A New Meta Heuristic Optimization Algorithm Based On The String Theory Paradigm From Physics

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A New Meta-heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics

Author : Oscar Castillo
Publisher : Springer Nature
ISBN 13 : 3030822885
Total Pages : 76 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis A New Meta-heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics by : Oscar Castillo

Download or read book A New Meta-heuristic Optimization Algorithm Based on the String Theory Paradigm from Physics written by Oscar Castillo and published by Springer Nature. This book was released on 2021-08-18 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the fields of nature-inspired algorithms, optimization problems and fuzzy logic. In this book, a new metaheuristic based on String Theory from Physics is proposed. It is important to mention that we have proposed the new algorithm to generate new potential solutions in optimization problems in order to find new ways that could improve the results in solving these problems. We are presenting the results for the proposed method in different cases of study. The first case, is optimization of traditional benchmark mathematical functions. The second case, is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting results of the CEC 2017 Competition on Constrained Real-Parameter Optimization that are problems that contain the presence of constraints that alter the shape of the search space making them more difficult to solve. Finally, in the third case, we are presenting the optimization of a fuzzy inference system, specifically for finding the optimal design of a fuzzy controller for an autonomous mobile robot. It is important to mention that in all study cases we are presenting statistical tests in or-der to validate the performance of proposed method. In summary, we believe that this book will be of great interest to a wide audience, ranging from engineering and science graduate students, to researchers and professors in computational intelligence, metaheuristics, optimization, robotics and control.

Advances in Metaheuristic Algorithms for Optimal Design of Structures

Author : Ali Kaveh
Publisher : Springer Nature
ISBN 13 : 3030593924
Total Pages : 890 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Advances in Metaheuristic Algorithms for Optimal Design of Structures by : Ali Kaveh

Download or read book Advances in Metaheuristic Algorithms for Optimal Design of Structures written by Ali Kaveh and published by Springer Nature. This book was released on 2021-01-21 with total page 890 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his graduate students, consisting of Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Democratic Particle Swarm Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which are developed by other authors and have been successfully applied to various optimization problems. These consist of Partical Swarm Optimization, Big Band Big Crunch algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm and Chaos Embedded Metaheuristic Algorithm. Finally a multi-objective Optimization is presented to Solve large scale structural problems based on the Charged System Search algorithm, In the second edition seven new chapters are added consisting of Enhance colliding bodies optimization, Global sensitivity analysis, Tug of War Optimization, Water evaporation optimization, Vibrating System Optimization and Cyclical Parthenogenesis Optimization algorithm. In the third edition, five new chapters are included consisting of the recently developed algorithms. These are Shuffled Shepherd Optimization Algorithm, Set Theoretical Shuffled Shepherd Optimization Algorithm, Set Theoretical Teaching-Learning-Based Optimization Algorithm, Thermal Exchange Metaheuristic Optimization Algorithm, and Water Strider Optimization Algorithm and Its Enhancement. The concepts and algorithm presented in this book are not only applicable to optimization of skeletal structure, finite element models, but can equally be utilized for optimal design of other systems such as hydraulic and electrical networks.

New Optimization Algorithms in Physics

Author : Alexander K. Hartmann
Publisher : Wiley-VCH
ISBN 13 : 352760457X
Total Pages : 312 pages
Book Rating : 4.5/5 (276 download)

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Book Synopsis New Optimization Algorithms in Physics by : Alexander K. Hartmann

Download or read book New Optimization Algorithms in Physics written by Alexander K. Hartmann and published by Wiley-VCH. This book was released on 2006-03-06 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many physicists are not aware of the fact that they can solve their problems by applying optimization algorithms. Since the number of such algorithms is steadily increasing, many new algorithms have not been presented comprehensively until now. This presentation of recently developed algorithms applied in physics, including demonstrations of how they work and related results, aims to encourage their application, and as such the algorithms selected cover concepts and methods from statistical physics to optimization problems emerging in theoretical computer science.

Meta-heuristic Optimization Techniques

Author : Anuj Kumar
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110716216
Total Pages : 202 pages
Book Rating : 4.1/5 (17 download)

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Book Synopsis Meta-heuristic Optimization Techniques by : Anuj Kumar

Download or read book Meta-heuristic Optimization Techniques written by Anuj Kumar and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-01-19 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offer a thorough overview of the most popular and researched meta-heuristic optimization techniques and nature inspired algorithms. Their wide applicability makes them a hot research topic and an efficient tool for the solution of complex optimization problems in various field of sciences, engineering and in numerous industries.

