scholarly journals Solving Random Travelling Salesman Problem using Firefly Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1037-1041
Keyword(s):  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Firefly Algorithm ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Ant Colony ◽  
Ant Colony Algorithms ◽  
Comparison Of The Results ◽  
Discrete Optimization Problems

The firefly algorithm is a recently developed optimization algorithm, which is suitable for solving any kind of discrete optimization problems. This is an algorithm inspired from the nature. In this paper, a firefly algorithm is proposed to solve random traveling salesman problem. The solution to this problem is already proposed by the algorithms like simulated annealing, genetic algorithms and ant colony algorithms. This algorithm is developed to deal with the issue of accuracy and convergence rate in the solutions provided by those algorithms. A comparison of the results produced by proposed algorithm with the results of simulated annealing, genetic algorithms and ant colony algorithm is given. Finally, the effectiveness of the proposed algorithm is discussed.

Solution of Travelling Salesman Problem Using Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1206-1212
Author(s):  
Sevda Dayıoğlu Gülcü ◽  
Şaban Gülcü ◽  
Humar Kahramanli
Keyword(s):  
Discrete Optimization ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Heuristic Method ◽  
Ant Colony ◽  
Ant Colony System ◽  
Travelling Salesman ◽  
Ant Colonies ◽  
Discrete Optimization Problems

Recently some studies have been revealed by inspiring from animals which live as colonies in the nature. Ant Colony System is one of these studies. This system is a meta-heuristic method which has been developed based upon food searching characteristics of the ant colonies. Ant Colony System is applied in a lot of discrete optimization problems such as travelling salesman problem. In this study solving the travelling salesman problem using ant colony system is aimed.

Intelligent selective disassembly using the ant colony algorithm

2003 ◽  
Vol 17 (4) ◽  
pp. 325-333 ◽  
Author(s):  
J.F. WANG ◽  
J.H. LIU ◽  
S.Q. LI ◽  
Y.F. ZHONG
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Solution Space ◽  
Ant Colony ◽  
Environmentally Conscious ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Selective Disassembly ◽  
Disassembly Matrix

Selective disassembly is an important issue in industrial and mechanical engineering for environmentally conscious manufacturing. This paper presents an intelligent selective disassembly approach based on ant colony algorithms, which take inspiration from the behavior of real ant colonies and are used to solve combinatorial optimization problems. For diverse assemblies, the algorithm generates different amounts of ants cooperating to find disassembly sequences for selected components, minimizing the reorientation of assemblies and removal of components. A candidate list that is composed of feasible disassembly operations, which are derived from a disassembly matrix of products, guides sequence construction in the implicit solution space and ensures the geometric feasibility of sequences. Preliminary implementation results show the effectiveness of the proposed method.

scholarly journals Network Intrusion Detection using a Combination of Fuzzy Clustering and Ant Colony Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 11-19
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Intrusion Detection ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Detection System ◽  
Ant Colony ◽  
Detection Accuracy ◽  
Ant Colony Optimization Algorithm ◽  
Network Intrusion ◽  
Secure Network ◽  
Main Component

As information technology grows, network security is a significant issue and challenge. The intrusion detection system (IDS) is known as the main component of a secure network. An IDS can be considered a set of tools to help identify and report abnormal activities in the network. In this study, we use data mining of a new framework using fuzzy tools and combine it with the ant colony optimization algorithm (ACOR) to overcome the shortcomings of the k-means clustering method and improve detection accuracy in IDSs. Introduced IDS. The ACOR algorithm is recognized as a fast and accurate meta-method for optimization problems. We combine the improved ACOR with the fuzzy c-means algorithm to achieve efficient clustering and intrusion detection. Our proposed hybrid algorithm is reviewed with the NSL-KDD dataset and the ISCX 2012 dataset using various criteria. For further evaluation, our method is compared to other tasks, and the results are compared show that the proposed algorithm has performed better in all cases.

