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A swarm exploring neural dynamics method for solving convex multi-objective optimization problem
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DOI：10.1016/j.neucom.2024.128203.png)
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Abstract
En 中文
Multi-objective optimization problem (MOP) plays an increasingly important role in finance and engineering. In order to obtain more accurate and evenly distributed target solution set to a multi-objective programming, a novel swarm exploring neural dynamics (SEND) method is proposed, analyzed and applied in this paper. Specifically, a scalarization approach is firstly applied to transform the MOP into a group of subproblems. Secondly, each subproblem is solved by a varying parameter recurrent neural network (VP-RNN). By solving these problems, a group of Pareto optimal solutions are obtained. Thirdly, a population evolution weight optimization algorithm is used to diversify the solution set to obtain evenly distributed solutions. Simulation results demonstrate that the proposed SEND method can obtain a more accurate and evenly distributed solution set than some previous methods and the convergence rate is faster than the state-of-art methods, such as collaborative neurodynamic approach (CNA).
Keywords:
Swarm exploring neural dynamics
Multi-objective optimization problem
Pareto optimal solutions
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