Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

Hussain, Abid; Shad, Muhammad Yousaf; Sajid, Muhammad Nauman; Hussain, Ijaz; Shoukry, Alaa Mohamd; Gani, Showkat

doi:10.1155/2017/7430125

Cited by 127 publications

(71 citation statements)

References 15 publications

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“…During the last three decades with the appearance of meta-heuristic algorithms, a new era of study about optimization problems and their applications has been started. These search algorithms have also been applied on TSP such as: particle swarm optimization [10], simulated annealing [11], ant colony optimization [12], neural network [13], tabu search [14], and genetic algorithms [15][16][17]. We also used GA in this research to solve the TSP.…”

Section: Related Workmentioning

confidence: 99%

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Development a New Crossover Scheme for Traveling Salesman Problem by aid of Genetic Algorithm

Ehtasham-ul-Haq¹,

Hussain²,

Ahmad³

2019

IJISA

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─This research work provides a detailed working principle and analysis technique of multioffspring crossover operator. The proposed approach is an extension of the basic partially-mapped crossover (PMX) based upon survival of the fittest theory. It improves the performance of the genetic algorithm (GA) for solving the well-known combinatorial optimization problem, the traveling salesman problem (TSP). This study is based on numerical experiments of the proposed with other traditional crossover operators for eighteen benchmarks TSPLIB instances. The simulation results show a considerable improvement because the proposed operator enhances the opportunity of having better offspring. Moreover, the t-test also establishes the improved significance of the proposed operator. Its preferable results not only confirm the advantages over others, but also show the long run survival of a generation having a number of offspring more than the number of parents with the help of mathematical ecology theory.

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Section: Related Workmentioning

confidence: 99%

“…A profit-based genetic algorithm with TSP and obtaining good results to test on networks of cities of Poland was presented [19]. A comparative study of various crossovers with the modified form of cycle crossover operator for TSP was presented [17].…”

Section: Related Workmentioning

confidence: 99%

Development a New Crossover Scheme for Traveling Salesman Problem by aid of Genetic Algorithm

Ehtasham-ul-Haq¹,

Hussain²,

Ahmad³

2019

IJISA

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“…GAs are heuristic computing techniques that have been developed to find the best or near-best solution to optimization problems where the parameters are numerous and a theoretical approach is not suitable. Their application area is wide and includes engineering [20], traffic problems [21], medical applications such as radiology, radiotherapy, oncology and surgery [22]. This type of algorithm makes use of an abstract version of the Darwinian laws of genetics: a population of possible solutions, which are called chromosomes, is recombined randomly and evolves from a generation to the next through an iterative process.…”

Section: Introductionmentioning

confidence: 99%

Shaping of a laser-accelerated proton beam for radiobiology applications via genetic algorithm

2019

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A B S T R A C TLaser-accelerated protons have a great potential for innovative experiments in radiation biology due to the subpicosecond pulse duration and high dose rate achievable. However, the broad angular divergence makes them not optimal for applications with stringent requirements on dose homogeneity and total flux at the irradiated target. The strategy otherwise adopted to increase the homogeneity is to increase the distance between the source and the irradiation plane or to spread the beam with flat scattering systems or through the transport system itself. Such methods considerably reduce the proton flux and are not optimal for laser-accelerated protons. In this paper we demonstrate the use of a Genetic Algorithm (GA) to design an optimal non-flat scattering system to shape the beam and efficiently flatten the transversal dose distribution at the irradiated target. The system is placed in the magnetic transport system to take advantage of the presence of chromatic focusing elements to further mix the proton trajectories. The effect of a flat scattering system placed after the transport system is also presented for comparison. The general structure of the GA and its application to the shaping of a laser-accelerated proton beam are presented, as well as its application to the optimisation of dose distribution in a water target in air.

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“…www.ijacsa.thesai.org Further, we considered results reported in [37] for comparing with our proposed crossover ASCX. Recently Weise et al [38] made a comparative study among eleven crossover operators for the TSP and found that heuristic crossover (HX) [39] is the best performing operator.…”

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confidence: 99%

“…So, we implemented the GA using HX and run on the above twelve problem instances. It is to be noted that the same common parameters' values selected for GAs in [37] are used for ASCX and HX. The parameters are as follows: population size, maximum generation, crossover, and mutation probabilities are 150, 500, 0.80, and 0.10, respectively, for less than 100 size instances, whereas population size and maximum generation are 200 and 1000, respectively for more than 100 size instances.…”

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Adaptive Sequential Constructive Crossover Operator in a Genetic Algorithm for Solving the Traveling Salesman Problem

Ahmed¹

2020

IJACSA

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Genetic algorithms are widely used metaheuristic algorithms to solve combinatorial optimization problems that are constructed on the survival of the fittest theory. They obtain near optimal solution in a reasonable computational time, but do not guarantee the optimality of the solution. They start with random initial population of chromosomes, and operate three different operators, namely, selection, crossover and mutation, to produce new and hopefully better populations in consecutive generations. Out of the three operators, crossover operator is the most important operator. There are many existing crossover operators in the literature. In this paper, we propose a new crossover operator, named adaptive sequential constructive crossover, to solve the benchmark travelling salesman problem. We then compare the efficiency of the proposed crossover operator with some existing crossover operators like greedy crossover, sequential constructive crossover, partially mapped crossover operators, etc., under same genetic settings, for solving the problem on some benchmark TSPLIB instances. The experimental study shows the effectiveness of our proposed crossover operator for the problem and it is found to be the best crossover operator.

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Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

Cited by 127 publications

References 15 publications

Development a New Crossover Scheme for Traveling Salesman Problem by aid of Genetic Algorithm

Development a New Crossover Scheme for Traveling Salesman Problem by aid of Genetic Algorithm

Shaping of a laser-accelerated proton beam for radiobiology applications via genetic algorithm

Adaptive Sequential Constructive Crossover Operator in a Genetic Algorithm for Solving the Traveling Salesman Problem

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