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2017 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Heuristic Search

The Emerging Science of Problem Solving

verfasst von: Prof. Saïd Salhi

Verlag: Springer International Publishing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book aims to provide a general overview of heuristic search, to present the basic steps of the most popular heuristics, and to stress their hidden difficulties as well as their opportunities. It provides a comprehensive understanding of Heuristic search, the applications of which are now widely used in a variety of industries including engineering, finance, sport, management and medicine. It intends to aid researchers and practitioners in solving complex combinatorial and global optimisation problems, and spark interest in this exciting decision science-based subject. It will provide the reader with challenging and lively methodologies through which they will be able to design and analyse their own techniques

Inhaltsverzeichnis

Frontmatter
1. Introduction
Abstract
In this introductory chapter, I first give an overview of heuristics in which are highlighted some possible methodological approaches, the needs for heuristics in practice and their complexity along with their performance evaluation. A loose classification that will be used throughout this book is also provided.
Saïd Salhi
2. Improvement-Only Heuristics
Abstract
In this chapter, I only consider those approaches that accept an improving solution that can be generated from one iteration to the next. Basic neighbourhood definitions with simple examples are first provided. This is followed by the descent or greedy method while emphasising some of its drawbacks. I then present some of the efficient hill climbing methods commonly used such as GRASP, a simple composite heuristic, multi-level, variable neighbourhood and perturbation schemes. A brief on large neighbourhood search, iterated local search and guided local search will also be given.
Saïd Salhi
3. Not Necessary Improving Heuristics
Abstract
In this chapter, I discuss those popular heuristics that improve the solutions by not necessarily restricting the next move to be an improving move. Note that allowing such inferior solutions to be chosen need to be controlled as the search may diverge to even worse solutions. Some of the well-established techniques that are based on this concept are covered here. I concentrate on simulated annealing, threshold accepting and tabu search with an emphasis on their respective key elements.
Saïd Salhi
4. Population-Based Heuristics
Abstract
In this chapter, I present those methods that generate a set of solutions from one iteration (known as generation) to the next instead of one only solution at a time. I give an overview of the most commonly used approaches in this particular class. These include genetic algorithms, harmony search, scatter search, differential evolution, ant systems, bees algorithm and particle swarm. The first four can be categorised under evolutionary computing, whereas the other three under swarm intelligence. For completeness, I also briefly mention other ones such as path relinking, heuristic cross-entropy, artificial immune systems, plant propagation and the psycho-clonal algorithm. Note that some of these heuristics can also be considered under the previous class, but to emphasise the power of having multiple solutions simultaneously, these are conveniently categorised under this class instead.
Saïd Salhi
5. Hybridisation Search
Abstract
The simplest way of hybridisation is obviously to put one heuristic after another. A more exciting approach is to extend such integration to cater for intermediate stages of the search by hybridising rules whenever needed within the search. In this chapter three main approaches are described, namely the hybridisation of heuristics with heuristics, the hybridisation of heuristics within exact methods and finally the hybridisation of exact methods within heuristics. The last two are also known to be called matheuristics. A special hybridisation that deals with data mining is also proposed.
Saïd Salhi
6. Implementation Issues
Abstract
In this chapter, I first provide some implementation issues that could arise when designing a heuristic. There are many ways of enhancing the implementation of a given heuristic so to improve its efficiency. Some key items that I found to be useful will be presented. Other related issues that deal with fuzzy logic and multi-objective optimisation are also discussed.
Saïd Salhi
7. Applications, Conclusion and Research Challenges
Abstract
In this chapter, I present some real-life applications that are addressed by metaheuristic approaches followed by a brief on those general optimisation problems that are intensively studied by academics. This is followed by a summary of the overall conclusions of this monograph. Finally, some research avenues that I believe to be exciting and worth examining are also discussed.
Saïd Salhi
Backmatter
Metadaten
Titel
Heuristic Search
verfasst von
Prof. Saïd Salhi
Copyright-Jahr
2017
Electronic ISBN
978-3-319-49355-8
Print ISBN
978-3-319-49354-1
DOI
https://doi.org/10.1007/978-3-319-49355-8

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