A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization

• Published in: IEEE transaction on neural networks and learning systems Vol. 29; no. 4; pp. 981 - 992
• Main Authors: Yang, Shaofu, Liu, Qingshan, Wang, Jun
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collaboration; Collaborative neurodynamic approach; Computer simulation; distributed optimization; Economic models; Linear programming; multiobjective optimization; Multiple objective analysis; Neural networks; Neurodynamics; Objective function; Optimization; Pareto optimal solutions; Pareto optimization; Pareto optimum; Switches; Switching theory; Topology
• ISSN: 2162-237X; 2162-2388
• DOI: 10.1109/TNNLS.2017.2652478

This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.