Toward Enhancing Metaheuristic Optimization Algorithms Using Center Based Sampling Strategies For Solving Single And Multi Objective Large Scale Problems

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Toward Enhancing Metaheuristic Optimization Algorithms Using Center-based Sampling Strategies for Solving Single- and Multi- Objective Large-scale Problems

Author : Hanan Hiba
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Toward Enhancing Metaheuristic Optimization Algorithms Using Center-based Sampling Strategies for Solving Single- and Multi- Objective Large-scale Problems by : Hanan Hiba

Download or read book Toward Enhancing Metaheuristic Optimization Algorithms Using Center-based Sampling Strategies for Solving Single- and Multi- Objective Large-scale Problems written by Hanan Hiba and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last decade, metaheuristic algorithms have become well-established approaches utilized for solving complex real-world optimization problems. Most metaheuristic algorithms have used stochastic strategies in their initialization as well as during the new candidate solution generation process where there is no a priori knowledge about the solution, which is a common assumption for any black-box optimization problem. In recent years, researchers have introduced a new concept called center-based sampling that can be used in any search component of the optimization process, but so far, it has mainly been utilized for population initialization. This concept clarifies that in a search space, the center point has a higher probability value to be closer to an unknown solution compared to a uniformly generated random point, especially when the dimension increases. Thus, this novel concept helps the optimizer to find a better solution efficiently. In this thesis, a comprehensive study has been conducted on the effect of center-based sampling to solve an optimization problem using three different levels of investigation. These levels are as follows: 1) no specific algorithm and no specific landscape (i.e., Monte-Carlo-based simulation); 2) a specific landscape but no specific algorithm (random search vs. center-based random search), and finally, 3) a specific algorithm and specific landscape (proposing three different schemes for using center-based sampling for solving Large-scale Global Optimization (LSGO) problems). Also, a center-based sampling for multi-objective optimization is proposed. Furthermore, in this thesis, I seek to investigate the properties and capabilities of center-based sampling during optimization, which can be extended to utilize it in machine learning techniques, as well. The proposed methods are evaluated on discrete and continuous Large-scale Global Optimization (LSGO) benchmark functions. The experimental results confirm that center based sampling has a crucial impact on improving the convergence rate of optimization/search algorithms when solving high-dimensional optimization problems.

Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms

Author : André A. Keller
Publisher : Bentham Science Publishers
ISBN 13 : 1681087065
Total Pages : 310 pages
Book Rating : 4.6/5 (81 download)

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Book Synopsis Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms by : André A. Keller

Download or read book Multi-Objective Optimization in Theory and Practice II: Metaheuristic Algorithms written by André A. Keller and published by Bentham Science Publishers. This book was released on 2019-03-28 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.

Extremal Optimization

Author : Yong-Zai Lu
Publisher : CRC Press
ISBN 13 : 1315360071
Total Pages : 228 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Extremal Optimization by : Yong-Zai Lu

Download or read book Extremal Optimization written by Yong-Zai Lu and published by CRC Press. This book was released on 2018-09-03 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extremal Optimization: Fundamentals, Algorithms, and Applications introduces state-of-the-art extremal optimization (EO) and modified EO (MEO) solutions from fundamentals, methodologies, and algorithms to applications based on numerous classic publications and the authors’ recent original research results. It promotes the movement of EO from academic study to practical applications. The book covers four aspects, beginning with a general review of real-world optimization problems and popular solutions with a focus on computational complexity, such as "NP-hard" and the "phase transitions" occurring on the search landscape. Next, it introduces computational extremal dynamics and its applications in EO from principles, mechanisms, and algorithms to the experiments on some benchmark problems such as TSP, spin glass, Max-SAT (maximum satisfiability), and graph partition. It then presents studies on the fundamental features of search dynamics and mechanisms in EO with a focus on self-organized optimization, evolutionary probability distribution, and structure features (e.g., backbones), which are based on the authors’ recent research results. Finally, it discusses applications of EO and MEO in multiobjective optimization, systems modeling, intelligent control, and production scheduling. The authors present the advanced features of EO in solving NP-hard problems through problem formulation, algorithms, and simulation studies on popular benchmarks and industrial applications. They also focus on the development of MEO and its applications. This book can be used as a reference for graduate students, research developers, and practical engineers who work on developing optimization solutions for those complex systems with hardness that cannot be solved with mathematical optimization or other computational intelligence, such as evolutionary computations.

