Ant Colony Hyper-heuristics for Travelling Salesman Problem

Aziz, Zalilah Abd

doi:10.1016/j.procs.2015.12.333

Cited by 34 publications

(12 citation statements)

References 8 publications

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“…The technique can improve the overall solution around seven percent using huge instances of data and it is also can reduce the computational time [9]. In addition, Ant Colony Optimization (ACO) hyperheuristic technique is also a better solution in handling navigation problem [11]. According to Aziz [11], ACO solution is better than several other methods such as simulated annealing, annealing genetic, Tabu search, and adaptive Tabu.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…In addition, Ant Colony Optimization (ACO) hyperheuristic technique is also a better solution in handling navigation problem [11]. According to Aziz [11], ACO solution is better than several other methods such as simulated annealing, annealing genetic, Tabu search, and adaptive Tabu. Beyond that, Roberti et al [12] found that general variable neighbourhood search and dynamic programming can solve 20 customers' instances in a short amount of computing time.…”

Section: Genetic Algorithmmentioning

confidence: 99%

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Heuristic based model for groceries shopping navigator

Peeyee

Abdul-Rahman

Hamid

et al. 2019

IJEECS

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This paper presents a heuristic based model for groceries shopping navigator that attempts to improve the navigation problem that usually face by customers while doing their shopping. A system known as Shopping Navigator or shortly SHoNa was developed to give the optimal sequence of shelves to be visited by the customer and the total estimated shopping time so that the user can plan their shopping task earlier. Genetic algorithm was employed and implemented in a web-based platform that is compatible with other devices such as smartphones and tablets. SHoNA can minimize the shopping time by identifying the most optimal order of shelves inside the supermarket that needs to be visited by the customer. A series of experimental was performed in producing the optimum model. Our findings showed that the combination of order one crossover and inverse mutation produced a better optimal performance, which is the minimum total amount of groceries shopping time. SHoNA can be further enhanced with visualization features for a better shopping experience.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Heuristic based model for groceries shopping navigator

Peeyee

Abdul-Rahman

Hamid

et al. 2019

IJEECS

View full text Add to dashboard Cite

show abstract

“…Beberapa contoh penelitian seperti, Cooperative Genetic Ant System (CGAS) [11] dan Ant Colony Hyper-heuristics (ACO HH) [8]. CGAS merupakan metode yang menggabungkan antara Algortima Genetika dan Ant System.…”

Section: Penelitian Terkaitunclassified

“…Metode ini merupakan perkembangan dari Algoritma Ant Colony, dimana ACO HH dirancang khusus untuk menaikan hasil yang optimal. Algoritma ACO HH hanya mengganti set beberapa nilai heuristik dan nilai fungsi evaluasi tergantung pada masalah yang akan dipecahkannya [8]. Namun pada penelitian ini, fokus pada perbandingan hasil penelitian terbaru yaitu menggunakan metode ACO HH [8].…”

Section: Penelitian Terkaitunclassified

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Kombinasi Firefly Algorithm-Tabu Search untuk Penyelesaian Traveling Salesman Problem

Hays¹

2017

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Abstrak-Traveling Salesman ProblemTSP merupakan masalah menemukan rute terpendek pada sebuah pencarian, dimana masing-masing kota harus dikunjungi tepat satu kali mulai dari sebuah kota dan kembali lagi pada kota tersebut. Hal ini menjadikan TSP berada dikelas non-deterministic polynomial-time hard problem. TSP juga terlibat dalam banyak aplikasi kehidupan nyata [8]. TSP mungkin terlihat sebagai masalah yang sederhana, namun masalah ini merupakan masalah yang penting dan membutuhkan penyelesaian yang konvensional. Hal ini dapat dibuktikan bahwa jika seseorang mencoba menyelesaikan TSP, ia akan membutuhkan waktu lebih lama dibandingkan umur ia hidup di Bumi [21].Pada Penelitian ini, diajukan metode yang terinspirasi dari perilaku segerombolan kunang-kunang. Metode ini disebut Firefly Algorithm (FA), FA sangat efektif dan dapat mengungguli algoritma konvensional lainnya, seperti algoritma genetika, untuk memecahkan banyak masalah optimasi (Nadhir 2013). Namun, pada dasarnya FA memiliki kekurangan dalam penyelesaian optimasi dengan skala besar [6]. Sehingga, dibutuhkan kombinasi metode agar hasil yang diharapkan akan lebih akurat. Tabu Search (TS) merupakan metode optimasi yang terbukti efektif dan sering digunakan untuk memecahkan masalah optimasi dengan skala besar [13][25]. Sehingga, kombinasi FA dan TS (FATS) diharapkan dapat diimplementasikan dalam menyelesaikan TSP, terlebih dapat meningkatkan hasil akurasi penyelesaiannya.

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Hyper-heuristics: Autonomous Problem Solvers

Mısır

2021

Automated Design of Machine Learning and Search Algorithms

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Ant Colony Hyper-heuristics for Travelling Salesman Problem

Cited by 34 publications

References 8 publications

Heuristic based model for groceries shopping navigator

Heuristic based model for groceries shopping navigator

Kombinasi Firefly Algorithm-Tabu Search untuk Penyelesaian Traveling Salesman Problem

Hyper-heuristics: Autonomous Problem Solvers

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