Biomimicry of symbiotic multi-species coevolution for discrete and continuous optimization in RFID networks

Saudi Journal of Biological Sciences

Fang Liu¹  Xiaodan Liang²  Na Lin²  Hanning Chen²  Shikai Jing³ 

[1] Beijing Shenzhou Aerospace Software Technology Co. Ltd., Beijing 110000, China;School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China;School of Mechanical Engineering, Beijing Institute of Technology, Beijing 110081, China;

关键词: Symbiosis;    Particle swarm optimization;    Multi-swarm coevolution;    Global optimization;    RFID;   

DOI  :  10.1016/j.sjbs.2017.01.033

来源: DOAJ

【 摘 要 】

In recent years, symbiosis as a rich source of potential engineering applications and computational model has attracted more and more attentions in the adaptive complex systems and evolution computing domains. Inspired by different symbiotic coevolution forms in nature, this paper proposed a series of multi-swarm particle swarm optimizers called PS2Os, which extend the single population particle swarm optimization (PSO) algorithm to interacting multi-swarms model by constructing hierarchical interaction topologies and enhanced dynamical update equations. According to different symbiotic interrelationships, four versions of PS2O are initiated to mimic mutualism, commensalism, predation, and competition mechanism, respectively. In the experiments, with five benchmark problems, the proposed algorithms are proved to have considerable potential for solving complex optimization problems. The coevolutionary dynamics of symbiotic species in each PS2O version are also studied respectively to demonstrate the heterogeneity of different symbiotic interrelationships that effect on the algorithm’s performance. Then PS2O is used for solving the radio frequency identification (RFID) network planning (RNP) problem with a mixture of discrete and continuous variables. Simulation results show that the proposed algorithm outperforms the reference algorithms for planning RFID networks, in terms of optimization accuracy and computation robustness.

【 授权许可】

Unknown