ACO powered by Local Searches Algorithms for the Solution of TSP Problems

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Nisreen L. Ahmed
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
General Purpose ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
New Approach ◽  
Local Searches ◽  
Probabilistic Technique ◽  
The Social ◽  
Great Deluge Algorithm

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.  Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

scholarly journals An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ivan A. Mantilla-Gaviria ◽  
Alejandro Díaz-Morcillo ◽  
Juan V. Balbastre-Tejedor
Keyword(s):  
Ant Colony Optimization ◽  
Filter Design ◽  
Optimization Method ◽  
General Purpose ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
X Band ◽  
Waveguide Filters ◽  
Convergence Performance ◽  
Band Pass

A practical and useful application of the Ant Colony Optimization (ACO) method for microwave corrugated filter design is shown. The classical, general purpose ACO method is adapted to deal with the microwave filter design problem. The design strategy used in this paper is an iterative procedure based on the use of an optimization method along with an electromagnetic simulator. The designs of high-pass and band-pass microwave rectangular waveguide filters working in the C-band and X-band, respectively, for communication applications, are shown. The average convergence performance of the ACO method is characterized by means of Monte Carlo simulations and compared with that obtained with the well-known Genetic Algorithm (GA). The overall performance, for the simulations presented herein, of the ACO is found to be better than that of the GA.

An Improved Personnel Evacuation Cellular Automata Model Based on the Ant Colony Optimization Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 3287-3291 ◽  
Author(s):  
Dan Qing Wang ◽  
Qing Ge Gong ◽  
Xiao Fei Shen
Keyword(s):  
Cellular Automata ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Evacuation Simulation ◽  
Simulation Research ◽  
Cellular Automata Model ◽  
The Social ◽  
Improved Model

Nowadays, public safety has already attracted great attention, especially when natural disasters and other emergencies happen more and more frequently. So, personnel evacuation simulation research in the populated areas has become one of the core issues to reduce the social damage. To improve the simulation theory, this paper puts forward an improved cellular automata model using some idea of the classic Ant Colony Optimization Algorithm for reference when making rules for the evacuating personnel. And the improved model takes the interaction among the crowd and the influences exerted by the evacuating personnel upon the environment into account. The new model cares more specific details of both environment and the personnel, so it simulates the crowd psychology successfully and provides a more reliable theory that is to expand and improve the cellular automaton simulation model on personnel evacuation.

Quantum-Inspired Automatic Clustering Technique Using Ant Colony Optimization Algorithm

2018 ◽  
pp. 27-54 ◽  
Author(s):  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Ujjwal Maulik
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
Real Life ◽  
Optimal Number ◽  
Ant Colony ◽  
Data Sets ◽  
Efficient Computation ◽  
Ant Colony Optimization Algorithm ◽  
Gray Scale ◽  
Automatic Clustering

Quantum computing has emerged as the most challenging field of research in efficient computation. This chapter introduces a novel quantum-inspired ant colony optimization technique for automatic clustering. This chapter presents an application of this proposed technique to the automatic clustering of real-life gray-scale image data sets. In contrary to the other techniques, the proposed one requires no previous knowledge of the data to be classified. It finds the optimal number of clusters of the data by itself. The Xie-Beni cluster validity measure has been employed as the objective function for clustering purpose. Effectiveness of the proposed technique is exhibited on four real-life gray-scale images. Superiority of the proposed technique is established over its counterpart with respect to various aspects, which include accuracy, stability, computational time and standard errors. Finally, a statistical supremacy test, called unpaired two-tailed t-test, is conducted between them. It shows that superiority in favor of the proposed technique is established.

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.

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara)

2019 ◽  
Vol 9 (2) ◽  
pp. 79-85
Author(s):  
Indah Noviasari ◽  
Andre Rusli ◽  
Seng Hansun
Keyword(s):  
Ant Colony Optimization ◽  
Educational Institution ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Ant Colony ◽  
Scheduling Problems ◽  
Perceived Ease Of Use ◽  
Ant Colony Optimization Algorithm ◽  
Scheduling System ◽  
Course Scheduling

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

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