Showing papers on "Ant colony optimization algorithms published in 2011"
01 Jan 2011
TL;DR: In this article, a new ACO model that overcomes the difficulties found when working with a huge construction graph is presented. But it is not suitable when the graph size can be a challenge for the computer memory and cannot be completely generated or stored in it.
Abstract: Ant Colony Optimization (ACO) has been successfully applied to those combinatorial optimization problems which can be translated into a graph exploration. Artificial ants build solutions step by step adding solution components that are represented by graph nodes. The existing ACO algorithms are suitable when the graph is not very large (thousands of nodes) but is not useful when the graph size can be a challenge for the computer memory and cannot be completely generated or stored in it. In this paper we study a new ACO model that overcomes the difficulties found when working with a huge construction graph. In addition to the description of the model, we analyze in the experimental section one technique used for dealing with this huge graph exploration. The results of the analysis can help to understand the meaning of the new parameters introduced and to decide which parameterization is more suitable for a given problem. For the experiments we use one real problem with capital importance in Software Engineering: refutation of safety properties in concurrent systems. This way, we foster an innovative research line related to the application of ACO to formal methods in Software Engineering.
1,323 citations
01 Sep 2011
TL;DR: A survey of some of the most important lines of hybridization of metaheuristics with other techniques for optimization, which includes, for example, the combination of exact algorithms and meta heuristics.
Abstract: Research in metaheuristics for combinatorial optimization problems has lately experienced a noteworthy shift towards the hybridization of metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problem-oriented. Nowadays the focus is on solving the problem at hand in the best way possible, rather than promoting a certain metaheuristic. This has led to an enormously fruitful cross-fertilization of different areas of optimization. This cross-fertilization is documented by a multitude of powerful hybrid algorithms that were obtained by combining components from several different optimization techniques. Hereby, hybridization is not restricted to the combination of different metaheuristics but includes, for example, the combination of exact algorithms and metaheuristics. In this work we provide a survey of some of the most important lines of hybridization. The literature review is accompanied by the presentation of illustrative examples.
684 citations
21 Sep 2011
TL;DR: This work model the workload consolidation problem as an instance of the multi-dimensional bin-packing (MDBP) problem and design a novel, nature-inspired workload consolidation algorithm based on the Ant Colony Optimization (ACO), which outperforms the evaluated greedy algorithm.
Abstract: With increasing numbers of energy hungry data centers energy conservation has now become a major design constraint. One traditional approach to conserve energy in virtualized data centers is to perform workload (i.e., VM) consolidation. Thereby, workload is packed on the least number of physical machines and over-provisioned resources are transitioned into a lower power state. However, most of the workload consolidation approaches applied until now are limited to a single resource (e.g., CPU) and rely on simple greedy algorithms such as First-Fit Decreasing (FFD), which perform resource-dissipative workload placement. Moreover, they are highly centralized and known to be hard to distribute. In this work, we model the workload consolidation problem as an instance of the multi-dimensional bin-packing (MDBP) problem and design a novel, nature-inspired workload consolidation algorithm based on the Ant Colony Optimization (ACO). We evaluate the ACO-based approach by comparing it with one frequently applied greedy algorithm (i.e., FFD). Our simulation results demonstrate that ACO outperforms the evaluated greedy algorithm as it achieves superior energy gains through better server utilization and requires less machines. Moreover, it computes solutions which are nearly optimal. Finally, the autonomous nature of the approach allows it to be implemented in a fully distributed environment.
311 citations
TL;DR: This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining, and provides a unifying framework that categorizes the swarm intelligence based data mining algorithms into two approaches: effective search and data organizing.
Abstract: This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). These are also the most popular swarm intelligence metaheuristics for data mining. In addition to an overview of these nature inspired computing methodologies, we discuss popular data mining techniques based on these principles and schematically list the main differences in our literature tables. Further, we provide a unifying framework that categorizes the swarm intelligence based data mining algorithms into two approaches: effective search and data organizing. Finally, we list interesting issues for future research, hereby identifying methodological gaps in current research as well as mapping opportunities provided by swarm intelligence to current challenges within data mining research.
