Abstract Ant Colony Optimization Research Articles

Application of ant colony optimization metaheuristic on set covering problems

Ant Colony Optimization (ACO) metaheuristic is a multi-agent system in which the behaviour of each ant is inspired by the foraging behaviour of real ants to solve optimization problem. Set Covering Problems (SCP), on the other hand, deal with maximizing the coverage of every subset while the weight nodes used must be minimized. In this paper, ACO was adapted and used to solve a case of Set Covering Problem. The adapted ACO for solving the SCP was implemented as a computer program using SciLab 5.4.1. The problem of determining the optimal location of Wi-Fi Access Points using the 802.11n protocol in the UP Los Banos Math Building was solved using this metaheuristic. Results show that in order to have 100% coverage of the MB, 7 access points are required. Methodology of the study can be adapted and results of the study can be used by decision makers on related optimization problems.

A novel approach to speed up ant colony algorithm via applying vertex coloring

Ant Colony Optimization (ACO) is a popular optimal algorithm, whose typical application is to solve Travel Salesman Problem (TSP). The time complexity of ACO is O(mNt), where the parameters m, N and t denote the number of ants, cities and iteration steps, respectively. Improving running speed is an important research focus, and reducing the parameter N and t is an effective way. Especially, reducing the parameter N is an efficient method. In this paper, we present a new clustering algorithm named Vertex Coloring Clustering (VCC), which uses the thought of vertex coloring to classify all cities into some classes and lets ACO act on each class to get local TSP routes, and joins these local routes as a whole route. Owing to Vertex Coloring has property that the number of color is the minimum, the minimum number of classes is obtained in this paper. Each class can be considered as a smaller TSP. So, when ACO algorithm calculate the local route on each class obtained by VCC algorithm, the parameter N and the iteration number t are reduced. The simulation shows that the improved ACO by VCC is faster than the ACO algorithm which uses algorithm Ant-Cycle by factors of 15-750.

Blind Watermarking Method Based on the Ant Colony

Ant colony optimization (ACO) is a cooperative search algorithm inspired by the behavior of real ants in nature. In this work, ACO is applied to solve the problem of extracting inserted data without recourse to the original image. Indeed, the heaviness of the extraction procedure of the watermark with the conventional watermarking methods has motivated the search for a new algorithm. The basic idea of the ACO approach is to use the pheromone trails as a medium for indirect communication to guide ants to the food source. This mechanism is used in this article, the ants are guided by the variation in pixel intensity values and their movement establishes a pheromone matrix that represents the hidden information. Our experimental results show the feasibility and success of the proposed method.

Ant Foraging Behavior for Job Shop Problem

Ant Colony Optimization (ACO) is a new algorithm approach, inspired by the foraging behavior of real ants. It has frequently been applied to many optimization problems and one such problem is in solving the job shop problem (JSP). The JSP is a finite set of jobs processed on a finite set of machine where once a job initiates processing on a given machine, it must complete processing and uninterrupted. In solving the Job Shop Scheduling problem, the process is measure by the amount of time required in completing a job known as a makespan and minimizing the makespan is the main objective of this study. In this paper, we developed an ACO algorithm to minimize the makespan. A real set of problems from a metal company in Johor bahru, producing 20 parts with jobs involving the process of clinching, tapping and power press respectively. The result from this study shows that the proposed ACO heuristics managed to produce a god result in a short time.

Image Edge Detection Based on Ant Colony Optimization Algorithm

Ant colony optimization (ACO) is a new heuristic algorithm which has been proven a successful technique. The article applies the ACO to the image edge detection, get edge image edge according to different neighborhood access policy through MATLAB simulation, and use the best neighborhood strategy to get detection. Compared with the traditional edge detection methods, the algorithm can effectively suppress the noise interference, retain most of the effective information of the image.

