer Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Suwon, Republic of Korea.
ScientificWorldJournal. 2013 Jul 2;2013:969734. doi: 10.1155/2013/969734. Print 2013.
Adaptation of control parameters, such as scaling factor (F), crossover rate (CR), and population size (NP), appropriately is one of the major problems of Differential Evolution (DE) literature. Well-designed adaptive or self-adaptive parameter control method can highly improve the performance of DE. Although there are many suggestions for adapting the control parameters, it is still a challenging task to properly adapt the control parameters for problem. In this paper, we present an adaptive parameter control DE algorithm. In the proposed algorithm, each individual has its own control parameters. The control parameters of each individual are adapted based on the average parameter value of successfully evolved individuals' parameter values by using the Cauchy distribution. Through this, the control parameters of each individual are assigned either near the average parameter value or far from that of the average parameter value which might be better parameter value for next generation. The experimental results show that the proposed algorithm is more robust than the standard DE algorithm and several state-of-the-art adaptive DE algorithms in solving various unimodal and multimodal problems.
适当地调整控制参数，如缩放因子（F）、交叉率（CR）和种群大小（NP），是差分进化（DE）文献中的主要问题之一。精心设计的自适应或自适应性参数控制方法可以极大地提高差分进化的性能。尽管有许多关于调整控制参数的建议，但针对具体问题恰当地调整控制参数仍然是一项具有挑战性的任务。在本文中，我们提出了一种自适应参数控制的差分进化算法。在所提出的算法中，每个个体都有其自己的控制参数。通过使用柯西分布，基于成功进化个体的参数值的平均参数值来调整每个个体的控制参数。通过这种方式，每个个体的控制参数被分配在平均参数值附近或远离平均参数值，而远离平均参数值的可能是下一代更好的参数值。实验结果表明，在解决各种单峰和多峰问题时，所提出的算法比标准差分进化算法和几种先进的自适应差分进化算法更具鲁棒性。
