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Ant Colony Optimization And Constraint Programming
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Book Synopsis Ant Colony Optimization and Constraint Programming by : Christine Solnon
Download or read book Ant Colony Optimization and Constraint Programming written by Christine Solnon and published by John Wiley & Sons. This book was released on 2013-03-04 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search approaches and metaheuristics, and shows how they can be integrated within constraint programming languages. The second part describes the ant colony optimization metaheuristic and illustrates its capabilities on different constraint satisfaction problems. The third part shows how the ant colony may be integrated within a constraint programming language, thus combining the expressive power of constraint programming languages, to describe problems in a declarative way, and the solving power of ant colony optimization to efficiently solve these problems.
Book Synopsis Ant Colony Optimization by : Marco Dorigo
Download or read book Ant Colony Optimization written by Marco Dorigo and published by MIT Press. This book was released on 2004-06-04 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
Book Synopsis Ant Colony Optimization by : Avi Ostfeld
Download or read book Ant Colony Optimization written by Avi Ostfeld and published by BoD – Books on Demand. This book was released on 2011-02-04 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ants communicate information by leaving pheromone tracks. A moving ant leaves, in varying quantities, some pheromone on the ground to mark its way. While an isolated ant moves essentially at random, an ant encountering a previously laid trail is able to detect it and decide with high probability to follow it, thus reinforcing the track with its own pheromone. The collective behavior that emerges is thus a positive feedback: where the more the ants following a track, the more attractive that track becomes for being followed; thus the probability with which an ant chooses a path increases with the number of ants that previously chose the same path. This elementary ant's behavior inspired the development of ant colony optimization by Marco Dorigo in 1992, constructing a meta-heuristic stochastic combinatorial computational methodology belonging to a family of related meta-heuristic methods such as simulated annealing, Tabu search and genetic algorithms. This book covers in twenty chapters state of the art methods and applications of utilizing ant colony optimization algorithms. New methods and theory such as multi colony ant algorithm based upon a new pheromone arithmetic crossover and a repulsive operator, new findings on ant colony convergence, and a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented.
Book Synopsis Ant Colony Optimization by : Helio Barbosa
Download or read book Ant Colony Optimization written by Helio Barbosa and published by BoD – Books on Demand. This book was released on 2013-02-20 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented.
Book Synopsis Ant Colony Optimization and Applications by : Stefka Fidanova
Download or read book Ant Colony Optimization and Applications written by Stefka Fidanova and published by Springer Nature. This book was released on 2021-02-27 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is interesting and full of new ideas. It provokes the curiosity of the readers. The book targets both researchers and practitioners. The students and the researchers will acquire knowledge about ant colony optimization and its possible applications as well as practitioners will find new ideas and solutions of their combinatorial optimization and decision-making problems. Ant colony optimization is between the best method for solving difficult optimization problems arising in real life and industry. It has obtained distinguished results on some applications with very restrictive constraints. The reader will find theoretical aspects of ant method as well as applications on a variety of problems. The following applications could be mentioned: multiple knapsack problem, which is an important economical problem; grid scheduling problem; GPS surveying problem; E. coli cultivation modeling; wireless sensor network positioning; image edges detection; workforce planning.
Book Synopsis A Study Into Ant Colony Optimisation, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems by : Jano Iljà van Hemert
Download or read book A Study Into Ant Colony Optimisation, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems written by Jano Iljà van Hemert and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Constraint-based Local Search by : Pascal Van Hentenryck
Download or read book Constraint-based Local Search written by Pascal Van Hentenryck and published by MIT Press (MA). This book was released on 2005 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ubiquity of combinatorial optimization problems in our society is illustrated by the novel application areas for optimization technology, which range from supply chain management to sports tournament scheduling. Over the last two decades, constraint programming has emerged as a fundamental methodology to solve a variety of combinatorial problems, and rich constraint programming languages have been developed for expressing and combining constraints and specifying search procedures at a high level of abstraction. Local search approaches to combinatorial optimization are able to isolate optimal or near-optimal solutions within reasonable time constraints. This book introduces a method for solving combinatorial optimization problems that combines constraint programming and local search, using constraints to describe and control local search, and a programming language, COMET, that supports both modeling and search abstractions in the spirit of constraint programming. After an overview of local search including neighborhoods, heuristics, and metaheuristics, the book presents the architecture and modeling and search components of constraint-based local search and describes how constraint-based local search is supported in COMET. The book describes a variety of applications, arranged by meta-heuristics. It presents scheduling applications, along with the background necessary to understand these challenging problems. The book also includes a number of satisfiability problems, illustrating the ability of constraint-based local search approaches to cope with both satisfiability and optimization problems in a uniform fashion.