Approximation performance of ant colony optimization for the TSP(1,2) problem

Ant colony optimization (ACO) is a kind of powerful and popular randomized search heuristic for solving combinatorial optimization problems. This paper investigates the performance of two ACO algorithms, called and , on the travelling salesman problem with distance one and two (TSP(1,2)) which is an NP-complete problem. It is shown that two ACO algorithms obtain an approximation ratio of with regard to the optimal solutions in expected polynomial runtimes. We also study the influence of pheromone information and heuristic information on the approximation performance. Finally, we construct an instance and demonstrate that ACO outperforms the local search algorithms on this instance.

A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the “original” pheromone table used to keep track of thepromisinginformation, a second pheromone table is added to the proposed algorithm to keep track of theunpromisinginformation so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.

Leafcutter Ant Colony Optimization Algorithm for Feature Subset Selection on Classifying Digital Mammograms

Ant Colony Optimization (ACO) has been applied in wide range of applications. In ACO, for every iteration the entire problem space is considered for the solution construction using the probability of the pheromone deposits. After convergence, the global solution is made with the path which has highest pheromone deposit. In this paper, a novel solution construction technique has been proposed to reduce the time complexity and to improve the performance of the ACO. The idea is derived from the behavior of a special ant species called ‘Leafcutter Ants', they spend much of their time for cutting leaves to make fertilizer to gardens in which they grow the fungi that they eat. This behavior is incorporated with the general ACO algorithm to propose a novel feature selection method called ‘Leafcutter Ant Colony Optimization' (LACO) algorithm. The LACO has been applied to select the relevant features for digital mammograms and their corresponding classification performance is studied and compared.

Applying Data Clustering Feature to Speed Up Ant Colony Optimization

Ant colony optimization (ACO) is often used to solve optimization problems, such as traveling salesman problem (TSP). When it is applied to TSP, its runtime is proportional to the squared size of problemNso as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data sizeNbecomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.

Segmentation and Edge Detection Based on Modified ant Colony Optimization for Iris Image Processing

Abstract Ant colony optimization (stocktickerACO) is a meta-heuristic algorithm inspired by food searching behavior of real ants. Recently stocktickerACO has been widely used in digital image processing. When artificial ants move in a discrete habitat like an image, they deposit pheromone in their prior position. Simultaneously, vaporizing of pheromone in each iteration step avoids from falling in the local minima trap. Iris recognition because of its great dependability and non-invasion has various applications. simulation results demonstrate stocktickerACO algorithm can effectively extract the iris texture. Also it is not sensitive to nuisance factors. Moreover, stocktickerACO in this research preserves details of the various synthetic and real images. Performance of ACO in iris segmentation is compared with operation of traditional approaches such as canny, robert, and sobel edge detections. Experimental results reveal high quality and quite promising of stocktickerACO to segment images with irregular and complex structures.

Parallelized ACO based Routing for Stagnation Mitigation and Avoidance of Multimedia Traffic Over MANETs

Ant Colony Optimization (ACO) (Deepalakshmi & Radhakrishnan, 2009; Sharma & Kotecha, 2011; Sharma, Karkhanawala, & Kotecha, 2011) is a meta-heuristic, suitable for optimized solutions to routing problem in Mobile Adhoc Networks (MANETs). ACO based algorithms are fully distributed, self-organizing, fault tolerant, and intrinsically adapts to changing traffic patterns. However, if the best path is preferred for routing over longer period of time, the exploratory behaviour of the ants may be affected, thus leading to stagnation of the best paths. The authors have reviewed various techniques used for stagnation control and avoidance (Li, Ma, & Cao, 2005). These include, Pheromone control (Schoonderwoerd, Holland, Bruten, & Rothkrantz, 1996; Wedde & Farooq, 2006; De Rango & Socievole, 2011), Pheromone heuristics control (Sim & Sun, 2003), Privileged pheromone laying (Wedde & Farooq, 2006; Stuzle & Hoos, 2000), Multiple ant colony optimization (De Rango & Socievole, 2011; Sim & Sun, 2003), and Multiple path routing (Upadhyaya & Setiya, 2009b) techniques. They also present a comparative analysis of these schemes with respect to the parameter on which they depend for stagnation avoidance. The paper also focuses on stagnation leading to losses of data, which can lay a drastic effect on the quality of multimedia transmission. The authors propose a scheme to improve the exploratory behaviour of ants, by paralleled release of two streams of forward ants in each iteration, along the path from source to destination. It is mentioned that the technique will improve the quality of multimedia routed through MANETs, due to the multipath based enhancements.

