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Appropriate Combination of crossover operator and Mutation operator in Genetic Algorithms for the Travelling Salesman Problem

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Computers, Materials & Continua 2024年第5期79卷 2399-2425页

作者： Zakir Hussain Ahmed Habibollah Haron Abdullah Al-Tameem Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Kingdom of Saudi Arabia Faculty of Computing Universiti Teknologi MalaysiaJohor Bahru81310Malaysia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Kingdom of Saudi Arabia

Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.

关键词： Travelling salesman problem genetic algorithms crossover operator mutation operator comprehensive sequential constructive crossover insertion mutation

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The TBD method for dim targets based on multi-level crossover and matching operator

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Journal of Harbin Institute of Technology(New Series) 2011年第1期18卷 57-61页

作者：张海英段鸿廖明宏 Software School Xiamen University

In order to improve the tracking performance in this paper following TBD(Track before Detection) framework multi-level crossover and matching operator is presented.In data association stage the greedy principle is adopted to handle time complexity in DPA and at the same time crossover mathing operator is given to construct candidate trajectory.In addition the corresponding strategy is introduced in preprocessing and postprocessing to remove clutter and suppress false alarm rate.By the experimental comparison and analysis it can be found that the method is more perfer to strengthen the tracking performance of targets with SNR < 2.0 dB.

关键词： track before detection high order correlation filter crossover operator dynamic programming

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Evolving Objects for Software Engineering

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Wuhan University Journal of Natural Sciences 2001年第Z1期6卷 263-267页

作者： Wang xiao\|ling, Dong yi\|sheng Department of Computer Science and Engineering Southeast university, Nanjing 210018,China Department of Computer Science and Engineering Southeast university Nanjing China

This paper is concerned with evolving objects method for software design that can adapt to the changing environments and requirements automatically. We present system architecture with objects library, where there are objects based on domain ontologies. We define some genetic operators for objects, and discuss how to apply these genetic operators on objects to get new objects, which can satisfy new requirements.

关键词： object oriented ontology evolving objects meta object genetic operator mutation operator crossover operator

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A Genetic Algorithm for the Flowshop Scheduling Problem

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Wuhan University Journal of Natural Sciences 1998年第4期3卷 410-412页

作者： Qi Yuesheng Wang Baozhong Kang Lishan(State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072,China)

The flowshop scheduling problem is NP complete. To solve it by genetic algorithm, an efficient crossover operator is designed. Compared with another crossover operator, this one often finds a better solution within th... 详细信息

关键词： genetic algorithm crossover operator flowshop scheduling problem combinatorial optimization

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Micro Variation Chaos Genetic Algorithm

Micro Variation Chaos Genetic Algorithm

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第30届中国控制与决策会议

作者： Pan Wei Yan Mingming Tan Yuejun Xie Huixiang Noncommissioned Officer Academy Army Artillery and Air Defense Forces Academy

The traditional genetic algorithm in the search of complex nonlinear problems, prone to early convergence and the late search fatigue and other issues. In view of these problems, the paper proposes the micro variation chaos genetic algorithm. Using the characteristics of chaos and randomness, the genetic algorithm is used to compensate for the slow evolution of the genetic algorithm and the difference of the genetic diversity. At the same time, the crossover operator, mutation operator and fitness function of genetic algorithm are improved, and the performance is improved. The simulation results show that the micro variation chaotic adaptive genetic algorithm is faster and more accurate than the conventional chaos genetic algorithm and genetic algorithm.

关键词： genetic algorithm premature convergence search fatigue crossover operator mutation operator

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A Method of Single Line Diagram Layout and Optimization for Regional Distribution System Based on GA

A Method of Single Line Diagram Layout and Optimization for ...

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第30届中国控制与决策会议

作者： Boxi Zhou Guojing Zhang Ting Guo Jinliang Wang Wendong Niu Nina Lv Lianxi Sun State Grid of China Technology College

A method of single line diagram layout and optimization for regional distribution system is proposed. Taking the minimum difference between the nodes number in a distribution line and the occupancy grids number in a path as the objective function, with well-designed crossover operator and mutation operator, the GA algorithm can be utilized to seek the optimum of nodes layout positions. While drawing the distribution lines a series of strategies are used, which including line path explore, cross and overlap region identification and layout adjustment. The simulation results show that the algorithm allows nodes uniformly distributed in the figure, the connection line is clean and clear, and computational efficiency and graphic effects can satisfy the practical requirements of project.

关键词： Automatic Layout Distribution Network Genetic Algorithm crossover operator

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