Environment and Social Psychology

Understanding social and environmental behavior patterns through hybrid neural swarm clustering

Xiangqin Dai

1 School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia 2 Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing, 40044, China

Mohd Najwadi Yusoff

School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, 11800, Malaysia

Lei Wang

School of Electrical and Electronic Engineering Engineering Campus, Universiti Sains Malaysia, Nibong Tebal 14300, Malaysia

Xiangguang Dai

Key Laboratory of Intelligent Information Processing and Control, Chongqing Three Gorges University, Chongqing, 40044, China

DOI: https://doi.org/10.59429/esp.v10i7.3833

Keywords: Discrete hopfield neural network; particle swarm optimization; integer optimization problem; bipartition

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Abstract

Most of the clustering problems can be reformulated as combinational optimization problems. It is not easy to search for the global solution for combinational optimization problems. In this paper, we use the Discrete Hopfield Neural Network (DHN) to solve the Bipartition Clustering Problem (BCP) and combine Particle Swarm Optimization (PSO) to search for the global solution. Firstly, the BCP is reformulated into an integer optimization problem. Secondly, to ensure the local solution of BCP in convergence and stability, some rules of  DHN are designed to solve the Integer Optimization Problem(IOP). Finally, PSO is proposed to reset the neuron states of DHN until the global solution of BCP is achieved. Numerical and real-world experiments are conducted to evaluate the validity and feasibility of the proposed method. The experimental results show that our method achieves better clustering results on different datasets and problem instances, and has higher accuracy and stability compared to traditional methods.

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