Multi-Objective Optimization using Artificial Intelligence Techniques

Multi-Objective Optimization using Artificial Intelligence Techniques
Author: Seyedali Mirjalili
Publisher: Springer
Total Pages: 58
Release: 2019-07-24
Genre: Technology & Engineering
ISBN: 3030248356
GET BOOK

Download Multi-Objective Optimization using Artificial Intelligence Techniques Book in PDF, Epub and Kindle

This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Multi-Objective Optimization using Artificial Intelligence Techniques

Multi-Objective Optimization using Artificial Intelligence Techniques
Author: Seyedali Mirjalili
Publisher: Springer
Total Pages: 58
Release: 2019-10-10
Genre: Computers
ISBN: 9783030248345
GET BOOK

Download Multi-Objective Optimization using Artificial Intelligence Techniques Book in PDF, Epub and Kindle

This book focuses on the most well-regarded and recent nature-inspired algorithms capable of solving optimization problems with multiple objectives. Firstly, it provides preliminaries and essential definitions in multi-objective problems and different paradigms to solve them. It then presents an in-depth explanations of the theory, literature review, and applications of several widely-used algorithms, such as Multi-objective Particle Swarm Optimizer, Multi-Objective Genetic Algorithm and Multi-objective GreyWolf Optimizer Due to the simplicity of the techniques and flexibility, readers from any field of study can employ them for solving multi-objective optimization problem. The book provides the source codes for all the proposed algorithms on a dedicated webpage.

Multi-Objective Machine Learning

Multi-Objective Machine Learning
Author: Yaochu Jin
Publisher: Springer Science & Business Media
Total Pages: 657
Release: 2007-06-10
Genre: Technology & Engineering
ISBN: 3540330194
GET BOOK

Download Multi-Objective Machine Learning Book in PDF, Epub and Kindle

Recently, increasing interest has been shown in applying the concept of Pareto-optimality to machine learning, particularly inspired by the successful developments in evolutionary multi-objective optimization. It has been shown that the multi-objective approach to machine learning is particularly successful to improve the performance of the traditional single objective machine learning methods, to generate highly diverse multiple Pareto-optimal models for constructing ensembles models and, and to achieve a desired trade-off between accuracy and interpretability of neural networks or fuzzy systems. This monograph presents a selected collection of research work on multi-objective approach to machine learning, including multi-objective feature selection, multi-objective model selection in training multi-layer perceptrons, radial-basis-function networks, support vector machines, decision trees, and intelligent systems.

Multi-Objective Optimization in Theory and Practice I: Classical Methods

Multi-Objective Optimization in Theory and Practice I: Classical Methods
Author: Andre A. Keller
Publisher: Bentham Science Publishers
Total Pages: 296
Release: 2017-12-13
Genre: Technology & Engineering
ISBN: 1681085682
GET BOOK

Download Multi-Objective Optimization in Theory and Practice I: Classical Methods Book in PDF, Epub and Kindle

Multi-Objective Optimization in Theory and Practice is a traditional two-part approach to solving multi-objective optimization (MOO) problems namely the use of classical methods and evolutionary algorithms. This first book is devoted to classical methods including the extended simplex method by Zeleny and preference-based techniques. This part covers three main topics through nine chapters. The first topic focuses on the design of such MOO problems, their complexities including nonlinearities and uncertainties, and optimality theory. The second topic introduces the founding solving methods including the extended simplex method to linear MOO problems and weighting objective methods. The third topic deals with particular structures of MOO problems, such as mixed-integer programming, hierarchical programming, fuzzy logic programming, and bimatrix games. Multi-Objective Optimization in Theory and Practice is a user-friendly book with detailed, illustrated calculations, examples, test functions, and small-size applications in Mathematica® (among other mathematical packages) and from scholarly literature. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science, and mathematics degree programs.

Multi-Objective Optimization using Evolutionary Algorithms

Multi-Objective Optimization using Evolutionary Algorithms
Author: Kalyanmoy Deb
Publisher: John Wiley & Sons
Total Pages: 540
Release: 2001-07-05
Genre: Mathematics
ISBN: 9780471873396
GET BOOK

Download Multi-Objective Optimization using Evolutionary Algorithms Book in PDF, Epub and Kindle

Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.

