A novel discrete Rat swarm optimization (DRSO) algorithm for solving the traveling salesman problem

Q1 Decision Sciences
Toufik Mzili, M. E. Riffi, I. Mzili, Gaurav Dhiman
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引用次数: 10

Abstract

: Metaheuristics are often used to find solutions to real and complex problems. These algorithms can solve optimization problems and provide solutions close to the global optimum in an acceptable and reasonable time. In this paper, we will present a new bio-inspired metaheuristic based on the natural chasing and attacking behaviors of rats in nature, called a Rat swarm optimizer. Which has given good results in solving several continuous optimization problems, and adapted it to solve a discrete, NP-hard, and classical optimization problem that is the traveling salesman problem (TSP) while respecting the natural behavior of rats. To test the efficiency of the adaptation of our proposal, we applied the adapted rat swarm optimization (RSO) algorithm to some reference instances of TSPLIB. The obtained results show the performance of the proposed method in solving the traveling salesman problem (TSP). design design
一种求解旅行商问题的离散鼠群优化算法
:元启发式通常用于寻找真实和复杂问题的解决方案。这些算法可以解决优化问题,并在可接受和合理的时间内提供接近全局最优的解决方案。在本文中,我们将提出一种新的基于老鼠在自然界中自然追逐和攻击行为的仿生元启发式方法,称为鼠群优化器。它在解决几个连续优化问题方面取得了良好的效果,并将其应用于解决一个离散的、NP难的、经典的优化问题,即旅行商问题(TSP),同时尊重大鼠的自然行为。为了测试我们的建议的自适应效率,我们将自适应鼠群优化(RSO)算法应用于TSPLIB的一些参考实例。所得结果表明了该方法在求解旅行商问题中的性能。设计
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来源期刊
Decision Making Applications in Management and Engineering
Decision Making Applications in Management and Engineering Decision Sciences-General Decision Sciences
CiteScore
14.40
自引率
0.00%
发文量
35
审稿时长
14 weeks
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