IEEE Trans Neural Netw Learn Syst. 2021 Jan;32(1):36-48. doi: 10.1109/TNNLS.2020.2973760. Epub 2021 Jan 4.
This article presents a two-timescale duplex neurodynamic approach to mixed-integer optimization, based on a biconvex optimization problem reformulation with additional bilinear equality or inequality constraints. The proposed approach employs two recurrent neural networks operating concurrently at two timescales. In addition, particle swarm optimization is used to update the initial neuronal states iteratively to escape from local minima toward better initial states. In spite of its minimal system complexity, the approach is proven to be almost surely convergent to optimal solutions. Its superior performance is substantiated via solving five benchmark problems.
本文提出了一种基于带有附加双线性等式或不等式约束的凸优化问题重新表述的混合整数优化双层神经动态方法。该方法采用两个在两个时间尺度上同时运行的递归神经网络。此外，粒子群优化用于迭代更新初始神经元状态，以从局部最小值逃脱到更好的初始状态。尽管其系统复杂度最低，但该方法被证明几乎肯定能收敛到最优解。通过求解五个基准问题验证了该方法的优越性能。
