Surprisingly Popular Algorithm-based Adaptive Euclidean Distance Topology Learning PSO
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The surprisingly popular algorithm (SPA) is a powerful crowd decision model proposed in social science, which can identify the knowledge possessed by the minority. Inspired by SPA, we build a new metric for particles, not just rely on fitness to evaluate particle performance. Due to the significant influence of the communication topology on exemplar selection, we propose an adaptive euclidean distance dynamic topology. And then we propose the Surprisingly Popular Algorithm-based Adaptive Euclidean Distance Topology Learning Particle Swarm Optimization (SpadePSO), which uses SPA to guide the search direction of the exploitation sub-population, and analyze the influence of different topologies on SPA. In the experimental part, we evaluate the proposed SpadePSO on the full CEC2014 benchmark suite, the spread spectrum radar polyphase coding design and the inference of ordinary differential equations. Especially, The experimental results on the full CEC2014 benchmark suite show that the SpadePSO is competitive with PSO, OLPSO, HCLPSO, GL-PSO, TSLPSO and XPSO. The mean and standard deviation of SpadePSO are lower than the other PSO variants on the spread spectrum radar polyphase coding design. Finally, the ordinary differential equations models' inference results show that SpadePSO performs better than LatinPSO, specially designed for this problem. SpadePSO has lower requirements for population number than LatinPSO.
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