Travelling Salesman Problem (TSP) adalah permasalahan optimasi kombinatorial untuk menemukan rute perjalanan dengan total jarak yang terpendek dengan syarat semua kota dikunjungi dan semua kota dikunjungi hanya sekali. Pada penelitian... more
Travelling Salesman Problem (TSP) adalah permasalahan optimasi kombinatorial untuk menemukan rute perjalanan dengan total jarak yang terpendek dengan syarat semua kota dikunjungi dan semua kota dikunjungi hanya sekali. Pada penelitian ini akan dilakukan penerapan algoritma Crow Search Algorithm (CSA) Modified CSA K-Factor (MCSA K-Factor), Modified CSA Dynamic Awareness Probability (MCSA DAP) dan Modified CSA Flight Length (MCSA fl) untuk menyelesaikan TSP. Data TSP yang akan digunakan meliputi: data kecil (5 kota), data sedang (48 kota) dan data besar (100 kota). Algoritma CSA, MCSA K-Factor, MCSA DAP, dan MCSA fl diimplementasikan pada program komputer dengan bahasa pemrograman PHP dan Javascript. Dari hasil perhitungan ditemukan bahwa pada penyelesaian TSP data kecil, algoritma CSA, MCSA K-Factor, MCSA DAP, dan MCSA fl dapat menghasilkan nilai total jarak sebesar 93,62 yang berhasil mencapai solusi optimal. Sedangkan penyelesaian masalah TSP data sedang, algoritma MCSA DAP menghasilkan nilai total jarak sebesar 26953,87. Dan penyelesaian TSP data besar, algoritma MCSA fl menghasilkan nilai total jarak sebesar 36363,6.
In this paper, a new crossover operator named Neighbor-based Constructive Crossover (NCX) is evolved for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The proposed crossover operator... more
In this paper, a new crossover operator named Neighbor-based Constructive Crossover (NCX) is evolved for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The proposed crossover operator uses the better edges present in parents’ structure by comparing the neighboring nodes of a node in order to generate off-springs. The efficacy of the proposed crossover operator, NCX is set against two other crossover operators, single point crossover (SPCX) [19] and sequential constructive crossover (SCX) [1] for several standard TSPLIB instances [2]. Empirical results and observations illustrate that the new crossover operator is better than the SPCX and SCX in terms of quality of solutions.
In this paper, a new crossover operator named Neighbor-based Constructive Crossover (NCX) is evolved for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The proposed crossover operator... more
In this paper, a new crossover operator named Neighbor-based Constructive Crossover (NCX) is evolved for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The proposed crossover operator uses the better edges present in parents' structure by comparing the neighboring nodes of a node in order to generate off-springs. The efficacy of the proposed crossover operator, NCX is set against two other crossover operators, single point crossover (SPCX) [19] and sequential constructive crossover (SCX) [1] for several standard TSPLIB instances [2]. Empirical results and observations illustrate that the new crossover operator is better than the SPCX and SCX in terms of quality of solutions.
