A New Particle Swarm Optimization Algorithm Based on Local-World Evolving Network Model

In this paper, A new particle swarm optimization algorithm based on local-world evolving network model is proposed to solve the problem that particle swarm optimization algorithm is prone to local optimum. The topology of particle swarm is generally considered to be a very important factor and determine the performance of the algorithm. In this paper, the particle swarm optimization algorithm and the local-world evolving network model are briefly reviewed, and then we use the network generated by the local-world evolving network model to represent the topological structure of the particle swarm. The new algorithm and original particle swarm optimization algorithm are tested in 6 benchmark functions. Comparing the test results, the new algorithm performs better on multimodal functions, the ability of the new algorithm to optimize on complex problems is significantly improved.

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