Book Synopsis Ant Colony Optimization and Swarm Intelligence by : Marco Dorigo
Download or read book Ant Colony Optimization and Swarm Intelligence written by Marco Dorigo and published by Springer. This book was released on 2004-11-24 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1 With its fourth edition, the ANTS series of workshops has changed its name. The original"ANTS-From Ant Colonies to Artificial Ants: International Workshop on Ant Algorithms" has become "ANTS - International Workshop on Ant Colony Optimization and Swarm Intelligence". This change is mainly due to the following reasons. First, the term "ant algorithms" was slower in spreading in the research community than the term "swarm intelligence", while at the same time research inso-called swarm robotics was the subject of increasing activity: it was therefore an obvious choice to substitute the term ant algorithms with the more accepted and used term swarm intelligence. Second, although swarm intelligence research has undoubtedly produced a 2 number of interesting and promising research directions, we think it is fair to say that its most successful strand is the one known as "ant colony optimization". Ant colony optimization, first introduced in the early 1990s as a novel tool for the approximate solution of discrete optimization problems, has recently seen an explosion in the number of its applications, both to academic and real-world problems, and is currently being extended to the realm of continuous optimization (a few papers on this subject being published in these proceedings). It is therefore a reasonable choice to have the term ant colony optimization as part of the workshop name
Book Synopsis Ant Colony Optimization and Swarm Intelligence by : Marco Dorigo
Download or read book Ant Colony Optimization and Swarm Intelligence written by Marco Dorigo and published by Springer Science & Business Media. This book was released on 2008-09-10 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series of biannual international conferences “ANTS – International C- ference on Ant Colony Optimization and Swarm Intelligence”, now in its sixth edition, was started ten years ago, with the organization of ANTS’98. As some readers might recall, the ?rst edition of ANTS was titled “ANTS’98 – From Ant Colonies to Arti?cial Ants: First International Workshop on Ant Colony Op- mization. ” In fact, at that time the focus was mainly on ant colony optimization (ACO), the ?rst swarm intelligence algorithm to go beyond a pure scienti?c interest and to enter the realm of real-world applications. Interestingly, in the ten years after the ?rst edition there has been a gr- ing interest not only for ACO, but for a number of other studies that belong more generally to the area of swarmintelligence. The rapid growth of the swarm intelligence ?eld is attested by a number of indicators. First, the number of s- missions and participants to the ANTS conferences has steadily increased over the years. Second, a number of international conferences in computational - telligence and related disciplines organize workshops on subjects such as swarm intelligence, ant algorithms, ant colony optimization, and particle swarm op- mization. Third, IEEE startedorganizing,in 2003,the IEEE SwarmIntelligence Symposium (in order to maintain unity in this growing ?eld, we are currently establishingacooperationagreementbetweenIEEE SISandANTSsoastohave 1 IEEE SIS in odd years and ANTS in even years). Last, the Swarm Intelligence journal was born.
Book Synopsis Theoretical and Practical Aspects of Ant Colony Optimization by : Christian Blum
Download or read book Theoretical and Practical Aspects of Ant Colony Optimization written by Christian Blum and published by IOS Press. This book was released on 2004 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combinatorial optimization problems are of high academical and practical importance. Unfortunately, many of them belong to the class of NP-hard problems and are therefore intractable. In other words, as their dimension increases, the time needed by exact methods to find an optimal solution grows exponentially. Metaheuristics are approximate methods for attacking these problems. An approximate method is a technique that is applied in order to find a good enough solution in a reasonable amount of time. Examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, and ant colony optimization (ACO), the subject of this book. The contributions of this book to ACO research are twofold. First, some new theoretical results are proven that improve our understanding of how ACO works. Second, a new framework for ACO algorithms is proposed that is shown to perform at the state-of-the-art level on some important combinatorial optimization problems such as the k-cardinality tree problem and the group shop scheduling problem, which is a general shop scheduling problem that includes among others the well-known job shop scheduling and the open shop scheduling problems.
