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Innate-Adaptive Response and Memory based Artificial Immune System for Dynamic Optimization

Volume 14, Number 9, September 2018, pp. 2048-2055
DOI: 10.23940/ijpe.18.09.p13.20482055

Weiwei Zhang, Menghua Zhang, Weizheng Zhang, Yinghui Meng, and Huaiguang Wu

School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Henan, 450000, China

(Submitted on May 23, 2018; Revised on July 24, 2018; Accepted on August 10, 2018)

Abstract:

Artificial immune systems (AIS) have been widely applied in optimization under static situations. Due to their dynamism, particular challenges are posed when handling dynamic optimization problems (DOPs). The designed algorithms must overcome these challenges to accomplish efficient results. In the paper, a new AIS based algorithm denoted as IAMAIS is proposed. In this algorithm, innate and adaptive responses in the immune system are elaborated on. The innate response is introduced to maintain the diversity of the population and implement global search, while the adaptive immune response is developed to locally locate the optima. Moreover, a memory mechanism is presented to reserve the found optima and further track the optima when environmental change happens. The experiments were applied on the most well-known benchmark, the Moving Peak Benchmark. Simulation results show that IAMAIS is competitive for Dops.

 

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