Hybrid Ant Algorithm and Applications for Vehicle Routing Problem

Abstract Ant colony optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. ACO has been successfully applied to several combinatorial optimization problems, but it has some short-comings like its slow computing speed and local-convergence. For solving Vehicle Routing Problem, we proposed Hybrid Ant Algorithm (HAA) in order to improve both the performance of the algorithm and the quality of solutions. The proposed algorithm took the advantages of Nearest Neighbor (NN) heuristic and ACO for solving VRP, it also expanded the scope of solution space and improves the global ability of the algorithm through importing mutation operation, combining 2-opt heuristics and adjusting the configuration of parameters dynamically. Computational results indicate that the hybrid ant algorithm can get optimal resolution of VRP effectively.

LED Sorting Path Optimization Based on Ant Colony Optimization

Ant colony optimization(ACO) is used to optimize LED sorting path.Block moving tactic of sorting are analyzed.The LED diaphragm's deformation is reduced.The fully connected undirected graph of sorting path is established and sorting time sequence and sorting path tactic are modeled and optimized by means of ACO.The number of ants,complexity of ant colony algorithm and factor of pheromone volatilizing are optimized.Experiments show that ACO is efficient in LED sorting,compared with the traditional one.An efficient method is offered for LED chip sorting path scheduling.

A statistical analysis of parameter values for the rank-based ant colony optimization algorithm for the traveling salesperson problem

Ant colony optimization (ACO) is a metaheuristic for solving combinatorial optimization problems that is based on the foraging behavior of biological ant colonies. Starting with the 1996 seminal paper by Dorigo, Maniezzo and Colorni, ACO techniques have been used to solve the traveling salesperson problem (TSP). In this paper, we focus on a particular type of the ACO algorithm, namely, the rank-based ACO algorithm for the TSP. In particular, this paper identifies an optimal set of key parameters by statistical analysis applied to results of the rank-based ACO for the TSP. Specifically, for six frequently used TSPs available on the World Wide Web, we will solve a total of 27 000 instances for each problem.

Data Query Protocol Based on Ant Colony Optimization for Wireless Sensor Networks

ACO (ant colony optimization) can find the optimal path from source nodes to sink nodes in data query for wireless sensor networks. But once all query tasks and query results are transmitted along the path, the energy of the path will be exhausted. So this paper puts forward an Energy Balance Data Query Protocol based on Ant Colony Optimization (EBDQ). This protocol rewards or punishes a path according to the energy consumption by pheromone decentralize energy consumption into different paths, and reposefully demotes the energy consumption of the whole network. Both theoretical analysis and simulation results show that EBDQ can prolong the lifetime and reduce the delay of the network.

First Order Deceptive Problem of ACO and Its Performance Analysis

Ant colony optimization(ACO), which is one of the intelligential optimization algorithm, has been widely used to solve combinational optimization problems. Deceptive problems have been considered difficult for ant colony optimization. It was believed that ACO will fail to converge to global optima of deceptive problems. This paper proves that the first order deceptive problem of ant colony algorithm satisfies value convergence under certain initial pheromone distribution, but does not satisfy solution convergence. We also present a first attempt towards the value-convergence time complexity analysis of ACO on the first-order deceptive systems taking the n -bit trap problem as the test instance. We prove that time complexity of MMAS, which is an ACO with limitations of the pheromone on each edge, on n -bit trap problem is O( n 2 m. log n ), here n is the size of the problem and m is the number of artificial ants. Our experimental results confirm the correctness of our analysis.