230 citations
TL;DR: The experimental results show that both the average solution and the percentage deviation of theaverage solution to the best known solution of the proposed method are better than the methods of Angeniol et al. (1988), Somhom et al ( 1988), Masutti and Castro (2009) and Pasti andCastle (2006).
Abstract: In this paper, we present a new method, called the genetic simulated annealing ant colony system with particle swarm optimization techniques, for solving the traveling salesman problem. We also make experiments using the 25 data sets obtained from the TSPLIB (http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) and compare the experimental results of the proposed method with the methods of Angeniol, Vaubois, and Texier (1988), Somhom, Modares, and Enkawa (1997), Masutti and Castro (2009) and Pasti and Castro (2006). The experimental results show that both the average solution and the percentage deviation of the average solution to the best known solution of the proposed method are better than the methods of Angeniol et al. (1988), Somhom et al. (1997), Masutti and Castro (2009) and Pasti and Castro (2006).
221 citations
Posted Content•
TL;DR: An overview of nature-inspired metaheuristic algorithms, from a brief history to their applications, to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA).
Abstract: Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.
204 citations
01 Dec 2011
TL;DR: A new taxonomy for classifying software-based parallel ACO algorithms is introduced and a systematic and comprehensive survey of the current state-of-the-art on Parallel ACO implementations is presented.
Abstract: Ant colony optimization (ACO) is a well-known swarm intelligence method, inspired in the social behavior of ant colonies for solving optimization problems. When facing large and complex problem instances, parallel computing techniques are usually applied to improve the efficiency, allowing ACO algorithms to achieve high quality results in reasonable execution times, even when tackling hard-to-solve optimization problems. This work introduces a new taxonomy for classifying software-based parallel ACO algorithms and also presents a systematic and comprehensive survey of the current state-of-the-art on parallel ACO implementations. Each parallel model reviewed is categorized in the new taxonomy proposed, and an insight on trends and perspectives in the field of parallel ACO implementations is provided.
198 citations
TL;DR: The modified ABC algorithm is described, which is a meta-heuristic optimization technique that mimics the process of food foraging of honeybees that is very effective and robust for the discrete optimization designs of truss structural problems.
Abstract: Over the past few years, swarm intelligence based optimization techniques such as ant colony optimization and particle swarm optimization have received considerable attention from engineering researchers and practitioners. These algorithms have been used in the solution of various engineering problems. Recently, a relatively new swarm based optimization algorithm called the Artificial Bee Colony (ABC) algorithm has begun to attract interest from researchers to solve optimization problems. The aim of this study is to present an optimization algorithm based on the ABC algorithm for the discrete optimum design of truss structures. The ABC algorithm is a meta-heuristic optimization technique that mimics the process of food foraging of honeybees. Originally the ABC algorithm was developed for continuous function optimization problems. This paper describes the modifications made to the ABC algorithm in order to solve discrete optimization problems and to improve the algorithm's performance. In order to demonstrate the effectiveness of the modified algorithm, four structural problems with up to 582 truss members and 29 design variables were solved and the results were compared with those obtained using other well-known meta-heuristic search techniques. The results demonstrate that the ABC algorithm is very effective and robust for the discrete optimization designs of truss structural problems.
174 citations
TL;DR: The resulting Ant Colony System algorithm hybridized with insertion heuristics for the Time-Dependent Vehicle Routing Problem with Time Windows turns out to be competitive, matching or improving the best known results in several benchmark problems.
165 citations
15 Jun 2011
TL;DR: Simulation results show that this new Artificial Bee Colony algorithm called Combinatorial ABC for Traveling Salesman Problem can be used for combinatorial optimization problems.
