Competitive Hopfield network combined with estimation of distribution for maximum diversity problems

Authors:
Jiahai Wang;Yalan Zhou;Jian Yin;Yunong Zhang
Affiliations:
Department of Computer Science, School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China;Information Science School, Guangdong University of Business Studies, Guangzhou, China;Department of Computer Science, School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China;Department of Automation, School of Information Science and Technology, Sun Yat-Sen University, Guangzhou, China
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Year:
2009

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Abstract

This paper presents a discrete competitive Hopfield neural network (HNN) (DCHNN) based on the estimation of distribution algorithm (EDA) for the maximum diversity problem. In order to overcome the local minimum problem of DCHNN, the idea of EDA is combined with DCHNN. Once the network is trapped in local minima, the perturbation based on EDA can generate a new starting point for DCHNN for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. The proposed algorithm is tested on 120 benchmark problems with the size ranging from 100 to 5000. Simulation results show that the proposed algorithm is better than the other improved DCHNN such as multistart DCHNN and DCHNN with random flips and is better than or competitive with metaheuristic algorithms such as tabu-search-based algorithms and greedy randomized adaptive search procedure algorithms.