Book Synopsis Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems by : Laurent Perron
Download or read book Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems written by Laurent Perron and published by Springer Science & Business Media. This book was released on 2008-05-08 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2008, held in Paris, France, in May 2008. The 18 revised long papers and 22 revised short papers presented together with 3 invited talks were carefully reviewed and selected from 130 submissions. The papers describe current research in the fields of constraint programming, artificial intelligence, and operations research to explore ways of solving large-scale, practical optimization problems through integration and hybridization of the fields' different techniques.
Book Synopsis Ant Colony Optimization and Swarm Intelligence by : Marco Dorigo
Download or read book Ant Colony Optimization and Swarm Intelligence written by Marco Dorigo and published by Springer. This book was released on 2008-09-20 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series of biannual international conferences “ANTS – International C- ference on Ant Colony Optimization and Swarm Intelligence”, now in its sixth edition, was started ten years ago, with the organization of ANTS’98. As some readers might recall, the ?rst edition of ANTS was titled “ANTS’98 – From Ant Colonies to Arti?cial Ants: First International Workshop on Ant Colony Op- mization. ” In fact, at that time the focus was mainly on ant colony optimization (ACO), the ?rst swarm intelligence algorithm to go beyond a pure scienti?c interest and to enter the realm of real-world applications. Interestingly, in the ten years after the ?rst edition there has been a gr- ing interest not only for ACO, but for a number of other studies that belong more generally to the area of swarmintelligence. The rapid growth of the swarm intelligence ?eld is attested by a number of indicators. First, the number of s- missions and participants to the ANTS conferences has steadily increased over the years. Second, a number of international conferences in computational - telligence and related disciplines organize workshops on subjects such as swarm intelligence, ant algorithms, ant colony optimization, and particle swarm op- mization. Third, IEEE startedorganizing,in 2003,the IEEE SwarmIntelligence Symposium (in order to maintain unity in this growing ?eld, we are currently establishingacooperationagreementbetweenIEEE SISandANTSsoastohave 1 IEEE SIS in odd years and ANTS in even years). Last, the Swarm Intelligence journal was born.
Book Synopsis Ant Colony Optimization and Swarm Intelligence by : Marco Dorigo
Download or read book Ant Colony Optimization and Swarm Intelligence written by Marco Dorigo and published by Springer. This book was released on 2006-08-29 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006. The 27 revised full papers, 23 revised short papers, and 12 extended abstracts presented were carefully reviewed and selected from 115 submissions.
Book Synopsis Mathematical Programming Solver Based on Local Search by : Frédéric Gardi
Download or read book Mathematical Programming Solver Based on Local Search written by Frédéric Gardi and published by John Wiley & Sons. This book was released on 2014-07-09 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today's end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kind of mathematical programming solver, namely LocalSolver, based on neighborhood search. First, an iconoclast methodology is presented to design and engineer local search algorithms. The authors' concern regarding industrializing local search approaches is of particular interest for practitioners. This methodology is applied to solve two industrial problems with high economic stakes. Software based on local search induces extra costs in development and maintenance in comparison with the direct use of mixed-integer linear programming solvers. The authors then move on to present the LocalSolver project whose goal is to offer the power of local search through a model-and-run solver for large-scale 0-1 nonlinear programming. They conclude by presenting their ongoing and future work on LocalSolver toward a full mathematical programming solver based on local search.
Book Synopsis Hybrids of Stochastic Metaheuristics and Constraint Programming for Combinatorial Optimization by : Dhananjay Raghavan Thiruvady
Download or read book Hybrids of Stochastic Metaheuristics and Constraint Programming for Combinatorial Optimization written by Dhananjay Raghavan Thiruvady and published by . This book was released on 2012 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combinatorial optimization problems are frequently encountered in scientific and industrial applications. A large number of these problems are known to be NP-hard and solving these problems by exact/complete methods is often impractical. Therefore, heuristic methods are often the best way to deal with such problems. However, when these problems additionally include hard constraints, heuristic/metaheuristic methods often fail and perform poorly. A solution to this is to integrate these methods with techniques like constraint programming (CP) where constraints maybe easily specified and efficiently dealt with thereby providing effective solutions to combinatorial optimization problems with non-trivial hard constraints. This thesis specifically investigates the integration of stochastic metaheuristics and constraint programming for combinatorial optimization problems (COPs) with non-trivial hard constraints. Among stochastic metaheuristics, we consider ant colony optimization (ACO) and beam search. The base algorithm we begin with is the hybrid CP-ACO (Meyer and Ernst, 2004) and the aim of the thesis is to show that this algorithm can be made efficient by parallelizing the solution construction of ACO (dependent solution construction as opposed to solution construction onmultiple machines/cores) via beam search leading