Automated Selection of Appropriate Pheromone Representations in Ant Colony Optimization

2005 ◽  
Vol 11 (3) ◽  
pp. 269-291 ◽  
Author(s):  
James Montgomery ◽  
Marcus Randall ◽  
Tim Hendtlass
Keyword(s):  
Ant Colony Optimization ◽  
Search Space ◽  
Ant Colony ◽  
Effective Size ◽  
Problem Model ◽  
Trail Pheromones ◽  
Wide Range ◽  
Representation Of Solutions ◽  
Ant Trail ◽  
Selection Of

Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this article, we present a novel system for automatically generating appropriate pheromone representations, based on the characteristics of the problem model that ensures unique pheromone representation of solutions. This is the first stage in the development of a generalized ACO system that could be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.

Implementasi Algoritma MAX-MIN Ant System pada Penjadwalan Mata Kuliah

2013 ◽  
Vol 5 (2) ◽  
pp. 48-53
Author(s):  
William Aprilius ◽  
Lorentzo Augustino ◽  
Ong Yeremia M. H.
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Ant System ◽  
University Course Timetabling ◽  
Alternative Approach ◽  
Ant System Algorithm ◽  
Index Terms ◽  
Course Timetabling Problem ◽  
University Course ◽  
Selection Of

University Course Timetabling Problem is a problem faced by every university, one of which is Universitas Multimedia Nusantara. Timetabling process is done by allocating time and space so that the whole associated class and course can be implemented. In this paper, the problem will be solved by using MAX-MIN Ant System Algorithm. This algorithm is an alternative approach to ant colony optimization. This algorithm uses two tables of pheromones as stigmergy, i.e. timeslot pheromone table and room pheromone table. In addition, the selection of timeslot and room is done by using the standard deviation of the value of pheromones. Testing is carried out by using 105 events, 45 timeslots, and 3 types of categories based on the number of rooms provided, i.e. large, medium, and small. In each category, testing is performed 5 times and for each testing, the data recorded is the unplace and Soft Constraint Penalty. In general, the greater the number of rooms, the smaller the unplace. Index Terms—ant colony optimization, max-min ant system, timetabling

Hybrid ant colony optimization for capacitated multiple-allocation cluster hub location problem

2017 ◽  
Vol 32 (1) ◽  
pp. 44-58 ◽  
Author(s):  
Mohammad Mirabi ◽  
Parya Seddighi
Keyword(s):  
Ant Colony Optimization ◽  
Location Problem ◽  
Demand Management ◽  
Telecommunication Networks ◽  
Ant Colony ◽  
Hub Location ◽  
Hub Location Problem ◽  
Total Cost ◽  
Wide Range ◽  
Cost Of Transportation

AbstractThe hub location problems involve locating facilities and designing hub networks to minimize the total cost of transportation (as a function of distance) between hubs, establishing facilities and demand management. In this paper, we consider the capacitated cluster hub location problem because of its wide range of applications in real-world cases, especially in transportation and telecommunication networks. In this regard, a mathematical model is presented to address this problem under capacity constraints imposed on hubs and transportation lines. Then, a new hybrid algorithm based on simulated annealing and ant colony optimization is proposed to solve the presented problem. Finally, the computational experiments demonstrate that the proposed heuristic algorithm is both effective and efficient.

scholarly journals Mobile robot path planning using an improved ant colony optimization

2018 ◽  
Vol 15 (3) ◽  
pp. 172988141877467 ◽  
Author(s):  
Khaled Akka ◽  
Farid Khaber
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Algorithm ◽  
Search Space ◽  
Step Length ◽  
Ant Colony ◽  
Dynamic Adjustment ◽  
Feedback Information ◽  
Intelligent Optimization Algorithm ◽  
Selection Of

Ant colony algorithm is an intelligent optimization algorithm that is widely used in path planning for mobile robot due to its advantages, such as good feedback information, strong robustness and better distributed computing. However, it has some problems such as the slow convergence and the prematurity. This article introduces an improved ant colony algorithm that uses a stimulating probability to help the ant in its selection of the next grid and employs new heuristic information based on the principle of unlimited step length to expand the vision field and to increase the visibility accuracy; and also the improved algorithm adopts new pheromone updating rule and dynamic adjustment of the evaporation rate to accelerate the convergence speed and to enlarge the search space. Simulation results prove that the proposed algorithm overcomes the shortcomings of the conventional algorithms.

