Showing papers on "Ant colony optimization algorithms published in 2008"

Nature-Inspired Metaheuristic Algorithms

Abstract: Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.

Ant Colony Optimization and Swarm Intelligence

Abstract: Keywords: Swarm Intelligence ; Collective Robotics ; SWARM_BOTS ; Evolutionary Robotics Note: Proceedings of the ANTS 2004, 4th International Workshop Sponsor: swarm-bots, OFES 01-0012-1 Reference LIS-BOOK-2004-002 URL: http://iridia.ulb.ac.be/~ants/ants2004/ Record created on 2006-01-12, modified on 2017-05-10

Swarm Intelligence in Optimization

Abstract: Optimization techniques inspired by swarm intelligence have become increasingly popular during the last decade. They are characterized by a decentralized way of working that mimics the behavior of swarms of social insects, flocks of birds, or schools of fish. The advantage of these approaches over traditional techniques is their robustness and flexibility. These properties make swarm intelligence a successful design paradigm for algorithms that deal with increasingly complex problems. In this chapter we focus on two of the most successful examples of optimization techniques inspired by swarm intelligence: ant colony optimization and particle swarm optimization. Ant colony optimization was introduced as a technique for combinatorial optimization in the early 1990s. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. In addition, particle swarm optimization was introduced for continuous optimization in the mid-1990s, inspired by bird flocking.

Reliability-Constrained Optimum Placement of Reclosers and Distributed Generators in Distribution Networks Using an Ant Colony System Algorithm

Abstract: Optimal placement of protection devices and distributed generators (DGs) in radial feeders is important to ensure power system reliability. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. In this paper, an ant colony system algorithm is used to derive the optimal recloser and DG placement scheme for radial distribution networks. A composite reliability index is used as the objective function in the optimization procedure. Simulations are carried out based on two practical distribution systems to validate the effectiveness of the proposed method. Furthermore, comparative studies in relation to genetic algorithm are also conducted.

Hybrid Metaheuristics: An Emerging Approach to Optimization

Abstract: Optimization problems are of great importance in many fields. They can be tackled, for example, by approximate algorithms such as metaheuristics. Examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, iterated local search, variable neighborhood search, and ant colony optimization. In recent years it has become evident that a skilled combination of a metaheuristic with other optimization techniques, a so called hybrid metaheuristic, can provide a more efficient behavior and a higher flexibility. This is because hybrid metaheuristics combine their advantages with the complementary strengths of, for example, more classical optimization techniques such as branch and bound or dynamic programming. The authors involved in this book are among the top researchers in their domain. The book is intended both to provide an overview of hybrid metaheuristics to novices of the field, and to provide researchers from the field with a collection of some of the most interesting recent developments.

Optimum design of steel frames using harmony search algorithm

Abstract: In this article, harmony search algorithm was developed for optimum design of steel frames. Harmony search is a meta-heuristic search method that has been developed recently. It bases on the analogy between the performance process of natural music and searching for solutions to optimization problems. The objective of the design algorithm is to obtain minimum weight frames by selecting suitable sections from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange (W) shapes. Strength constraints of AISC load and resistance factor design specification and displacement constraints were imposed on frames. The effectiveness and robustness of harmony search algorithm, in comparison with genetic algorithm and ant colony optimization-based methods, were verified using three steel frames. The comparisons showed that the harmony search algorithm yielded lighter designs.

Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization

Abstract: Recent computational developments in ant colony systems have proved fruitful for transforming discrete domains of application into continuous ones. In this paper, new combinations of an ant colony inspired algorithm (ACA) and chaotic sequences (ACH) are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that ACA and ACH handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature.

An ant colony optimization method for generalized TSP problem

Abstract: Focused on a variation of the euclidean traveling salesman problem (TSP), namely, the generalized traveling salesman problem (GTSP), this paper extends the ant colony optimization method from TSP to this field. By considering the group influence, an improved method is further improved. To avoid locking into local minima, a mutation process and a local searching technique are also introduced into this method. Numerical results show that the proposed method can deal with the GTSP problems fairly well, and the developed mutation process and local search technique are effective.

Biologically Inspired Algorithms for Financial Modelling

Abstract: Predicting the future for financial gain is a difficult, sometimes profitable activity. The focus of this book is the application of biologically inspired algorithms (BIAs) to financial modelling. In a detailed introduction, the authors explain computer trading on financial markets and the difficulties faced in financial market modelling. Then Part I provides a thorough guide to the various bioinspired methodologies neural networks, evolutionary computing (particularly genetic algorithms and grammatical evolution), particle swarm and ant colony optimization, and immune systems. Part II brings the reader through the development of market trading systems. Finally, Part III examines real-world case studies where BIA methodologies are employed to construct trading systems in equity and foreign exchange markets, and for the prediction of corporate bond ratings and corporate failures. The book was written for those in the finance community who want to apply BIAs in financial modelling, and for computer scientists who want an introduction to this growing application domain.

Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment

Abstract: Multiple sequence alignment, known as NP-complete problem, is among the most important and challenging tasks in computational biology. For multiple sequence alignment, it is difficult to solve this type of problems directly and always results in exponential complexity. In this paper, we present a novel algorithm of genetic algorithm with ant colony optimization for multiple sequence alignment. The proposed GA-ACO algorithm is to enhance the performance of genetic algorithm (GA) by incorporating local search, ant colony optimization (ACO), for multiple sequence alignment. In the proposed GA-ACO algorithm, genetic algorithm is conducted to provide the diversity of alignments. Thereafter, ant colony optimization is performed to move out of local optima. From simulation results, it is shown that the proposed GA-ACO algorithm has superior performance when compared to other existing algorithms.

A collaborative ant colony algorithm to stochastic mixed-model U-shaped disassembly line balancing and sequencing problem

Abstract: Disassembly operations are inevitable elements of product recovery with the disassembly line as the best choice to carry out the same. In the light of different structures of returned products (models) and variations in task completion times, the process of disassembly could not be efficiently mapped on a simple straight line. Another important issue that needs consideration is the task-time variability pertaining to human factor. In order to resolve these complexities a Mixed-Model U-shaped Disassembly Line with Stochastic Task Times has been proposed in this article. A novel approach, Collaborative Ant Colony Optimization (CACO), has been utilized that simultaneously tackles the interrelated problem of line balancing and model sequencing. The distinguishing feature of the proposed approach is that it maintains bilateral colonies of ants which independently identifies the two sequences, but utilizes the information obtained by their collaboration to guide the future path. The approach is tested on benchm...

Ant Colony Optimization for Optimal Control of Pumps in Water Distribution Networks

Abstract: Reducing energy consumption of water distribution networks has never had more significance than today. The greatest energy savings can be obtained by careful scheduling of operation of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper a new explicit representation is presented. It is based on time controlled triggers, where the maximum number of pump switches is specified beforehand. In this representation a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules (search space) compared to the binary representation. Ant colony optimization (ACO) is a stochastic meta-heuristic for combinatorial optimization problems that is inspired by the foraging behavior of some species of ants. In this paper, an application of the ACO framework was developed for the optimal scheduling of pumps. The proposed representation was adapted to an ant colony Optimization framework and solved for the optimal pump schedules. Minimization of electrical cost was considered as the objective, while satisfying system constraints. Instead of using a penalty function approach for constraint violations, constraint violations were ordered according to their importance and solutions were ranked based on this order. The proposed approach was tested on a small test network and on a large real-world network. Results are compared with those obtained using a simple genetic algorithm based on binary representation and a hybrid genetic algorithm that uses level-based triggers.

An ant colony optimization algorithm for image edge detection

Abstract: Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed ACO-based edge detection approach is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the imagepsilas intensity values. Experimental results are provided to demonstrate the superior performance of the proposed approach.

A bottom-up approach to discover transition rules of cellular automata using ant intelligence

Abstract: This paper presents a new method to discover transition rules of geographical cellular automata (CA) based on a bottom-up approach, ant colony optimization (ACO). CA are capable of simulating the evolution of complex geographical phenomena. The core of a CA model is how to define transition rules so that realistic patterns can be simulated using empirical data. Transition rules are often defined by using mathematical equations, which do not provide easily understandable explicit forms. Furthermore, it is very difficult, if not impossible, to specify equation-based transition rules for reflecting complex geographical processes. This paper presents a method of using ant intelligence to discover explicit transition rules of urban CA to overcome these limitations. This 'bottom-up' ACO approach for achieving complex task through cooperation and interaction of ants is effective for capturing complex relationships between spatial variables and urban dynamics. A discretization technique is proposed to deal with continuous spatial variables for discovering transition rules hidden in large datasets. The ACO-CA model has been used to simulate rural-urban land conversions in Guangzhou, Guangdong, China. Preliminary results suggest that this ACO-CA method can have a better performance than the decision-tree CA method.

Optimal Planning of Radial Distribution Networks by Simulated Annealing Technique

Abstract: A method for optimal planning of radial distribution networks is presented in detail based upon a combination of the steepest descent and the simulated annealing approaches. The object of investigation is the complete network of available routes and the optimization goal is to find the routes that provide the minimal total annual cost. The minimum capital cost oriented solution created by applying the steepest descent approach is used as the initial solution for the optimization procedure that is further improved by simulated annealing to obtain the minimum total cost solution. The method takes into account the capital recovery, energy loss and undelivered energy costs.

