A Memetic Algorithm with Genetic Particle Swarm Optimization and Neural Network for Maximum Cut Problems

摘要

In this paper, we incorporate a chaotic discrete Hopfield neural network (CDHNN), as a local search scheme, into a genetic particle swarm optimization (GPSO) and develop a memetic algorithm GPSO-CDHNN for the maximum cut problem. The proposed algorithm not only performs exploration by using the population-based evolutionary search ability of the GPSO, but also performs exploitation by using the CDHNN. Simulation results show that the proposed algorithm has superior ability for maximum cut problems.