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

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

Author : Oscar Castillo

Publisher : Springer Nature

Page : 76 pages

File Size : 46,33 MB

Release : 2021-08-18

Category : Technology & Engineering

ISBN : 3030822885

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Book Description

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

Page : 890 pages

File Size : 20,65 MB

Release : 2021-01-21

Category : Technology & Engineering

ISBN : 3030593924

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Book Description

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

Page : 312 pages

File Size : 20,8 MB

Release : 2006-03-06

Category : Science

ISBN : 352760457X

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Book Description

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

Page : 202 pages

File Size : 32,61 MB

Release : 2022-01-19

Category : Computers

ISBN : 3110716216

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Book Description

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

Page : 503 pages

File Size : 33,89 MB

Release : 2020-01-17

Category : Business & Economics

ISBN : 3030264580

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Book Description

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

Page : 306 pages

File Size : 43,28 MB

Release : 2017-10-09

Category : Mathematics

ISBN : 1119386993

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Book Description

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

Page : 437 pages

File Size : 34,34 MB

Release : 2016-07-20

Category : Computers

ISBN : 3319411926

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Book Description

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

Page : 472 pages

File Size : 23,29 MB

Release : 2006-03-30

Category : Business & Economics

ISBN : 0387236678

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Book Description

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

Page : 128 pages

File Size : 46,65 MB

Release : 2008

Category : Computers

ISBN : 1905986106

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Book Description

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

Page : 68 pages

File Size : 41,65 MB

Release : 2019-04-04

Category : Computers

ISBN : 3668916144

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Book Description

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.