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Sinan Melih Nigdeli, Joong Hoon Kim, Gebrail Bekdaş, Anupam Yadav e.a.

Proceedings of 6th International Conference on Harmony Search, Soft Computing and Applications

Name: Proceedings of 6th International Conference on Harmony Search, Soft Computing and Applications
Author: Sinan Melih Nigdeli

ICHSA 2020, Istanbul

Paperback Engels 2020 9789811586026

240,99

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Samenvatting

This book covers different aspects of real-world applications of optimization algorithms. It provides insights from the Sixth International Conference on Harmony Search, Soft Computing and Applications held at Istanbul University, Turkey, in July 2020. Harmony Search (HS) is one of the most popular metaheuristic algorithms, developed in 2001 by Prof. Joong Hoon Kim and Prof. Zong Woo Geem, that mimics the improvisation process of jazz musicians to seek the best harmony. The book consists of research articles on novel and newly proposed optimization algorithms; the theoretical study of nature-inspired optimization algorithms; numerically established results of nature-inspired optimization algorithms; and real-world applications of optimization algorithms and synthetic benchmarking of optimization algorithms.

Specificaties

ISBN13:9789811586026

Taal:Engels

Bindwijze:paperback

Uitgever:Springer Nature Singapore

Serie:Advances in Intelligent Systems and Computing

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Inhoudsopgave

Performance Quantification of Search Operators in Hybrid Harmony Search Algorithms.- Harmony Search Algorithms for Optimizing Extreme Learning Machines.- Freestyle Rap Harmony Search (FRHS) for Engineering Problem Optimization.- Improvement of Voltage Profile and Loss Reduction based on Optimal Placement and Sizing of Renewable Distributed Generations using 4-Rule Harmony Search Algorithm.- Control of a jacket platform under wave load using ATMD and optimization by HSA.- Hybrid Harmony Search Algorithm for Optimum Design of Vibration Absorber System for Adjacent Buildings.- The Effect of Initial Values on Metaheuristic-based Optimum Design of Tuned Mass Dampers.- The Comparison of Classical and Artificial Neural Network-based Formulations for Tuned Mass Damper Optimization. 

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