Quantifying Ordinal Performance of Ant Colony Optimization

2013 ◽  
Vol 378 ◽  
pp. 387-393
Author(s):  
Zhao Jun Zhang ◽  
Zu Ren Feng
Keyword(s):  
Ant Colony Optimization ◽  
Quality Evaluation ◽  
Feasible Solution ◽  
Theoretical Foundation ◽  
Search Space ◽  
Ant Colony ◽  
Analysis Method ◽  
Solution Quality ◽  
Intelligent Optimization Algorithms ◽  
Feasible Solutions

In contrast to many successful applications of ant colony optimization, the theoretical foundation is rather weak. It greatly limits the application in practical problems. One problem, called solution quality evaluation, is how to quantify the performance of the algorithm. It is hardly solved by theoretical methods. Experimental analysis method based on the analysis of search space and characteristic of algorithm itself is proposed in this paper. As algorithm runs, it would produce a large number of feasible solutions. After preprocessing, they were clustered according to distance. Then, good enough set was partitioned by the results of clustering. Last, evaluation result of ordinal performance was got by using relative knowledge of statistics. As the method only uses feasible solution produced by optimization algorithm, it is independent to specific algorithm. Therefore, the proposed method can be adopted by other intelligent optimization algorithms. The method is demonstrated through traveling salesman problem.

Optimal Selection of Parameters for Nonuniform Embedding of Chaotic Time Series Using Ant Colony Optimization

2013 ◽  
Vol 43 (2) ◽  
pp. 790-802 ◽  
Author(s):  
Meie Shen ◽  
Wei-Neng Chen ◽  
Jun Zhang ◽  
Henry Shu-Hung Chung ◽  
O. Kaynak
Keyword(s):  
Time Series ◽  
Ant Colony Optimization ◽  
Chaotic Time Series ◽  
Ant Colony ◽  
Optimal Selection ◽  
Selection Of

An Improved Ant Colony Optimization Supervised by PSO

2010 ◽  
Vol 108-111 ◽  
pp. 1354-1359
Author(s):  
Zhi Gang Zhou
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Search Space ◽  
Ant Colony ◽  
Ant Colony System ◽  
Control Parameters ◽  
Evaporation Coefficient ◽  
The Core ◽  
Stochastic Selection

Combined with the idea of the particle swarm optimization (PSO) algorithm, the ant colony optimization (ACO) algorithm is presented to solve the well known traveling salesman problem (TSP). The core of this algorithm is using PSO to optimize the control parameters of ACO which consist of heuristic factor, pheromone evaporation coefficient and the threshold of stochastic selection, and applying ant colony system to routing. The new algorithm effectively overcomes the influence of control parameters of ACO and decreases the numbers of useless experiments, aiming to find the balance between exploiting the optimal solution and enlarging the search space.

The Planning of the Dynamic Path of the 3rd Party Logistics Center, which Based on the Ant Colony Optimization

2012 ◽  
Vol 253-255 ◽  
pp. 1472-1475
Author(s):  
Yue Ran Zhen ◽  
Jia Cheng Huang ◽  
Wen Yong Li ◽  
Fu Xiang Li ◽  
Yang Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Transportation Costs ◽  
Ant Colony ◽  
Third Party ◽  
Third Party Logistics ◽  
Transport Costs ◽  
Wide Range ◽  
Planning Methods ◽  
Dynamic Path

Logistics profits which is the third profit source, it has become a "black hole" of the profits of the entire supply chain. At all the logistics costs, the proportion of transport costs is approximately 35% to 60%, thus by through reducing transportation costs, can we improve the effectiveness of the logistics system, and finally to maximize profits. Dynamic path planning of third-party logistics centers undoubtedly reduces transportation costs and optimizes logistics management research priority. The article first introduces the TSP and VRP, both are classic path planning methods, and then through the basic idea and the basic principles of detailing the Ant colony optimization to illustrate the wide range of applications of it in third-party logistics.

scholarly journals Ant Colony Optimization (ACO) Based MANET Routing Protocols: A Comprehensive Review

2019 ◽  
Vol 12 (1) ◽  
pp. 82
Author(s):  
Ahmad Khader Habboush
Keyword(s):  
Ant Colony Optimization ◽  
Communication Networks ◽  
Ad Hoc ◽  
Data Communication ◽  
Vital Role ◽  
Ant Colony ◽  
Comprehensive Review ◽  
Wide Range ◽  
Soft Computing Techniques ◽  
Manet Routing

Application of mobile ad hoc networks (MANETs) has gained significant popularity among researchers in the field of data communication networks. However, a MANET operating in a wireless environment imposes a number of challenges for the implementers so far as routing of packets across it is concerned. There is a wide range of research contributions are available in the literature wherein authors propose various solutions to overcome the problems and bottleneck related to routing in MANET. Especially soft computing techniques and Ant Colony Optimization (ACO) in particular has been significantly popular among the researchers to resolve MANET routing issues. This technique plays a vital role in route discovery in particular. In this paper, we have conducted a comprehensive review of this technique applied to routing in MANET with respect to various criteria. Hopefully this paper serves to a perfect document for researchers in this field.

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