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Travelling Salesman Problem

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Travelling Salesman Problem
Solusi optimal travelling salesman problem dengan Ant Colony System (ACS)
Rina Refianti dan A.Benny Mutiara Jurusan Teknik Informatika, Universitas Gunadarma Abstraksi Ant Colony System (ACS) adalah sebuah metodologi yang dihasilkan melalui pengamatan terhadap semut. Di dalam ACS terdapat sejumlah semut buatan, dinamai ants, yang berfungsi sebagai agent yang ditugaskan untuk mencari solusi terhadap suatu masalah optimisasi. ACS telah diterapkan dalam berbagai bidang, salah satunya adalah untuk mencari solusi optimal pada Traveling Salesman Problem (TSP). Dengan memberikan sejumlah n kota, TSP dapat didefinisikan sebagai suatu permasalahan dalam menemukan jalur terpendek dengan mengunjungi setiap kota yang ada hanya sekali. ACS telah diuji dan dibandingkan dengan metodologi yang lain untuk membuktikan keoptimalannya tersebut. Dan hal inilah yang menjadi dasar bagi penulis dalam melakukan penelitian terhadap ACS, yaitu untuk membuktikan keoptimalannya tersebut. Penelitian ini dilakukan dengan mengimplementasikan ACS ke dalam bentuk kode-kode program berbahasa Java. Kemudian dilakukan percobaan untuk membandingkan antara ACS dengan metodologi lainnya yang juga meng-implementasikan agent di dalamnya. Dari hasil percobaan diketahui bahwa secara garis besar ACS terbukti merupakan metodologi yang paling optimal dalam menemukan jalur terpendek. Dan penelitian ini telah berhasil membuktikan keoptimalan dari ACS dalam menemukan solusi terhadap TSP. Keyword: Genetic Algorithm, Ant System, Software Agent, Pheromone, state transition rule 1. Pengantar Salah rekayasa satu paradigma lunak baru dalam dapat dihindari akan dibutuhkannya lebih dari satu agent, seiring dengan semakin kompleksnya tugas yang dikerjakan oleh sistem tersebut. Paradigma pengembangan sistem di mana dalam suatu komunitas terdapat beberapa agent yang saling berinteraksi, bernegosiasi, dan berkoordinasi satu sama lain dalam menjalankan pekerjaan disebut dengan Multi Agent System (MAS) [Romi]. Colony M. Dan
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