to the hybrid CP-Beam-ACO.We consider three case studies to demonstrate the effectiveness of the new hybrid. The first problem is single machine job scheduling with sequence dependent setup times (SMJS). The aim here is to minimize makespan making sure hard release and due time constraints are not violated. Secondly, a resource constrained multiple machine job scheduled (MMJS) problem is tackled. Here, constraints include release times, due times, precedences, and a resource constraint across the machines. The aim is to minimize the total weighted tardiness of the scheduled jobs. The last problem considered is the car sequencing (CS) problem. Here a number of cars requiring options must be sequenced such that sub-sequences of a particular length may only allow a specific number of each option. We investigate the performance of our algorithms on the optimization version which further requires the utilization of options be effectively modulated across the sub-sequences.By applying ACO, Beam-ACO, CP-ACO and CP-Beam-ACO to the above problems we see that CP-ACO methods are by far the best performing algorithms in terms of solution quality. This is clearly seen on the SMJS and CS problems for which complex CP models have been defined. There is a slight disadvantage to CP-ACO for the MMJS problem where the CP model is relatively simple. In termsof finding feasible solutions, the feasibility advantages of CP-ACO are inherited by CP-Beam-ACO. We also see for the MMJS problem that CP-Beam-ACO has a significant advantage in this respect.In conclusion, this thesis demonstrates that constraint-based ACO is an efficient and effective method to tackle real-world COPs. In particular, CP-ACO can be made efficient by parallelizing the solution construction of the ACO component resulting in CP-Beam-ACO. The new algorithm provides significant advantages for three different types of real-world COPs with non-trivial hard constraints and can be viewed as the practically viable option for implementing ACO with CP.
Book Synopsis Recent Advances in Computational Optimization by : Stefka Fidanova
Download or read book Recent Advances in Computational Optimization written by Stefka Fidanova and published by Springer. This book was released on 2017-06-25 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents new optimization approaches and methods and their application in real-world and industrial problems, and demonstrates how many of the problems arising in engineering, economics and other domains can be formulated as optimization problems. Constituting a comprehensive collection of extended contributions from the 9th International Workshop on Computational Optimization (WCO) held in Gdansk, Poland, September 11–14, 2016, the book discusses important applications such as job scheduling, wildfire modeling, parameter settings for controlling different processes, capital budgeting, data mining, finding the location of sensors in a given network, identifying the conformation of molecules, algorithm correctness, decision support system, and computer memory management. Further, it shows how to develop algorithms for these based on new intelligent methods like evolutionary computations, ant colony optimization and constraint programming. The book is a valuable resource for researchers and practitioners alike.
Book Synopsis Autonomous Search by : Youssef Hamadi
Download or read book Autonomous Search written by Youssef Hamadi and published by Springer Science & Business Media. This book was released on 2012-01-05 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decades of innovations in combinatorial problem solving have produced better and more complex algorithms. These new methods are better since they can solve larger problems and address new application domains. They are also more complex which means that they are hard to reproduce and often harder to fine-tune to the peculiarities of a given problem. This last point has created a paradox where efficient tools are out of reach of practitioners. Autonomous search (AS) represents a new research field defined to precisely address the above challenge. Its major strength and originality consist in the fact that problem solvers can now perform self-improvement operations based on analysis of the performances of the solving process -- including short-term reactive reconfiguration and long-term improvement through self-analysis of the performance, offline tuning and online control, and adaptive control and supervised control. Autonomous search "crosses the chasm" and provides engineers and practitioners with systems that are able to autonomously self-tune their performance while effectively solving problems. This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms. Autonomous search (AS) represents a new research field defined to precisely address the above challenge. Its major strength and originality consist in the fact that problem solvers can now perform self-improvement operations based on analysis of the performances of the solving process -- including short-term reactive reconfiguration and long-term improvement through self-analysis of the performance, offline tuning and online control, and adaptive control and supervised control. Autonomous search "crosses the chasm" and provides engineers and practitioners with systems that are able to autonomously self-tune their performance while effectively solving problems. This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms. This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms. This is the first book dedicated to this topic, and it can be used as a reference for researchers, engineers, and postgraduates in the areas of constraint programming, machine learning, evolutionary computing, and feedback control theory. After the editors' introduction to autonomous search, the chapters are focused on tuning algorithm parameters, autonomous complete (tree-based) constraint solvers, autonomous control in metaheuristics and heuristics, and future autonomous solving paradigms.