Abstract: Traveling Salesman Problem is an important optimization issue of many fields such as transportation, logistics and semiconductor industries and it is about finding a Hamiltonian path with minimum cost. To solve this problem, many researchers have proposed different approaches including metaheuristic methods. Artificial Bee Colony algorithm is a well known swarm based optimization technique. In this paper we propose a new Artificial Bee Colony algorithm called Combinatorial ABC for Traveling Salesman Problem. Simulation results show that this Artificial Bee Colony algorithm can be used for combinatorial optimization problems.
164 citations
TL;DR: An improved ant colony optimization (IACO) to solve period vehicle routing problem with time windows (PVRPTW), in which the planning period is extended to several days and each customer must be served within a specified time window.
Abstract: This paper proposes an improved ant colony optimization (IACO) to solve period vehicle routing problem with time windows (PVRPTW), in which the planning period is extended to several days and each customer must be served within a specified time window. Multi-dimension pheromone matrix is used to accumulate heuristic information on different days. Two-crossover operations are introduced to improve the performance of the algorithm. The effectiveness of IACO is evaluated using a set of well-known benchmarks. Some of the results are better than the best-known solutions. Results also show the IACO seems to be a powerful tool for PVRPTW.
01 Jan 2011
TL;DR: This chapter presents results of an empirical study of the solution quality over computation time for Ant Colony System and MAX-MIN Ant System, two well-known ACO algorithms, and provides insights on the behaviour of the algorithms in dependence of fixed parameter settings.
Abstract: This chapter reviews the approaches that have been studied for the online adaptation of the parameters of ant colony optimization (ACO) algorithms, that is, the variation of parameter settings while solving an instance of a problem. We classify these approaches according to the main classes of online parameter-adaptation techniques. One conclusion of this review is that the available approaches do not exploit an in-depth understanding of the effect of individual parameters on the behavior of ACO algorithms. Therefore, this chapter also presents results of an empirical study of the solution quality over computation time for Ant Colony System and MAX-MIN Ant System, two well-known ACO algorithms. The first part of this study provides insights on the behaviour of the algorithms in dependence of fixed parameter settings. One conclusion is that the best fixed parameter settings of MAX-MIN Ant System depend strongly on the available computation time. The second part of the study uses these insights to propose simple, pre-scheduled parameter variations. Our experimental results show that such pre-scheduled parameter variations can dramatically improve the anytime performance of MAX-MIN Ant System.
TL;DR: An ant colony optimization based approach is developed to solve the bilevel model of a hierarchical production-distribution planning problem and uses ants to compute the routes of a feasible solution of the associated multi-depot vehicle route problem.
TL;DR: An improved ant colony optimization with coarse-grain parallel strategy, ant-weight strategy and mutation operation, is presented for the V-MDVRP.
Abstract: This paper presents a method for solving multi-depot vehicle routing problem (MDVRP). First, a virtual central depot is added to transfer MDVRP to the multi-depot vehicle routing problem with the virtual central depot (V-MDVRP), which is similar to a vehicle routing problem (VRP) with the virtual central depot as the origin. An improved ant colony optimization with coarse-grain parallel strategy, ant-weight strategy and mutation operation, is presented for the V-MDVRP. The computational results for 23 benchmark problems are reported and compared to those of other ant colony optimizations.
TL;DR: FCS-ANTMINER outperforms several famous and recent methods in classification accuracy for diabetes disease diagnosis and has new characteristics that make it different from the existing methods that have utilized the Ant Colony Optimization for classification tasks.
Abstract: Classification systems have been widely utilized in medical domain to explore patient's data and extract a predictive model. This model helps physicians to improve their prognosis, diagnosis or treatment planning procedures. The aim of this paper is to use an Ant Colony-based classification system to extract a set of fuzzy rules for diagnosis of diabetes disease, named FCS-ANTMINER. We will review some recent methods and describe a new and efficient approach that leads us to considerable results for diabetes disease classification problem. FCS-ANTMINER has new characteristics that make it different from the existing methods that have utilized the Ant Colony Optimization (ACO) for classification tasks. The obtained classification accuracy is 84.24% which reveals that FCS-ANTMINER outperforms several famous and recent methods in classification accuracy for diabetes disease diagnosis.
