Multi objective ant colony algorithm for electrical wire routing

IF 0.8 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Swarm Intelligence Research Pub Date : 2020-03-20 DOI:10.1504/ijsi.2020.106411

W. Pemarathne, T. Fernando

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引用次数: 2

Abstract

Ant colony optimisation algorithms have been applied to solve wide range of difficult combinatorial optimisation problems like routing problems, assigning problems, scheduling problems and revealed remarkable solutions. In this paper we present a novel approach of ant colony optimisation algorithm to solve the electrical cable routing problem. The study focuses on optimising wire lengths, number of bends and angles of bends. We have studied these objectives in cable routing and modified the ant colony system algorithm to get better solutions. Ants are directed to search for the optimal path between the starting and the ending points by avoiding the obstacles. While ants are navigating, they travel the paths with less number of bends and consider angles of the bends towards 90, 180, and 270 degrees. Normal walls are presented as a grid and doors, windows and other obstacles are represented as rectangles. The possible points to follow by ants are designed according to the BS 7671 (IET Wiring Regulations) standards. The results of the simulation prove with comparisons that this method is feasible and effective for optimising the electrical wire routing.

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电线布线的多目标蚁群算法

蚁群优化算法已被广泛应用于解决各种复杂的组合优化问题，如路由问题、分配问题、调度问题，并揭示了显著的解决方案。本文提出了一种求解电缆布线问题的蚁群优化算法。研究的重点是优化导线长度、弯头数量和弯头角度。我们对电缆布线中的这些目标进行了研究，并对蚁群算法进行了改进以得到更好的解。蚂蚁通过避开障碍物来寻找起点和终点之间的最优路径。蚂蚁在导航时，会选择弯道较少的路径，并考虑弯道的角度为90度、180度和270度。普通的墙壁呈现为网格，门、窗和其他障碍物呈现为矩形。蚂蚁可能遵循的点是根据BS 7671 (IET布线规则)标准设计的。仿真结果与对比表明，该方法对优化布线是可行和有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Swarm Intelligence Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

2.50

自引率

0.00%

发文量

期刊介绍： The mission of the International Journal of Swarm Intelligence Research (IJSIR) is to become a leading international and well-referred journal in swarm intelligence, nature-inspired optimization algorithms, and their applications. This journal publishes original and previously unpublished articles including research papers, survey papers, and application papers, to serve as a platform for facilitating and enhancing the information shared among researchers in swarm intelligence research areas ranging from algorithm developments to real-world applications.