A Bee Colony Optimization Algorithm for Traveling Salesman Problem

Abstract: A bee colony optimization (BCO) algorithm for traveling salesman problem (TSP) is presented in this paper. The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. Experimental results comparing the proposed BCO model with some existing approaches on a set of benchmark problems are presented.

Advances in metaheuristics for hard optimization

Abstract: Comparison of Simulated Annealing, Interval Partitioning and Hybrid Algorithms in Constrained Global Optimization.- Linkage Synthesis of a Four-Bar Mechanism for n Desired Path Points Using Simulated Annealing.- MOSS-II Tabu/Scatter Search for Nonlinear Multiobjective Optimization.- Feature Selection for Heterogeneous Ensembles of Nearest Neighbour Classifiers Using Hybrid Tabu Search.- Parallel Ant Colony Optimization Algorithm for Solving Continuous Type Engineering Problems.- An Ant-Bidding Algorithm for Multistage Flowshop Scheduling Problem: Optimization and Phase Transitions.- Dynamic Load Balancing Using an Ant Colony Approach in Microcellular Systems.- How to Calibrate Evolutionary Algorithms.- Divide and Evolve: A Sequential Hybridization Strategy Using Evolutionary Algorithms.- Evolvable Artificial Creature.- Local Search Based on Genetic Algorithms.- A Study on Locality and Heritability in Hybrid Evolutionary Cluster Optimization.- Aligning Time Series with Genetically Tuned Dynamic Time Warping Algorithm.- Some Guidelines for Genetic Algorithm implementation in MINLP Batch Plant Design Problems.- Coevolutionary Genetic Algorithm to Solve Economic Dispatch.- An Evolutionary Approach to Solve a Novel Mechatronic Multiobjective Optimization Problem.- Optimizing Stochastic Functions by Using Genetic Algorithm: An Aeronautic Military Application.- Learning Structure Illuminates Black Boxes: An Introduction into Estimation of Distribution Algorithms.- Making a Difference to Differential Evolution.- Hidden Markov Models Training Using Population-Based Metaheuristics.- New Metaheuristic Approaches in Data Mining

Feature Subset Selection Using Ant Colony Optimization

Abstract: Feature selection is an important step in many pattern classification problems. It is applied to select a subset of features, from a much larger set, such that the selected subset is sufficient to perform the classification task. Due to its importance, the problem of feature selection has been investigated by many researchers. In this paper, a novel feature subset search procedure that utilizes the Ant Colony Optimization (ACO) is presented. The ACO is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It looks for optimal solutions by considering both local heuristics and previous knowledge. When applied to two different classification problems, the proposed algorithm achieved very promising results. Keywords—Ant Colony Optimization, ant systems, feature selection, pattern recognition.

Using a style-based ant colony system for adaptive learning

Abstract: Adaptive learning provides an alternative to the traditional ''one size fits all'' approach and has driven the development of teaching and learning towards a dynamic learning process for learning. Therefore, exploring the adaptive paths to suit learners personalized needs is an interesting issue. This paper proposes an extended approach of ant colony optimization, which is based on a recent metaheuristic method for discovering group patterns that is designed to help learners advance their on-line learning along an adaptive learning path. The investigation emphasizes the relationship of learning content to the learning style of each participant in adaptive learning. An adaptive learning rule was developed to identify how learners of different learning styles may associate those contents which have the higher probability of being useful to form an optimal learning path. A style-based ant colony system is implemented and its algorithm parameters are optimized to conform to the actual pedagogical process. A survey was also conducted to evaluate the validity and efficiency of the system in producing adaptive paths to different learners. The results reveal that both the learners and the lecturers agree that the style-based ant colony system is able to provide useful supplementary learning paths.

Two-sided assembly line balancing using an ant-colony-based heuristic

Abstract: Two-sided assembly line balancing (ALB) problems usually occur in plants which are producing large-sized high-volume products, such as buses, trucks, and domestic products. Many algorithms and heuristics have been proposed to balance the well known classical one-sided assembly lines. However, little attention has been paid to solve two-sided ALB problems. Moreover, according to our best knowledge, there is no published work in the literature on two-sided ALB problems with zoning constraints (2sALBz). In this study, an ant-colony-based heuristic algorithm is proposed for solving 2sALBz problems. This paper also makes one of the first attempts to show how an ant colony heuristic (ACH) can be applied to solve 2sALBz problems. In the paper, example applications are presented and computational experiments are performed to present the suitability of the ACH to solve 2sALBz problems. Promising results are obtained from the solution of several test problems.