A Collaborative Neurodynamic Approach to Multiobjective Optimization

Man Fai Leung, Jun Wang

Research output: Contribution to journalArticlepeer-review

55 Citations (Scopus)


There are two ultimate goals in multiobjective optimization. The primary goal is to obtain a set of Pareto-optimal solutions while the secondary goal is to obtain evenly distributed solutions to characterize the efficient frontier. In this paper, a collaborative neurodynamic approach to multiobjective optimization is presented to attain both goals of Pareto optimality and solution diversity. The multiple objectives are first scalarized using a weighted Chebyshev function. Multiple projection neural networks are employed to search for Pareto-optimal solutions with the help of a particle swarm optimization (PSO) algorithm in reintialization. To diversify the Pareto-optimal solutions, a holistic approach is proposed by maximizing the hypervolume (HV) using again a PSO algorithm. The experimental results show that the proposed approach outperforms three other state-of-the-art multiobjective algorithms (i.e., HMOEA/D, MOEA/DD, and NSGAIII) most of times on 37 benchmark datasets in terms of HV and inverted generational distance.

Original languageEnglish
Article number8327871
Pages (from-to)5738-5748
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number11
Publication statusPublished - Nov 2018
Externally publishedYes


  • Collaborative neurodynamic approach
  • multiobjective optimization
  • neural networks
  • Pareto-optimal solutions


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