Yazar "Aktaş, Muhammet" seçeneğine göre listele

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    Swarm intelligence algorithms for combinatorial optimization: Encoding and decoding strategies
    (Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi, 2025) Aktaş, Muhammet; Kılıç, Fatih
    This thesis introduces a new Discrete StarFish Optimization Algorithm (D-SFOA) to solve a complex discrete Symmetric Travelling Salesman Problem (STSP). In the discrete SFOA algorithm, the continuous values of individuals in the population are converted to the discrete version using the random key method. Ten neighbourhood methods used in this study provide diversity to the starfish population, and the 2-opt local search algorithm allows the study to find shorter tours. The performance of D-SFOA is tested on STSP datasets ranging in size from 30 to 1084 from TSPLIB. This thesis also introduces a Modified Choice Function (MCF) to the Discrete Artificial Bee Colony algorithm for adaptive neighbor selection in the symmetric traveling salesman problem. The Metropolis Acceptance Criteria (MAC) are then utilized to provide a chance for more inferior optimal solutions. Discrete versions of the Grey Wolf Optimizer (D-GWO) and Harris Hawk Optimization (D-HHO) algorithms are applied with the same parameters to compare the performance of the proposed algorithms. The algorithm uses descriptive statistics such as average tour, best tour, percentage of deviation of the mean tour, percentage of deviation of the best tour, and execution time to ensure a fair comparison. The Wilcoxon signed-rank test and Ablation test are applied to measure the significant difference in the values of the algorithms and to observe the performance effect of the main components used in the proposed algorithm on tour length and execution time, respectively. The proposed D-SFOA and D-ABC algorithms outperform the other algorithms.