A Modified Two Part Chromosome Crossover for Solving MTSP Using Genetic Algorithms

Kaliaperumal, Rajeswari; Ramalingam, A.; J, Sripriya

doi:10.1145/2743065.2743103

Cited by 7 publications

(4 citation statements)

References 3 publications

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“…In multiobjective MTSP the total distance traveled by the agents and the balance of agent working times are the objectives. Kaliaperumal et al [47] also used Genetic Algorithm to solve the MTSP. They developed a brand new crossover operator called modified two-part chromosome crossover and obtained better results using this new operator.…”

Section: Overview Of Solution Methodsmentioning

confidence: 99%

The Multiagent Planning Problem

et al. 2017

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The classical Multiple Traveling Salesmen Problem is a well-studied optimization problem. Given a set ofngoals/targets andmagents, the objective is to findmround trips, such that each target is visited only once and by only one agent, and the total distance of these round trips is minimal. In this paper we describe the Multiagent Planning Problem, a variant of the classical Multiple Traveling Salesmen Problem: given a set ofngoals/targets and a team ofmagents,msubtours (simple paths) are sought such that each target is visited only once and by only one agent. We optimize for minimum time rather than minimum total distance; therefore the objective is to find the Team Plan in which the longest subtour is as short as possible (a min–max problem). We propose an easy to implement Genetic Algorithm Inspired Descent (GAID) method which evolves a set of subtours using genetic operators. We benchmarked GAID against other evolutionary algorithms and heuristics. GAID outperformed the Ant Colony Optimization and the Modified Genetic Algorithm. Even though the heuristics specifically developed for Multiple Traveling Salesmen Problem (e.g.,k-split, bisection) outperformed GAID, these methods cannot solve the Multiagent Planning Problem. GAID proved to be much better than an open-source Matlab Multiple Traveling Salesmen Problem solver.

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Section: Overview Of Solution Methodsmentioning

confidence: 99%

The Multiagent Planning Problem

et al. 2017

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“…In [29], a modified two-part chromosome crossover is proposed for the problem. As reported, the algorithm assigns various number of cities for different salesman and obtained good solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Genetic Algorithms for the Multiple Travelling Salesman Problem

Al-Furhud¹,

Hussain²

2020

IJACSA

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We consider the multiple travelling salesman Problem (MTSP) that is one of the generalization of the travelling salesman problem (TSP). For solving this problem genetic algorithms (GAs) based on numerous crossover operators have been described in the literature. Choosing effective crossover operator can give effective GA. Generally, the crossover operators that are developed for the TSP are applied to the MTSP. We propose to develop simple and effective GAs using sequential constructive crossover (SCX), adaptive SCX, greedy SCX, reverse greedy SCX and comprehensive SCX for solving the MTSP. The effectiveness of the crossover operators is demonstrated by comparing among them and with another crossover operator on some instances from TSPLIB of various sizes with different number of salesmen. The experimental study shows the promising results by the crossover operators, especially CSCX, for the MTSP.

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“…A modified TCX is suggested in [36] for the MTSP. As reported, the algorithm assigns different number of cities for different salesmen and obtains good solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem

Al-Furhud

Ahmed

2020

Mathematical Problems in Engineering

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The multiple travelling salesman problem (MTSP), an extension of the well-known travelling salesman problem (TSP), is studied here. In MTSP, starting from a depot, multiple salesmen require to visit all cities so that each city is required to be visited only once by one salesman only. It is NP-hard and is more complex than the usual TSP. So, exact optimal solutions can be obtained for smaller sized problem instances only. For large-sized problem instances, it is essential to apply heuristic algorithms, and amongst them, genetic algorithm is identified to be successfully deal with such complex optimization problems. So, we propose a hybrid genetic algorithm (HGA) that uses sequential constructive crossover, a local search approach along with an immigration technique to find high-quality solution to the MTSP. Then our proposed HGA is compared against some state-of-the-art algorithms by solving some TSPLIB symmetric instances of several sizes with various number of salesmen. Our experimental investigation demonstrates that the HGA is one of the best algorithms.

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A Modified Two Part Chromosome Crossover for Solving MTSP Using Genetic Algorithms

Cited by 7 publications

References 3 publications

The Multiagent Planning Problem

The Multiagent Planning Problem

Genetic Algorithms for the Multiple Travelling Salesman Problem

Experimental Study of a Hybrid Genetic Algorithm for the Multiple Travelling Salesman Problem

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