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.

Novel Opposition-based Sampling Methods for Efficiently Solving Challenging Optimization Problems

Author : Ali Esmailzadeh
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (755 download)

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Book Synopsis Novel Opposition-based Sampling Methods for Efficiently Solving Challenging Optimization Problems by : Ali Esmailzadeh

Download or read book Novel Opposition-based Sampling Methods for Efficiently Solving Challenging Optimization Problems written by Ali Esmailzadeh and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In solving noise-free and noisy optimization problems, candidate initialization and sampling play a key role, but are not deeply investigated. It is of interest to know if the entire search space has the same quality for candidate-solutions during solving different type of optimization problems. In this thesis, a comprehensive investigation is conducted in order to clear those doubts, and to examine the effects of variant sampling methods on solving challenging optimization problems, such as large-scale, noisy, and multi-modal problems. As a result, the search space is segmented by using seven segmentation schemes, namely: Center-Point, Center-Based, Modula-Opposite, Quasi-Opposite, Quasi-Reflection, Supper- Opposite, and Opposite-Random. The introduced schemes are studied using Monte-Carlo simulation, on various types of noise-free optimization problems, and ultimately ranked based on their performance in terms of probability of closeness, average distance to unknown solution, number of solutions found, and diversity. Based on the results of the experiments, high-ranked schemes are selected and utilized on well-known metaheuristic algorithms, as case studies. Two categories of case studies are targeted; one for a singlesolution- based metaheuristic (S-metaheuristic) and another one for a population based metaheuristic (P-metaheuristic). A high-ranked single-solution-based scheme is utilized to accelerate Simulated Annealing (SA) algorithm, as a noise-free S-metaheuristic case study. Similarly, for noise-free P-metaheuristic case study, an effective population-based algorithm, Differential Evolution (DE), has been utilized. The experiments confirm that the new algorithms outperform the parent algorithm (DE) on large-scale problems. In the same direction, with regards to solving noisy problems more efficiently, a Shaking-based sampling method is introduced, in which the original noise is tackled by adding an additional noise into the search process. As a case study, the Shaking-based sampling is utilized on the DE algorithm, from which two variant algorithms have been developed and showed impressive performance in comparison to the classical DE, in tackling noisy largescale problems. This thesis has created an opportunity for a comprehensive investigation on search space segmentation schemes and proposed new sampling methods. The current study has provided a guide to use appropriate sampling schemes for a given types of problems such as noisy, large-scale and multi-modal optimization problems. Furthermore, this thesis questions the effectiveness of uniform-random sampling method, which is widely used in of S-Metaheuristic and P-Metaheuristic algorithms.

Discrete Diversity and Dispersion Maximization

Author : Rafael Martí
Publisher : Springer Nature
ISBN 13 : 3031383109
Total Pages : 350 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Discrete Diversity and Dispersion Maximization by : Rafael Martí

Download or read book Discrete Diversity and Dispersion Maximization written by Rafael Martí and published by Springer Nature. This book was released on 2024-01-06 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates the metaheuristic methodologies that apply to maximum diversity problems to solve them. Maximum diversity problems arise in many practical settings from facility location to social network analysis and constitute an important class of NP-hard problems in combinatorial optimization. In fact, this volume presents a “missing link” in the combinatorial optimization-related literature. In providing the basic principles and fundamental ideas of the most successful methodologies for discrete optimization, this book allows readers to create their own applications for other discrete optimization problems. Additionally, the book is designed to be useful and accessible to researchers and practitioners in management science, industrial engineering, economics, and computer science, while also extending value to non-experts in combinatorial optimization. Owed to the tutorials presented in each chapter, this book may be used in a master course, a doctoral seminar, or as supplementary to a primary text in upper undergraduate courses. The chapters are divided into three main sections. The first section describes a metaheuristic methodology in a tutorial style, offering generic descriptions that, when applied, create an implementation of the methodology for any optimization problem. The second section presents the customization of the methodology to a given diversity problem, showing how to go from theory to application in creating a heuristic. The final part of the chapters is devoted to experimentation, describing the results obtained with the heuristic when solving the diversity problem. Experiments in the book target the so-called MDPLIB set of instances as a benchmark to evaluate the performance of the methods.

