A Branch-and-Bound-Based Crossover Operator for the Traveling Salesman Problem

Weise, Thomas; Jiang, Yan; Qi, Qi; Liu, Weichen

doi:10.4018/ijcini.2019070101

Cited by 3 publications

(1 citation statement)

References 42 publications

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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. The HX applies a greedy heuristic to create an offspring from two parents.…”

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confidence: 99%

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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confidence: 99%

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

Ahmed¹

2020

IJACSA

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Review of Traveling Salesman Problem Solution Methods

Yang,

Wang,

et al. 2024

Communications in Computer and Information Science

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A novel method for driving path planning with spark

Lin

Wan

et al. 2021

The Journal of Engineering

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Efficient and accurate driving path planning can help drivers drive. To solve the problem of low efficiency of traditional heuristic algorithms such as PSO and GA in solving driving path planning, Excellence Coefficient is introduced into PSO and GA combine by serial hybrid structure and make a parallel design based on Spark, which is called EC-SPPSOGA. Excellence Coefficient, calculated by length of the side, can increase the probability of good edges being left, simultaneously, preserves the possibility of longer side being selected. The parallel design of EC-SPPSOGA is based on time-consuming analysis of heuristic algorithms. The performance of EC-SPPSOGA is validated based on the real latitude and longitude of Hohhot and the data in TSPLIB. It is verified that the EC-SPPSOGA can improve efficiency of driving path planning and has good scalability.

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A Branch-and-Bound-Based Crossover Operator for the Traveling Salesman Problem

Cited by 3 publications

References 42 publications

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

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

Review of Traveling Salesman Problem Solution Methods

A novel method for driving path planning with spark

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