文章基本信息

标题：A Comparative Study of Geometric Hopfield Network and Ant Colony Algorithm to Solve Travelling Salesperson Problem
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作者：Yogeesha C.B ; Ramachandra V Pujeri
期刊名称：International Journal of Advanced Computer Research
印刷版ISSN：2249-7277
电子版ISSN：2277-7970
出版年度：2014
卷号：4
期号：16
页码：843-848
出版社：Association of Computer Communication Education for National Triumph (ACCENT)
摘要：The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and/or differentiable. Evolutionary Computation is a subfield of artificial intelligence that involves combinatorial optimization problems. Travelling Salesperson Problem (TSP), which considered being a classic example for Combinatorial Optimization problem. It is said to be NP-Complete problem that cannot be solved conventionally particularly when number of cities increase. So Evolutionary techniques is the feasible solution to such problem. This paper explores an evolutionary technique: Geometric Hopfield Neural Network model to solve Travelling Salesperson Problem. Paper also achieves the results of Geometric TSP and compares the result with one of the existing widely used nature inspired heuristic approach Ant Colony Optimization Algorithms (ACA/ACO) to solve Travelling Salesperson Problem.
关键词：Hopfield Neural Networks; Combinatorial Optimization Problem; Geometric –TSP; Ant Colony Algorithm - ACA.