Is there a computational advantage to representing evaporation rate in ant colony optimization as a gaussian random variable?

2012 ◽  
Author(s):  
Ashraf M. Abdelbar
Keyword(s):  
Ant Colony Optimization ◽  
Evaporation Rate ◽  
Random Variable ◽  
Ant Colony ◽  
Gaussian Random Variable ◽  
Computational Advantage

scholarly journals Penentuan Jalur Evakuasi Terpendek Pada Industri Plastik Menggunakan Ant Colony Optimization

2021 ◽  
Vol 8 (1) ◽  
pp. 53-61
Author(s):  
Mades Darul Khairansyah ◽  
Moch Luqman Ashari ◽  
Imroatul Mufidah
Keyword(s):  
Ant Colony Optimization ◽  
Evaporation Rate ◽  
Ant Colony ◽  
Number Of Iterations

Industri plastik memiliki karyawan sebanyak 927 orang yang menempati lahan 12.062 m2. Jam operasional pada industri plastik untuk unit produksi adalah 24 jam non stop. Dengan jumlah dan jadwal tersebut mengkibatkan kepadatan pekerja yang memiliki risiko untuk mengakibatkan kecelakaan. Perusahaan plastik ini memiliki riwayat kecelakaan kerja. Pada perusahaan ini pernah terjadi kebakaran pada unit ABM pada bulan September tahun 2018 dan divisi bengkel pada Mei tahun 2014. Ant Colony Optimization (ACO) sangat cocok menentukan jalur evakuasi dalam penanganan bencana karena algoritma ini melakukan pembaruan pada feromon yang dapat menghasilkan simulasi dengan solusi lebih optimal karena memiliki laju konvergensi yang cepat, sehingga ACO akan digunakan dalam menentukan Jalur evakuasi terpendek pada industri plastik. Pada penelitian ini digunakan feromon awal sebesar 0,0098. Hasil penentuan parameter yang akan digunakan, meliputi number of iterations yaitu 500, number of ant sebesar 100, nilai Alpha sebesar 1 serta Beta sebesar 5 dan evaporation rate sebesar 0.5. Dari hasil optimasi cost yang paling rendah adalah cost 2 sehingga didapatkan rute evakuasi untuk ruang Circullar Loom menuju Koridor 3 dilanjutkan keluar melalui pintu exit 4 sehingga menuju Titik kumpul 1. Semakin tinggi cost yang dihasilkan maka akan mengakibatkan rute yang dilewati menajadi jauh sehingga meningkatkan waktu evakuasi.

Assembly Sequence Planning Utilizing Chaotic Adaptive Ant Colony Optimization Algorithm

2010 ◽  
Vol 26-28 ◽  
pp. 391-396 ◽  
Author(s):  
Yong Wang ◽  
Tian De ◽  
Ji Hong Liu
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Evaporation Rate ◽  
Assembly Sequence Planning ◽  
Ant Colony ◽  
Assembly Sequence ◽  
Ant Colony Optimization Algorithm ◽  
Assembly Sequences ◽  
Mechanical Products ◽  
Optimal Assembly

The chaotic adaptive ant colony optimization algorithm (CAACO) is proposed to seek the optimal or near-optimal assembly sequences of mechanical products. Different from the general AACO algorithm, the parameter denoting the global evaporation rate of the AACO algorithm is not specified by the designers, but is generated with the chaotic operators in the optimization process. An example is used to validate the capability of the CAACO algorithm, and the results show that the robustness of the CAACO algorithm is enhanced and more ants in the ant colony can find their own optimal or near-optimal assembly sequences than those of the general AACO algorithm.

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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