TL;DR: This paper proposes a new approach to solving the EEC problem using a novel Ant Colony Optimization (ACO) algorithm that uses three types of pheromones to find the solution efficiently, whereas conventional ACO algorithms use only one type of peromone.
Abstract: The Efficient-Energy Coverage (EEC) problem is an important issue when implementing Wireless Sensor Networks (WSNs) because of the need to limit energy use. In this paper, we propose a new approach to solving the EEC problem using a novel Ant Colony Optimization (ACO) algorithm. The proposed ACO algorithm has a unique characteristic that conventional ACO algorithms do not have. The proposed ACO algorithm (Three Pheromones ACO, TPACO) uses three types of pheromones to find the solution efficiently, whereas conventional ACO algorithms use only one type of pheromone. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI), and the other is used to form a sensor set that has as many sensors as an ant has selected the number of active sensors by using the former pheromone. The TPACO algorithm has another advantage in that the two user parameters of ACO algorithms are not used. We also introduce some techniques that lead to a more realistic approach to solving the EEC problem. The first technique is to utilize the probabilistic sensor detection model. The second method is to use different kinds of sensors, i.e., heterogeneous sensors in continuous space, not a grid-based discrete space. Simulation results show the effectiveness of our algorithm over other algorithms, in terms of the whole network lifetime.
TL;DR: This paper presents the SR-GCWS-CS probabilistic algorithm that combines Monte Carlo simulation with splitting techniques and the Clarke and Wright savings heuristic to find competitive quasi-optimal solutions to the Capacitated Vehicle Routing Problem (CVRP) in reasonable response times.
Abstract: This paper presents the SR-GCWS-CS probabilistic algorithm that combines Monte Carlo simulation with splitting techniques and the Clarke and Wright savings heuristic to find competitive quasi-optimal solutions to the Capacitated Vehicle Routing Problem (CVRP) in reasonable response times. The algorithm, which does not require complex fine-tuning processes, can be used as an alternative to other metaheuristics—such as Simulated Annealing, Tabu Search, Genetic Algorithms, Ant Colony Optimization or GRASP, which might be more difficult to implement and which might require non-trivial fine-tuning processes—when solving CVRP instances. As discussed in the paper, the probabilistic approach presented here aims to provide a relatively simple and yet flexible algorithm which benefits from: (a) the use of the geometric distribution to guide the random search process, and (b) efficient cache and splitting techniques that contribute to significantly reduce computational times. The algorithm is validated through a set of CVRP standard benchmarks and competitive results are obtained in all tested cases. Future work regarding the use of parallel programming to efficiently solve large-scale CVRP instances is discussed. Finally, it is important to notice that some of the principles of the approach presented here might serve as a base to develop similar algorithms for other routing and scheduling combinatorial problems.
Book•
20 Jan 2011
TL;DR: This handbook volume serves as a useful foundational as well as consolidatory state-of-art collection of articles in the field from various researchers around the globe, giving an in-depth flavor of what SI is capable of achieving.
Abstract: From nature, we observe swarming behavior in the form of ant colonies, bird flocking, animal herding, honey bees, swarming of bacteria, and many more. It is only in recent years that researchers have taken notice of such natural swarming systems as culmination of some form of innate collective intelligence, albeit swarm intelligence (SI) - a metaphor that inspires a myriad of computational problem-solving techniques. In computational intelligence, swarm-like algorithms have been successfully applied to solve many real-world problems in engineering and sciences. This handbook volume serves as a useful foundational as well as consolidatory state-of-art collection of articles in the field from various researchers around the globe. It has a rich collection of contributions pertaining to the theoretical and empirical study of single and multi-objective variants of swarm intelligence based algorithms like particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization algorithm (BFOA), honey bee social foraging algorithms, and harmony search (HS). With chapters describing various applications of SI techniques in real-world engineering problems, this handbook can be a valuable resource for researchers and practitioners, giving an in-depth flavor of what SI is capable of achieving.
