Two efficient local search algorithms for the vertex bisection minimization problem

Tian, Xiaochen; Ouyang, Dantong; Sun, Rui; Zhou, Huisi; Zhang, Liming

doi:10.1016/j.ins.2022.07.106

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…The graph max‐cut problem is a well‐known nonpolynomial hard problem that can be solved through combinatorial optimization. [ 65 , 66 , 67 ] The max‐cut problem is generally used for optimizing complex circuit designs, and its aim is to identify a line that cuts the largest number of edges linking two different nodes in a graph composed of several nodes. The Hopfield neural network (HNN), [ 11 , 68 ] a recurrent neural network consisting of neurons (detailed information in Note S1 , Supporting Information), was developed using a 6 × 6 array of synapses to solve the max‐cut problem composed of six nodes.…”

Section: Resultsmentioning

confidence: 99%

Organic Memristor‐Based Flexible Neural Networks with Bio‐Realistic Synaptic Plasticity for Complex Combinatorial Optimization

Kim

Lee

et al. 2023

Advanced Science

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Hardware neural networks with mechanical flexibility are promising next‐generation computing systems for smart wearable electronics. Several studies have been conducted on flexible neural networks for practical applications; however, developing systems with complete synaptic plasticity for combinatorial optimization remains challenging. In this study, the metal‐ion injection density is explored as a diffusive parameter of the conductive filament in organic memristors. Additionally, a flexible artificial synapse with bio‐realistic synaptic plasticity is developed using organic memristors that have systematically engineered metal‐ion injections, for the first time. In the proposed artificial synapse, short‐term plasticity (STP), long‐term plasticity, and homeostatic plasticity are independently achieved and are analogous to their biological counterparts. The time windows of the STP and homeostatic plasticity are controlled by the ion‐injection density and electric‐signal conditions, respectively. Moreover, stable capabilities for complex combinatorial optimization in the developed synapse arrays are demonstrated under spike‐dependent operations. This effective concept for realizing flexible neuromorphic systems for complex combinatorial optimization is an essential building block for achieving a new paradigm of wearable smart electronics associated with artificial intelligent systems.

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