A collaborative neurodynamic approach to global and combinatorial optimization
A collaborative neurodynamic approach to global and combinatorial optimization
复制标题
用于全局和组合优化的协作神经动力学方法
DOI：
10.1016/j.neunet.2019.02.002
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发表时间：
2019-06-01
期刊：
影响因子：
7.8
通讯作者：
Wang, Jun
中科院分区：
文献类型：
--
作者：
Che, Hangjun;Wang, Jun
在本文中，提出了一种用于全局和组合优化的协作神经动力学优化方法。首先，将组合优化问题重新表述为全局优化问题。其次，提出了一种基于增强拉格朗日函数的神经动力学优化模型，并证明在目标函数或约束存在非凸性的情况下，其状态在严格的局部最小值处渐近稳定。此外，采用多个神经动力学优化模型协同搜索全局最优解，并使用粒子群优化（PSO）来优化它们的初始状态。所提出的方法被证明是全局收敛于全局最优解的，并被证实可以解决基准问题。 (c) 2019 Elsevier Ltd. 保留所有权利。
In this paper, a collaborative neurodynamic optimization approach is proposed for global and combinatorial optimization. First, a combinatorial optimization problem is reformulated as a global optimization problem. Second, a neurodynamic optimization model based on an augmented Lagrangian function is proposed and its states are proven to be asymptotically stable at a strict local minimum in the presence of nonconvexity in objective function or constraints. In addition, multiple neurodynamic optimization models are employed to search for global optimal solutions collaboratively and particle swarm optimization (PSO) is used to optimize their initial states. The proposed approach is shown to be globally convergent to global optimal solutions as substantiated for solving benchmark problems. (c) 2019 Elsevier Ltd. All rights reserved.