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  • Open Access

    ARTICLE

    Solving the Generalized Traveling Salesman Problem Using Sequential Constructive Crossover Operator in Genetic Algorithm

    Zakir Hussain Ahmed1,*, Maha Ata Al-Furhood2, Abdul Khader Jilani Saudagar3, Shakir Khan4

    Computer Systems Science and Engineering, Vol.48, No.5, pp. 1113-1131, 2024, DOI:10.32604/csse.2024.053574 - 13 September 2024

    Abstract The generalized travelling salesman problem (GTSP), a generalization of the well-known travelling salesman problem (TSP), is considered for our study. Since the GTSP is NP-hard and very complex, finding exact solutions is highly expensive, we will develop genetic algorithms (GAs) to obtain heuristic solutions to the problem. In GAs, as the crossover is a very important process, the crossover methods proposed for the traditional TSP could be adapted for the GTSP. The sequential constructive crossover (SCX) and three other operators are adapted to use in GAs to solve the GTSP. The effectiveness of GA using More >

  • Open Access

    ARTICLE

    Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

    Zakir Hussain Ahmed1,*, Habibollah Haron2, Abdullah Al-Tameem3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2399-2425, 2024, DOI:10.32604/cmc.2024.049704 - 15 May 2024

    Abstract Genetic algorithms (GAs) are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems. A simple GA begins with 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… More >

  • Open Access

    ARTICLE

    Mobile-Deep Based PCB Image Segmentation Algorithm Research

    Lisang Liu1, Chengyang Ke1,*, He Lin2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2443-2461, 2023, DOI:10.32604/cmc.2023.042582 - 29 November 2023

    Abstract Aiming at the problems of inaccurate edge segmentation, the hole phenomenon of segmenting large-scale targets, and the slow segmentation speed of printed circuit boards (PCB) in the image segmentation process, a PCB image segmentation model Mobile-Deep based on DeepLabv3+ semantic segmentation framework is proposed. Firstly, the DeepLabv3+ feature extraction network is replaced by the lightweight model MobileNetv2, which effectively reduces the number of model parameters; secondly, for the problem of positive and negative sample imbalance, a new loss function is composed of Focal Loss combined with Dice Loss to solve the category imbalance and improve… More >

  • Open Access

    ARTICLE

    Genetic Crossover Operators for the Capacitated Vehicle Routing Problem

    Zakir Hussain Ahmed1,*, Naif Al-Otaibi1, Abdullah Al-Tameem2, Abdul Khader Jilani Saudagar2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1575-1605, 2023, DOI:10.32604/cmc.2023.031325 - 22 September 2022

    Abstract We study the capacitated vehicle routing problem (CVRP) which is a well-known NP-hard combinatorial optimization problem (COP). The aim of the problem is to serve different customers by a convoy of vehicles starting from a depot so that sum of the routing costs under their capacity constraints is minimized. Since the problem is very complicated, solving the problem using exact methods is almost impossible. So, one has to go for the heuristic/metaheuristic methods and genetic algorithm (GA) is broadly applied metaheuristic method to obtain near optimal solution to such COPs. So, this paper studies GAs… More >

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