Introduction to Nature-Inspired Optimization - Image 1

£81.89

Academic Press Introduction to Nature-Inspired Optimization

Name: Introduction to Nature-Inspired Optimization
Brand: Academic Press
Price: 81.89 GBP
Availability: InStock

Operational Research

Price History & Forecast

Last 18 days • 18 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 18 days • 1 price levels

Days at Price

Price Analysis

Most common price: £82 (18 days, 100.0%)

Price range: £82 - £82

Price levels: 1 different prices over 18 days

Description

Introduction to Nature-Inspired Optimization brings together many of the innovative mathematical methods for non-linear optimization that have their origins in the way various species behave in order to optimize their chances of survival. The book describes each method, examines their strengths and weaknesses, and where appropriate, provides the MATLAB code to give practical insight into the detailed structure of these methods and how they work. Nature-inspired algorithms emulate processes that are found in the natural world, spurring interest for optimization. Lindfield/Penny provide concise coverage to all the major algorithms, including genetic algorithms, artificial bee colony algorithms, ant colony optimization and the cuckoo search algorithm, among others. This book provides a quick reference to practicing engineers, researchers and graduate students who work in the field of optimization. Applies concepts in nature and biology to develop new algorithms for nonlinear optimization Offers working MATLAB® programs for the major algorithms described, applying them to a range of problems Provides useful comparative studies of the algorithms, highlighting their strengths and weaknesses Discusses the current state-of-the-field and indicates possible areas of future development

Product Specifications

Brand: Academic Press
Format: Paperback
ASIN: 0128036362
Category: Books > Subjects > Business, Finance & Law > Management > Operational Research
Domain: Amazon UK
Release Date: 25 August 2017
Listed Since: 22 February 2017

Barcode

No barcode data available

Academic Press Introduction to Nature-Inspired Optimization

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Nature-Inspired Optimization Algorithms

Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications: 927 (Studies in Computational Intelligence, 927)

Nature-Inspired Computing and Optimization: Theory and Applications: 10 (Modeling and Optimization in Science and Technologies, 10)

Applied Nature-Inspired Computing: Algorithms and Case Studies (Springer Tracts in Nature-Inspired Computing)

Engineering Optimization: An Introduction with Metaheuristic Applications

Intelligent Optimisation with the Bees Algorithm: Concepts and Applications (Springer Series in Advanced Manufacturing)

Swarm Intelligence Algorithms: A Tutorial: 1

Swarm Intelligence Algorithms: A Tutorial

Swarm Intelligence: Principles, Advances, and Applications

Optimization: Algorithms and Applications

Swarm Intelligence: Principles, Advances, and Applications

Springer Biologically-Inspired Optimisation Methods - Vol 210

Computational Intelligence, Optimization and Inverse Problems with Applications in Engineering

Swarm Intelligence and Bio-Inspired Computation: Theory and Applications

Nature-Inspired Computing: Physics and Chemistry-Based Algorithms

Swarm Intelligence and Bio-Inspired Computation..: Theory and Applications (Elsevier Insights)

Computational Intelligence Paradigms: Theory & Applications using MATLAB

Nature-Inspired Computation and Swarm Intelligence: Algorithms, Theory and Applications

Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB

Swarm Intelligence Algorithms (Two Volume Set)

Metaheuristic Computation with MATLAB®

Swarm Intelligence Algorithms (Two Volume Set)

Metaheuristics: From Design to Implementation

Evolutionary Optimization Algorithms