Applications of Multi-objective Evolutionary Algorithms

Author : Carlos A. Coello Coello
Publisher : World Scientific
ISBN 13 : 9812561064
Total Pages : 792 pages
Book Rating : 4.8/5 (125 download)

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Book Synopsis Applications of Multi-objective Evolutionary Algorithms by : Carlos A. Coello Coello

Download or read book Applications of Multi-objective Evolutionary Algorithms written by Carlos A. Coello Coello and published by World Scientific. This book was released on 2004 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: - Detailed MOEA applications discussed by international experts - State-of-the-art practical insights in tackling statistical optimization with MOEAs - A unique monograph covering a wide spectrum of real-world applications - Step-by-step discussion of MOEA applications in a variety of domains

Metaheuristics in Water, Geotechnical and Transport Engineering

Author : Xin-She Yang
Publisher : Newnes
ISBN 13 : 0123982960
Total Pages : 503 pages
Book Rating : 4.1/5 (239 download)

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Book Synopsis Metaheuristics in Water, Geotechnical and Transport Engineering by : Xin-She Yang

Download or read book Metaheuristics in Water, Geotechnical and Transport Engineering written by Xin-She Yang and published by Newnes. This book was released on 2012-09 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are often large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems. This book examines the latest developments of metaheuristics and their applications in water, geotechnical and transport engineering offering practical case studies as examples to demonstrate real world applications. Topics cover a range of areas within engineering, including reviews of optimization algorithms, artificial intelligence, cuckoo search, genetic programming, neural networks, multivariate adaptive regression, swarm intelligence, genetic algorithms, ant colony optimization, evolutionary multiobjective optimization with diverse applications in engineering such as behavior of materials, geotechnical design, flood control, water distribution and signal networks. This book can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheursitics, optimization in civil engineering and computational intelligence. Provides detailed descriptions of all major metaheuristic algorithms with a focus on practical implementation Develops new hybrid and advanced methods suitable for civil engineering problems at all levels Appropriate for researchers and advanced students to help to develop their work

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

Evolutionary Algorithms for Solving Multi-Objective Problems

Author : Carlos Coello Coello
Publisher : Springer Science & Business Media
ISBN 13 : 0387367977
Total Pages : 810 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis Evolutionary Algorithms for Solving Multi-Objective Problems by : Carlos Coello Coello

Download or read book Evolutionary Algorithms for Solving Multi-Objective Problems written by Carlos Coello Coello and published by Springer Science & Business Media. This book was released on 2007-08-26 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.

Cohort Intelligence: A Socio-inspired Optimization Method

Author : Anand Jayant Kulkarni
Publisher : Springer
ISBN 13 : 3319442546
Total Pages : 140 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Cohort Intelligence: A Socio-inspired Optimization Method by : Anand Jayant Kulkarni

Download or read book Cohort Intelligence: A Socio-inspired Optimization Method written by Anand Jayant Kulkarni and published by Springer. This book was released on 2016-09-22 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Volume discusses the underlying principles and analysis of the different concepts associated with an emerging socio-inspired optimization tool referred to as Cohort Intelligence (CI). CI algorithms have been coded in Matlab and are freely available from the link provided inside the book. The book demonstrates the ability of CI methodology for solving combinatorial problems such as Traveling Salesman Problem and Knapsack Problem in addition to real world applications from the healthcare, inventory, supply chain optimization and Cross-Border transportation. The inherent ability of handling constraints based on probability distribution is also revealed and proved using these problems.