scholarly journals Optimized Resource Allocation Algorithm for Crowd-Creation Space Computing Based on Cloud Computing Environment

2021 ◽  
pp. 08-25
Author(s):  
Mustafa El .. ◽  
◽  
◽  
Aaras Y Y.Kraidi
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Simulated Annealing ◽  
Task Allocation ◽  
Ant Colony Algorithm ◽  
Simulated Annealing Algorithm ◽  
Ant Colony ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Resource Allocation Algorithm

The crowd-creation space is a manifestation of the development of innovation theory to a certain stage. With the creation of the crowd-creation space, the problem of optimizing the resource allocation of the crowd-creation space has become a research hotspot. The emergence of cloud computing provides a new idea for solving the problem of resource allocation. Common cloud computing resource allocation algorithms include genetic algorithms, simulated annealing algorithms, and ant colony algorithms. These algorithms have their obvious shortcomings, which are not conducive to solving the problem of optimal resource allocation for crowd-creation space computing. Based on this, this paper proposes an In the cloud computing environment, the algorithm for optimizing resource allocation for crowd-creation space computing adopts a combination of genetic algorithm and ant colony algorithm and optimizes it by citing some mechanisms of simulated annealing algorithm. The algorithm in this paper is an improved genetic ant colony algorithm (HGAACO). In this paper, the feasibility of the algorithm is verified through experiments. The experimental results show that with 20 tasks, the ant colony algorithm task allocation time is 93ms, the genetic ant colony algorithm time is 90ms, and the improved algorithm task allocation time proposed in this paper is 74ms, obviously superior. The algorithm proposed in this paper has a certain reference value for solving the creative space computing optimization resource allocation.

Logistics for the Garbage Collection through the use of Ant Colony Algorithms

2012 ◽  
pp. 33-51
Author(s):  
Julio Cesar Ponce Gallegos ◽  
Fatima Sayuri Quezada Aguilera ◽  
José Alberto Hernandez Aguilar ◽  
Christian José Correa Villalón
Keyword(s):  
Waste Management ◽  
Garbage Collection ◽  
Optimization Problems ◽  
Decision Makers ◽  
Ant Colony ◽  
Ant Colony System ◽  
Waste Collection ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Acs Algorithm

The contribution of this chapter is to present an approach to explain the Ant Colony System applied on the Waste Collection Problem, because waste management is moving up to the concern over health and environmental impacts. These algorithms are a framework for decision makers in order to analyze and simulate various spatial waste management problems. In the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present work, an individual metaheuristic Ant Colony System (ACS) algorithm is introduced, implemented and discussed for the identification of optimal routes in the case Solid Waste collection. This algorithm is applied to a waste collection and transport system, obtaining recollection routes with the less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach.

Ant Colony Algorithms for Steiner Trees

2011 ◽  
pp. 1551-1575 ◽  
Author(s):  
Gurdip Singh ◽  
Sanjoy Das ◽  
Shekhar V. Gosavi ◽  
Sandeep Pujar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Steiner Trees ◽  
Ant Colony ◽  
Wireless Sensor ◽  
Graph Theoretic ◽  
Ant Colony Algorithms ◽  
Complex Optimization

This chapter introduces ant colony optimization as a method for computing minimum Steiner trees in graphs. Tree computation is achieved when multiple ants, starting out from different nodes in the graph, move towards one another and ultimately merge into a single entity. A distributed version of the proposed algorithm is also described, which is applied to the specific problem of data-centric routing in wireless sensor networks. This research illustrates how tree based graph theoretic computations can be accomplished by means of purely local ant interaction. The authors hope that this work will demonstrate how innovative ways to carry out ant interactions can be used to design effective ant colony algorithms for complex optimization problems.

scholarly journals Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 884
Author(s):  
Petr Stodola ◽  
Karel Michenka ◽  
Jan Nohel ◽  
Marian Rybanský
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Dynamic Environment ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Local Optimum ◽  
Use Of Knowledge

The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).

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