Computational Intelligence in Optimization

Computational Intelligence in Optimization
Author: Yoel Tenne
Publisher: Springer Science & Business Media
Total Pages: 424
Release: 2010-06-30
Genre: Technology & Engineering
ISBN: 3642127754
GET BOOK

Download Computational Intelligence in Optimization Book in PDF, Epub and Kindle

This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

Multi-Objective Optimization in Computational Intelligence: Theory and Practice

Multi-Objective Optimization in Computational Intelligence: Theory and Practice
Author: Thu Bui, Lam
Publisher: IGI Global
Total Pages: 496
Release: 2008-05-31
Genre: Technology & Engineering
ISBN: 1599045001
GET BOOK

Download Multi-Objective Optimization in Computational Intelligence: Theory and Practice Book in PDF, Epub and Kindle

Multi-objective optimization (MO) is a fast-developing field in computational intelligence research. Giving decision makers more options to choose from using some post-analysis preference information, there are a number of competitive MO techniques with an increasingly large number of MO real-world applications. Multi-Objective Optimization in Computational Intelligence: Theory and Practice explores the theoretical, as well as empirical, performance of MOs on a wide range of optimization issues including combinatorial, real-valued, dynamic, and noisy problems. This book provides scholars, academics, and practitioners with a fundamental, comprehensive collection of research on multi-objective optimization techniques, applications, and practices.

Applications of Multi-objective Evolutionary Algorithms

Applications of Multi-objective Evolutionary Algorithms
Author: Carlos A. Coello Coello
Publisher: World Scientific
Total Pages: 792
Release: 2004
Genre: Computers
ISBN: 9812561064
GET BOOK

Download Applications of Multi-objective Evolutionary Algorithms Book in PDF, Epub and Kindle

- 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

2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)

2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)
Author: IEEE Staff
Publisher:
Total Pages:
Release: 2021-07-02
Genre:
ISBN: 9781665448437
GET BOOK

Download 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI) Book in PDF, Epub and Kindle

2021 IEEE the 4th International Conference on Big Data and Artificial Intelligence (BDAI 2021) will be held at Ocean University of China, Qingdao, China during July 02 04, 2021 The aim of BDAI 2021 is to set up a forum for scholars, researchers & scientists to present their latest research work and results of in related fields of Big Data and Artificial Intelligence

Bio-Inspired Systems: Computational and Ambient Intelligence

Bio-Inspired Systems: Computational and Ambient Intelligence
Author: Joan Cabestany
Publisher: Springer
Total Pages: 1403
Release: 2009-06-05
Genre: Computers
ISBN: 3642024785
GET BOOK

Download Bio-Inspired Systems: Computational and Ambient Intelligence Book in PDF, Epub and Kindle

This volume presents the set of final accepted papers for the tenth edition of the IWANN conference “International Work-Conference on Artificial neural Networks” held in Salamanca (Spain) during June 10–12, 2009. IWANN is a biennial conference focusing on the foundations, theory, models and applications of systems inspired by nature (mainly, neural networks, evolutionary and soft-computing systems). Since the first edition in Granada (LNCS 540, 1991), the conference has evolved and matured. The list of topics in the successive Call for - pers has also evolved, resulting in the following list for the present edition: 1. Mathematical and theoretical methods in computational intelligence. C- plex and social systems. Evolutionary and genetic algorithms. Fuzzy logic. Mathematics for neural networks. RBF structures. Self-organizing networks and methods. Support vector machines. 2. Neurocomputational formulations. Single-neuron modelling. Perceptual m- elling. System-level neural modelling. Spiking neurons. Models of biological learning. 3. Learning and adaptation. Adaptive systems. Imitation learning. Reconfig- able systems. Supervised, non-supervised, reinforcement and statistical al- rithms. 4. Emulation of cognitive functions. Decision making. Multi-agent systems. S- sor mesh. Natural language. Pattern recognition. Perceptual and motor functions (visual, auditory, tactile, virtual reality, etc.). Robotics. Planning motor control. 5. Bio-inspired systems and neuro-engineering. Embedded intelligent systems. Evolvable computing. Evolving hardware. Microelectronics for neural, fuzzy and bio-inspired systems. Neural prostheses. Retinomorphic systems. Bra- computer interfaces (BCI). Nanosystems. Nanocognitive systems.