Nature-Inspired Methods for Metaheuristics Optimization

Author : Fouad Bennis
Publisher : Springer Nature
ISBN 13 : 3030264580
Total Pages : 503 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Nature-Inspired Methods for Metaheuristics Optimization by : Fouad Bennis

Download or read book Nature-Inspired Methods for Metaheuristics Optimization written by Fouad Bennis and published by Springer Nature. This book was released on 2020-01-17 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

Author : Omid Bozorg-Haddad
Publisher : John Wiley & Sons
ISBN 13 : 1119386993
Total Pages : 306 pages
Book Rating : 4.1/5 (193 download)

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Book Synopsis Meta-heuristic and Evolutionary Algorithms for Engineering Optimization by : Omid Bozorg-Haddad

Download or read book Meta-heuristic and Evolutionary Algorithms for Engineering Optimization written by Omid Bozorg-Haddad and published by John Wiley & Sons. This book was released on 2017-10-09 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering; Relates optimization algorithms to engineering problems employing a unifying approach. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science. OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran. MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran. HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.

Search and Optimization by Metaheuristics

Author : Ke-Lin Du
Publisher : Birkhäuser
ISBN 13 : 3319411926
Total Pages : 437 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Search and Optimization by Metaheuristics by : Ke-Lin Du

Download or read book Search and Optimization by Metaheuristics written by Ke-Lin Du and published by Birkhäuser. This book was released on 2016-07-20 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones. An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics. Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.

Metaheuristic Optimization via Memory and Evolution

Author : Cesar Rego
Publisher : Springer Science & Business Media
ISBN 13 : 0387236678
Total Pages : 472 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Metaheuristic Optimization via Memory and Evolution by : Cesar Rego

Download or read book Metaheuristic Optimization via Memory and Evolution written by Cesar Rego and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tabu Search (TS) and, more recently, Scatter Search (SS) have proved highly effective in solving a wide range of optimization problems, and have had a variety of applications in industry, science, and government. The goal of Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search is to report original research on algorithms and applications of tabu search, scatter search or both, as well as variations and extensions having "adaptive memory programming" as a primary focus. Individual chapters identify useful new implementations or new ways to integrate and apply the principles of TS and SS, or that prove new theoretical results, or describe the successful application of these methods to real world problems.

Nature-Inspired Metaheuristic Algorithms

Author : Xin-She Yang
Publisher : Luniver Press
ISBN 13 : 1905986106
Total Pages : 128 pages
Book Rating : 4.9/5 (59 download)

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Book Synopsis Nature-Inspired Metaheuristic Algorithms by : Xin-She Yang

Download or read book Nature-Inspired Metaheuristic Algorithms written by Xin-She Yang and published by Luniver Press. This book was released on 2008 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Harmony Search Algorithm. Theory and Applications

Author : Assif Assad
Publisher : GRIN Verlag
ISBN 13 : 3668916144
Total Pages : 68 pages
Book Rating : 4.6/5 (689 download)

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Book Synopsis Harmony Search Algorithm. Theory and Applications by : Assif Assad

Download or read book Harmony Search Algorithm. Theory and Applications written by Assif Assad and published by GRIN Verlag. This book was released on 2019-04-04 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doctoral Thesis / Dissertation from the year 2018 in the subject Computer Sciences - Artificial Intelligence, grade: A, Indian Institute of Technology Roorkee, language: English, abstract: The aim of this book is to introduce Harmony Search algorithm in the context of solving real life problems. Harmony Search (HS) is a musician’s behavior inspired metaheuristic algorithm developed in 2001, though it is a relatively new meta heuristic algorithm, its effectiveness and advantages have been demonstrated in various applications like traffic routing, multi objective optimization, design of municipal water distribution networks, load dispatch problem in electrical engineering, rostering problems, clustering, structural design, classification and feature selection to name a few. Optimization is the process of finding the best alternate solution among a given set of solutions under some given constraints. The process of finding the maximum or minimum possible value, which a function can attain in its domain, is known as optimization. One of the most striking trends that emerged in the optimization field is the simulation of natural processes as efficient global search methods. The natural processes or phenomena are firstly analyzed mathematically and then coded as computer programs for solving complex nonlinear real-world problems. The resulting methods are called Nature Inspired Algorithms that can often outperform classic methods. The advantages of these methods are their ability to solve various standard or application-based problems successfully without any prior knowledge of the problem space. Moreover, these algorithms are more likely to obtain the global optima of a given problem. They do not require any continuity and differentiability of the objective functions. Also, they work on a randomly generated population of solutions instead of one solution. They are easy to program and can be easily implemented on a computer. Some of the examples of Nature Inspired Optimization Techniques are Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony Optimization and Ant Colony Optimization.

Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications

Author : Modestus O. Okwu
Publisher :
ISBN 13 : 9783030611125
Total Pages : 0 pages
Book Rating : 4.6/5 (111 download)

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Book Synopsis Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications by : Modestus O. Okwu

Download or read book Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications written by Modestus O. Okwu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.

Nature-inspired Methods for Metaheuristics Optimization

Author :
Publisher :
ISBN 13 : 9783030264598
Total Pages : 503 pages
Book Rating : 4.2/5 (645 download)

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Book Synopsis Nature-inspired Methods for Metaheuristics Optimization by :

Download or read book Nature-inspired Methods for Metaheuristics Optimization written by and published by . This book was released on 2020 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature. The first group of chapters describes in detail different meta-heuristic algorithms, and shows their applicability using some test or real-world problems. The second part of the book is especially focused on advanced applications and case studies. They span different engineering fields, including mechanical, electrical and civil engineering, and earth/environmental science, and covers topics such as robotics, water management, process optimization, among others. The book covers both basic concepts and advanced issues, offering a timely introduction to nature-inspired optimization method for newcomers and students, and a source of inspiration as well as important practical insights to engineers and researchers.

New Meta-heuristic Optimization Algorithms for Solving Continuous and Combinatorial Problems

Author : Azmi Rafi Alazzam
Publisher :
ISBN 13 : 9781303746253
Total Pages : 248 pages
Book Rating : 4.7/5 (462 download)

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Book Synopsis New Meta-heuristic Optimization Algorithms for Solving Continuous and Combinatorial Problems by : Azmi Rafi Alazzam

Download or read book New Meta-heuristic Optimization Algorithms for Solving Continuous and Combinatorial Problems written by Azmi Rafi Alazzam and published by . This book was released on 2013 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advances in Metaheuristic Algorithms for Optimal Design of Structures

Author : A. Kaveh
Publisher : Springer
ISBN 13 : 9783319834597
Total Pages : 631 pages
Book Rating : 4.8/5 (345 download)

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Book Synopsis Advances in Metaheuristic Algorithms for Optimal Design of Structures by : A. Kaveh

Download or read book Advances in Metaheuristic Algorithms for Optimal Design of Structures written by A. Kaveh and published by Springer. This book was released on 2018-06-29 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents efficient metaheuristic algorithms for optimal design of structures. Many of these algorithms are developed by the author and his colleagues, consisting of Democratic Particle Swarm Optimization, Charged System Search, Magnetic Charged System Search, Field of Forces Optimization, Dolphin Echolocation Optimization, Colliding Bodies Optimization, Ray Optimization. These are presented together with algorithms which were developed by other authors and have been successfully applied to various optimization problems. These consist of Particle Swarm Optimization, Big Bang-Big Crunch Algorithm, Cuckoo Search Optimization, Imperialist Competitive Algorithm, and Chaos Embedded Metaheuristic Algorithms. Finally a multi-objective optimization method is presented to solve large-scale structural problems based on the Charged System Search algorithm. The concepts and algorithms presented in this book are not only applicable to optimization of skeletal structures and finite element models, but can equally be utilized for optimal design of other systems such as hydraulic and electrical networks. In the second edition seven new chapters are added consisting of the new developments in the field of optimization. These chapters consist of the Enhanced Colliding Bodies Optimization, Global Sensitivity Analysis, Tug of War Optimization, Water Evaporation Optimization, Vibrating Particle System Optimization and Cyclical Parthenogenesis Optimization algorithms. A chapter is also devoted to optimal design of large scale structures.

Metaheuristic Optimization Algorithms

Author : Laith Abualigah
Publisher : Elsevier
ISBN 13 : 0443139253
Total Pages : 290 pages
Book Rating : 4.4/5 (431 download)

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Book Synopsis Metaheuristic Optimization Algorithms by : Laith Abualigah

Download or read book Metaheuristic Optimization Algorithms written by Laith Abualigah and published by Elsevier. This book was released on 2024-06 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. The book provides readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm that is followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies.

Nature-Inspired Optimization Algorithms

Author : Xin-She Yang
Publisher : Elsevier
ISBN 13 : 0124167454
Total Pages : 277 pages
Book Rating : 4.1/5 (241 download)

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Book Synopsis Nature-Inspired Optimization Algorithms by : Xin-She Yang

Download or read book Nature-Inspired Optimization Algorithms written by Xin-She Yang and published by Elsevier. This book was released on 2014-02-17 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. - Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature - Provides a theoretical understanding as well as practical implementation hints - Provides a step-by-step introduction to each algorithm

Ant Colony Optimization

Author : Marco Dorigo
Publisher : MIT Press
ISBN 13 : 9780262042192
Total Pages : 324 pages
Book Rating : 4.0/5 (421 download)

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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.