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Taufiq A.Gani
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Neuroscience Transportation

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PENGARUH INISIALISASI POPULASI RANDOM SEARCH PADA ALGORITMA BEREVOLUSI DALAM OPTIMASI TRAVELLING SALESMAN PROBLEM (TSP) Fitriyani Fitriyani; Yuwaldi Away; Taufiq A.Gani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 1, No 2 (2018): OKTOBER 2018
Publisher : Program Studi Teknik Informatika, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (543.632 KB) | DOI: 10.32672/jnkti.v1i2.770

Abstract

Traveling Salesman Problem (TSP) is a problem where each initial route of departure and return path between regions remains the same. The problem with TSP is how to get the optimum results to get the shortest path that will be passed, to solve TSP problems, one way can be by using evolved algorithms. Evolution Algorithm (AE) is a method that uses natural selection as the main idea in solving a particular problem. This algorithm is implemented through computer simulations starting from the individual population that will be randomly generated and then evaluated to achieve the best solution. Random search is included in part of stochastic optimization and global optimization. Random search is a direct search method that does not require derivatives to search for continue domains. The purpose of this study was to see the effect of population initialization with random search on algorithms evolving in TSP optimization. Results of initialization strategy implementation Random search has shown more optimal results compared to pure initialization without using random search. This is due to the initialization searching for space for random initialization.
PENGARUH INISIALISASI POPULASI RANDOM SEARCH PADA ALGORITMA BEREVOLUSI DALAM OPTIMASI TRAVELLING SALESMAN PROBLEM (TSP) Fitriyani Fitriyani; Yuwaldi Away; Taufiq A.Gani
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 1, No 2 (2018): OKTOBER 2018
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v1i2.770

Abstract

Traveling Salesman Problem (TSP) is a problem where each initial route of departure and return path between regions remains the same. The problem with TSP is how to get the optimum results to get the shortest path that will be passed, to solve TSP problems, one way can be by using evolved algorithms. Evolution Algorithm (AE) is a method that uses natural selection as the main idea in solving a particular problem. This algorithm is implemented through computer simulations starting from the individual population that will be randomly generated and then evaluated to achieve the best solution. Random search is included in part of stochastic optimization and global optimization. Random search is a direct search method that does not require derivatives to search for continue domains. The purpose of this study was to see the effect of population initialization with random search on algorithms evolving in TSP optimization. Results of initialization strategy implementation Random search has shown more optimal results compared to pure initialization without using random search. This is due to the initialization searching for space for random initialization.
Co-Authors Fitriyani Fitriyani Fitriyani, Martunis Yuwaldi Away