An Improved Ant Algorithm for Multidimensional Knapsack Problem

Ant Colony Optimization(ACO)is a metaheuristic.And it has been applied to many hard discrete optimization problems.Recently,some researchers have proposed several different ACO algorithms to solve the multidimensional knapsack problem(MKP),which is an NP-hard combinatorial optimization problem.Although these algorithms could obtain good solutions of MKP,they consume too more CPU time.For the sake of decreasing the time complexity of the existing ACO algorithms,a new improved ACO algorithm is proposed for MKP in this paper.First,a pheromone representation,which was defined by Blum C.in his paper "the hyper-cube framework for ant colony optimization" as an example,models binary decision variables assigned to all items and their values.Based on this pheromone model,a new rule choosing the objective item is defined.In this way,the time complexity of the proposed ACO algorithm can be decreased.However,incorporating the heuristic information into the solution construction of the artificial ants is difficult.A new heuristic information of MKP based on some order of all items is described.Experimental results show that in the case of the same computing task,the new one uses less CPU time and obtains better solutions compared with other ACO algorithms whether the serial or parallel implementation.Also,the new algorithm has obtained two new solutions of test 5.250-22.

Multiple colony ant algorithm for job-shop scheduling problem

Ant colony optimization (ACO) is a metaheuristic that takes inspiration from the foraging behaviour of a real ant colony to solve the optimization problem. This paper presents a multiple colony ant algorithm to solve the Job-shop Scheduling Problem with the objective that minimizes the makespan. In a multiple colony ant algorithm, ants cooperate to find good solutions by exchanging information among colonies which are stored in a master pheromone matrix that serves the role of global memory. The exploration of the search space in each colony is guided by different heuristic information. Several specific features are introduced in the algorithm in order to improve the efficiency of the search. Among others is the local search method by which the ant can fine-tune their neighbourhood solutions. The proposed algorithm is tested over set of benchmark problems and the computational results demonstrate that the multiple colony ant algorithm performs well on the benchmark problems.

ROUTING IN OPTICAL MULTISTAGE NETWORKS WITH LIMITED CROSSTALK USING ANT COLONY OPTIMIZATION

Ant Colony Optimization (ACO) techniques can be successfully implemented to solve many combinatorial optimization problems. After the traveling salesman problem was successfully solved using the ACO technique, other researchers have concentrated on solving other problems like the quadratic assignment and the job-shop scheduling problems using the same technique. In this paper we use the ACO technique to route messages through an N × N Optical Multistage Interconnection Network (OMIN) allowing upto ' C ' limited crosstalk's (conflicts between messages within a switch) where ' C ' is a technology driven parameter and is always less than log 2 N . Messages with switch conflicts satisfying the crosstalk constraint are allowed to pass in the same group, but if there is any link conflict, then messages have to be routed in a different group. The focus is to minimize the number of passes required for routing allowing upto ' C ' limited crosstalks in an N × N optical network. This routing problem is an NP-hard problem. In this paper we show how the ACO technique can be applied to the routing problem and compare the performance of the ACO technique to that of the degree-descending algorithm using simulation techniques. Finally the lower bound estimate on the minimum number of passes required is calculated and compared to the results obtained using the two algorithms discussed. The results obtained show that the ACO technique performs better than the degree-descending algorithm and is quite close to optimal algorithms to the problem.

ANT COLONY OPTIMIZATION WITH GLOBAL PHEROMONE EVALUATION FOR SCHEDULING A SINGLE MACHINE

Ant Colony Optimization (ACO) is a metaheuristic that has recently been applied to scheduling problems. We propose an ACO algorithm for the Single Machine Total Weighted Tardiness Problem and compare it to an existing ACO algorithm for the unweighted problem. The proposed algorithm has some novel properties that are of general interest for ACO optimization. A main novelty is that the ants are guided on their way through the decision space by global pheromone information instead of using only local pheromone information. It is also shown that the ACO optimization behaviour can be improved when priority scheduling heuristics are adapted so that they appropriately reflect absolute quality differences between the alternatives before they are used by the ants. Further improvements can be obtained by identifying situations where the ants can perform optimal decisions.