01 Dec 2011
TL;DR: This article proposes a pheromone correction heuristic strategy that uses information about the best-found solution to exclude suspicious elements from it and improves pure ant colony optimization algorithm by avoiding early trapping in local convergence.
Abstract: The minimum weight vertex cover problem is an interesting and applicable NP-hard problem that has been investigated from many different aspects. The ant colony optimization metaheuristic is a relatively new technique that was successfully adjusted and applied to many hard combinatorial optimization problems, including the minimum weight vertex cover problem. Some kind of hybridization or exploitation of the knowledge about specific problem often greatly improves the performance of standard evolutionary algorithms. In this article we propose a pheromone correction heuristic strategy that uses information about the best-found solution to exclude suspicious elements from it. Elements are suspicious if they have some undesirable properties that make them unlikely members of the optimal solution. This hybridization improves pure ant colony optimization algorithm by avoiding early trapping in local convergence. We tested our algorithm on numerous test-cases that were used in the previous research of the same problem and our algorithm uniformly performed better, giving slightly better results in significantly shorter time.
05 May 2011
TL;DR: This paper intends to provide an overview of convergence and efficiency studies of metaheuristics, and try to provide a framework for analyzing meta heuristics in terms of convergenceand efficiency, which can form a basis for analyzing other algorithms.
Abstract: Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majority of metaheuristic algorithms, while efficiency analysis is equally challenging. In this paper, we intend to provide an overview of convergence and efficiency studies of metaheuristics, and try to provide a framework for analyzing metaheuristics in terms of convergence and efficiency. This can form a basis for analyzing other algorithms. We also outline some open questions as further research topics.
TL;DR: Endmember extraction based on ant colony algorithms can avoid some defects of N-FINDR, VCA and other algorithms, improve the representation of endmembers for all image pixels, decrease the average value of root-mean-square error, and therefore achieve better endmember extraction results than the N- FINDR and VCA algorithms.
Abstract: Spectral mixture analysis has been an important research topic in remote sensing applications, particularly for hyperspectral remote sensing data processing. On the basis of linear spectral mixture models, this paper applied directed and weighted graphs to describe the relationship between pixels. In particular, we transformed the endmember extraction problem in the decomposition of mixed pixels into an issue of optimization and built feasible solution space to evaluate the practical significance of the objective function, thereby establishing two ant colony optimization algorithms for endmember extraction. In addition to the detailed process of calculation, we also addressed the effects of different operating parameters on algorithm performance. Finally we designed two sets of simulation data experiments and one set of actual data experiments, and the results of those experiments prove that endmember extraction based on ant colony algorithms can avoid some defects of N-FINDR, VCA and other algorithms, improve the representation of endmembers for all image pixels, decrease the average value of root-mean-square error, and therefore achieve better endmember extraction results than the N-FINDR and VCA algorithms.
TL;DR: In this article, a detailed review of previous studies that employed metaheuristics to address problems/issues encountered in the life time of a construction or engineering project is presented, with the objectives to optimize cost and time through the efficient uses of constrained or unconstrained resources.
01 Jan 2011
TL;DR: A new optimal watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD) using multi-objective ant colony optimization (MOACO) is presented.
Abstract: In this paper, a new optimal watermarking scheme based on lifting wavelet transform (LWT) and singular value decomposition (SVD) using multi-objective ant colony optimization (MOACO) is presented. The singular values of the binary water- mark are embedded in a detail subband of host image. To achieve the highest possible robustness without losing watermark transparency, multiple scaling factors (MSF) are used instead of a single scaling factor (SSF). Determining the optimal values of the mul- tiple scaling factors (MSF) is a dicult problem. However, to determine these values, a multi-objective ant colony-based optimization method is used. Experimental results show much improved performances in terms of transparency and robustness for the proposed method compared to other watermarking schemes. Furthermore, the proposed scheme does not suer from the problem of high probability of false positive detections of the watermarks.
