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Nature-inspired methods for metaheur...

Bennis, Fouad.

Nature-inspired methods for metaheuristics optimizationalgorithms and applications in science and engineering /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Nature-inspired methods for metaheuristics optimizationedited by Fouad Bennis, Rajib Kumar Bhattacharjya.
其他題名:	algorithms and applications in science and engineering /
其他作者:	Bennis, Fouad.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xiii, 502 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Natural computation.
電子資源:	https://doi.org/10.1007/978-3-030-26458-1
ISBN:	9783030264581$q(electronic bk.)

Nature-inspired methods for metaheuristics optimizationalgorithms and applications in science and engineering /
Nature-inspired methods for metaheuristics optimizationalgorithms and applications in science and engineering /[electronic resource] :edited by Fouad Bennis, Rajib Kumar Bhattacharjya. - Cham :Springer International Publishing :2020. - xiii, 502 p. :ill., digital ;24 cm. - Modeling and optimization in science and technologies,v.162196-7326 ;. - Modeling and optimization in science and technologies ;v.2..

Part I. Algorithms: 1. Genetic algorithms: A mature bio-inspired optimization technique for difficult problems -- 2. Introduction to Genetic Algorithm with a Simple Analogy -- 3. Interactive genetic algorithm to collect user perceptions. Application to the design of stemmed glasses -- 4. Differential Evolution and its application in Identification of Virus Release Location in a Sewer Line -- 5. Artiﬁcial Bee Colony Algorithm and An Application to Software Defect Prediction -- 6. Firefly Algorithm and its Applications in Engineering Optimization -- 7. Introduction to Shuffled Frog Leaping Algorithm and its Sensitivity to the Parameters of the Algorithm -- 8. Groundwater Management using Coupled Analytic Element based Transient Groundwater Flow and Optimization Model -- 9. Investigation of Bacterial Foraging Algorithm applied for PV parameter estimation, Selective harmonic elimination in inverters and optimal power flow for stability -- 10. Application of artificial immune system in Optimal Design of Irrigation Canal -- 11. Biogeography Based Optimization for Water Pump Switching Problem -- 12. Introduction to Invasive Weed Optimization Method -- 13. Single-Level Production Planning in Petrochemical Industries using Novel Computational Intelligence Algorithms -- 14. A Multi-Agent platform to support knowledge based modelling in engineering Design -- Part II. Applications: 15. Synthesis of reference trajectories for humanoid robot supported by genetic algorithm -- 16. Linked Simulation Optimization Model for Evaluation of Optimal Bank Protection Measures -- 17. A GA Based Iterative Model for Identification of Unknown Groundwater Pollution Sources Considering Noisy Data -- 18. Efficiency of Binary Coded Genetic Algorithm in Stability Analysis of an Earthen Slope -- 19. Corridor allocation as a constrained optimization problem using a permutation-based multi-objective genetic algorithm -- 20. The constrained single-row facility layout problem with repairing mechanisms -- 21. Geometric size optimization of annular step fin array for heat transfer by natural convection -- 22. Optimal control of saltwater intrusion in coastal aquifers using analytical approximation based on density dependent flow correction -- 23. Dynamic Nonlinear Active Noise Control. A Multi-Objective Evolutionary Computing Approach -- 24. Scheduling of Jobs on Dissimilar Parallel Machine using Computational Intelligence Algorithms -- 25. Branch-and-Bound Method for Just-in-Time Optimization of Radar Search Patterns -- 26. Optimization of the GIS based DRASTIC model for Groundwater Vulnerability Assessment.

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.

ISBN: 9783030264581$q(electronic bk.)

Standard No.: 10.1007/978-3-030-26458-1doiSubjects--Topical Terms:

339160
Natural computation.

LC Class. No.: QA76.9.N37 / N388 2020

Dewey Class. No.: 006.382

Nature-inspired methods for metaheuristics optimizationalgorithms and applications in science and engineering /
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