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

doi:10.23940/ijpe.18.09.p13.20482055

Abstract

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.

Key words: artificial immune system, dynamic environment, moving peaks problem

Weiwei Zhang, Menghua Zhang, Weizheng Zhang, Yinghui Meng, and Huaiguang Wu. Innate-Adaptive Response and Memory based Artificial Immune System for Dynamic Optimization [J]. Int J Performability Eng, 2018, 14(9): 2048-2055.

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