This book presents state-of-the-art technical contributions based around one of the most successful evolutionary optimization algorithms published to date: Harmony Search. Contributions span from novel technical derivations of this algorithm to applications in the broad fields of civil engineering, energy, transportation & mobility and health, among many others and focus not only on its cross-domain applicability, but also on its core evolutionary operators, including elements inspired from other meta-heuristics.

The global scientific community is witnessing an upsurge in groundbreaking, new advances in all areas of computational intelligence, with a particular flurry of research focusing on evolutionary computation and bio-inspired optimization. Observed processes in nature and sociology have provided the basis for innovative algorithmic developments aimed at leveraging the inherent capability to adapt characterized by various animals, including ants, fireflies, wolves and humans. However, it is the behavioral patterns observed in music composition that motivated the advent of the Harmony Search algorithm, a meta-heuristic optimization algorithm that over the last decade has been shown to dominate other solvers in a plethora of application scenarios.

The book consists of a selection of the best contributions presented at ICHSA, a major biannual event where leading global experts on meta-heuristic optimization present their latest findings and discuss the past, present, and future of the exciting field of Harmony Search optimization. It provides a valuable reference resource for researchersworking in the field of optimization meta-heuristics, and a solid technical base for frontline investigations around this algorithm.

Offers a up-to-date reference for the research community working on Harmony Search Presents views from worldwide experts on evolution- and nature-inspired heuristics Describes novel applications of Harmony Search in industry and civil engineering Considers new research directions, including the hybridization of Harmony Search with Big Data predictive models

Autorentext

Dr. Javier (Javi) Del Ser received his first PhD degree (cum laude) in Electrical Engineering from the University of Navarra (Spain) in October 2006, and a second PhD degree (summa cum laude) in Signal Processing from the University of Alcala (Spain) in May 2013. From 2003 to 2005 he was a teaching assistant at TECNUN (University of Navarra, Spain). From August to December 2007 he was a visiting scholar at University of Delaware (USA), and from February to September 2008 he was an associate professor at the University of Mondragon, Spain. In October 2008 he joined Fundacion Robotiker as a senior research scientist at the Telecom Unit.

Currently Javier is the leading researcher of the OPTIMA area at TECNALIA RESEARCH & INNOVATION, an adjunct lecturer at the University of the Basque Country (EHU/UPV)and an external researcher at Basque Centre of Apllied Mathematics (BCAM). His research interests are focused on artificial intelligence, machine learning and in general, dataanalytics for paradigms arising in diverse fields such as energy, telecommunications, mobility and operations research, among many others. In these fields he has published more than 140 technical papers and conferences, co-supervised 6 PhD and 13 M.Sc. theses, edited 2 books and invented 4 patents. He has been granted twice with the Torres Quevedo grant from the Spanish Ministry of Science and Innovation (2007 & 2009), and is a senior member of the IEEE. Recently he has been awarded the Talent of Bizkaia prize for his outstanding professional curriculum.

Inhalt

Sensitivity Analysis on Migration Parameters of Parallel Harmony Search.- Multi-layered Harmony Search Algorithm: Introduction of a Novel and Ecient Structure.- Application of Self-adaptive Method in Multi-objective Harmony Search Algorithm.- A Comparative Study of Exploration Ability of Harmony Search Algorithms.- The Extraordinary Particle Swarm Optimization and its Application in Constrained Engineering Problems.- Metaheuristic based Optimization for Tuned Mass Dampers using Frequency Domain Responses.