The Linear Ordering Problem

Author : Rafael Martí
Publisher : Springer
ISBN 13 : 9783642167287
Total Pages : 172 pages
Book Rating : 4.1/5 (672 download)

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Book Synopsis The Linear Ordering Problem by : Rafael Martí

Download or read book The Linear Ordering Problem written by Rafael Martí and published by Springer. This book was released on 2011-01-05 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Faced with the challenge of solving the hard optimization problems that abound in the real world, existing methods often encounter great difficulties. Important applications in business, engineering or economics cannot be tackled by the techniques that have formed the predominant focus of academic research throughout the past three decades. Exact and heuristic approaches are dramatically changing our ability to solve problems of practical significance and are extending the frontier of problems that can be handled effectively. This monograph details state-of-the-art optimization methods, both exact and heuristic, for the LOP. The authors employ the LOP to illustrate contemporary optimization technologies as well as how to design successful implementations of exact and heuristic procedures. Therefore, they do not limit the scope of this book to the LOP, but on the contrary, provide the reader with the background and practical strategies in optimization to tackle different combinatorial problems.

Advances in Metaheuristic Algorithms for Optimal Design of Structures

Author : A. Kaveh
Publisher : Springer
ISBN 13 : 9783319461724
Total Pages : 0 pages
Book Rating : 4.4/5 (617 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 2016-11-17 with total page 0 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.

Multiobjective Optimization

Author : Jürgen Branke
Publisher : Springer
ISBN 13 : 3540889086
Total Pages : 481 pages
Book Rating : 4.5/5 (48 download)

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Book Synopsis Multiobjective Optimization by : Jürgen Branke

Download or read book Multiobjective Optimization written by Jürgen Branke and published by Springer. This book was released on 2008-10-18 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multiobjective optimization deals with solving problems having not only one, but multiple, often conflicting, criteria. Such problems can arise in practically every field of science, engineering and business, and the need for efficient and reliable solution methods is increasing. The task is challenging due to the fact that, instead of a single optimal solution, multiobjective optimization results in a number of solutions with different trade-offs among criteria, also known as Pareto optimal or efficient solutions. Hence, a decision maker is needed to provide additional preference information and to identify the most satisfactory solution. Depending on the paradigm used, such information may be introduced before, during, or after the optimization process. Clearly, research and application in multiobjective optimization involve expertise in optimization as well as in decision support. This state-of-the-art survey originates from the International Seminar on Practical Approaches to Multiobjective Optimization, held in Dagstuhl Castle, Germany, in December 2006, which brought together leading experts from various contemporary multiobjective optimization fields, including evolutionary multiobjective optimization (EMO), multiple criteria decision making (MCDM) and multiple criteria decision aiding (MCDA). This book gives a unique and detailed account of the current status of research and applications in the field of multiobjective optimization. It contains 16 chapters grouped in the following 5 thematic sections: Basics on Multiobjective Optimization; Recent Interactive and Preference-Based Approaches; Visualization of Solutions; Modelling, Implementation and Applications; and Quality Assessment, Learning, and Future Challenges.

Nature-inspired Metaheuristic Algorithms

Author : Xin-She Yang
Publisher : Luniver Press
ISBN 13 : 1905986289
Total Pages : 148 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 2010 with total page 148 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.

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.

Robots and Biological Systems: Towards a New Bionics?

Author : Paolo Dario
Publisher : Springer Science & Business Media
ISBN 13 : 3642580696
Total Pages : 782 pages
Book Rating : 4.6/5 (425 download)

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Book Synopsis Robots and Biological Systems: Towards a New Bionics? by : Paolo Dario

Download or read book Robots and Biological Systems: Towards a New Bionics? written by Paolo Dario and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bionics evolved in the 1960s as a framework to pursue the development of artificial systems based on the study of biological systems. Numerous disciplines and technologies, including artificial intelligence and learningdevices, information processing, systems architecture and control, perception, sensory mechanisms, and bioenergetics, contributed to bionics research. This volume is based on a NATO Advanced Research Workshop within the Special Programme on Sensory Systems for Robotic Control, held in Il Ciocco, Italy, in June 1989. A consensus emerged at the workshop, and is reflected in the book, on the value of learning from nature in order to derive guidelines for the design of intelligent machines which operate in unstructured environments. The papers in the book are grouped into seven chapters: vision and dynamic systems, hands and tactile perception, locomotion, intelligent motor control, design technologies, interfacing robots to nervous systems, and robot societies and self-organization.