TL;DR: The performance of the proposed decision-making system shall assist the practitioners in the construction industry to deliver construction projects in a more efficient and effective manner, and thus construction costs could be reduced significantly.
TL;DR: In this article, a mixed integer programming model is presented to solve the balancing problem of the multi-manned assembly lines optimally, which minimizes the total number of workers on the line as the first objective and the number of opened multimanned workstations as the second one.
Abstract: In real-world assembly lines, that the size of the product is large (e.g., automotive industry), usually there are multi-manned workstations where a group of workers simultaneously perform different operations on the same individual product. This paper presents a mixed integer programming model to solve the balancing problem of the multi-manned assembly lines optimally. This model minimizes the total number of workers on the line as the first objective and the number of opened multi-manned workstations as the second one. Since this problem is well known as NP (nondeterministic polynomial-time)-hard, a heuristic approach based on the ant colony optimization approach is developed to solve the medium- and large-size scales of this problem. In the proposed algorithm, each ant tries to allocate given tasks to multi-manned workstations in order to build a balancing solution for the assembly line balancing problems by considering the precedence relations, multi-manned assembly line configuration, task times, and cycle time constraints. Through computational experiments, the performance of the proposed ACO is compared with some existing heuristic on various problem instances. The experimental results validate the effectiveness and efficiency of the proposed algorithm.
TL;DR: A new method, called the parallelized genetic ant colony system (PGACS), for solving the traveling salesman problem, which consists of the genetic algorithm, including the new crossover operations and the hybrid mutation operations, and the ant colony systems with communication strategies.
Abstract: In this paper, we present a new method, called the parallelized genetic ant colony system (PGACS), for solving the traveling salesman problem. It consists of the genetic algorithm, including the new crossover operations and the hybrid mutation operations, and the ant colony systems with communication strategies. We also make an experiment with three classical data sets got from the TSP library to test the performance of the proposed method. The experiment results show that the performance of the proposed method is better than Chu et al.'s method (2004).
TL;DR: The present ACO algorithm employs the concept of cooperative pheromone accumulation, which is typical of ACO selection methods, and optimizes PLS models using a pre-defined number of variables, employing a Monte Carlo approach to discard irrelevant sensors.
Journal Article•
01 Jan 2011
TL;DR: An ant system is designed to solve the fixed destination multi-depot multiple traveling salesmen problem and the results are compared to the answers obtained by solving the same problems by Lingo 8.0 which uses exact methods.
Abstract: The fixed destination multi-depot multiple traveling salesmen problem (MmTSP) is a problem, in which more than one salesmen depart from several starting cities and having returned to the starting city, form tours so that each city is visited with exactly one salesman, and the tour lengths stay within certain limits. This problem is of a great complexity and few investigations have been done on it previously. In this paper an ant system is designed to solve the problem and the results are compared to the answers obtained by solving the same problems by Lingo 8.0 which uses exact methods.
01 Jul 2011
TL;DR: This paper presents a new combined method for optimal reconfiguration using a multi-objective function with fuzzy variables that considers both objectives of load balancing and loss reduction in the feeders.
Abstract: All utility companies strive to achieve the well-balanced distribution systems in order to improve system voltage regulation by means of equal load balancing of feeders and reducing power loss. Optimal reconfiguration is one of the best solutions to reach this goal. This paper presents a new combined method for optimal reconfiguration using a multi-objective function with fuzzy variables. This method considers both objectives of load balancing and loss reduction in the feeders. Since reconfiguration is a nonlinear optimization problem, the ant colony algorithm is employed for the optimized response in search space. This method has been applied on two IEEE 33-bus and 69-bus distribution systems. Simulation results confirm the effectiveness of the proposed method in comparison with other techniques for optimal reconfiguration.