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2018 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Advanced Optimization by Nature-Inspired Algorithms

herausgegeben von: Omid Bozorg-Haddad

Verlag: Springer Singapore

Buchreihe : Studies in Computational Intelligence

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

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Über dieses Buch

This book, compiles, presents, and explains the most important meta-heuristic and evolutionary optimization algorithms whose successful performance has been proven in different fields of engineering, and it includes application of these algorithms to important engineering optimization problems. In addition, this book guides readers to studies that have implemented these algorithms by providing a literature review on developments and applications of each algorithm. This book is intended for students, but can be used by researchers and professionals in the area of engineering optimization.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
In this chapter, some general knowledge relative to the realm of nature-inspired optimization algorithms (NIOA) is introduced. The desirable merits of these intelligent algorithms and their initial successes in many fields have inspired researchers to continuously develop such revolutionary algorithms and implement them to solve various real-world problems. Such a truly interdisciplinary environment of the research and development provides rewarding opportunities for scientific breakthrough and technology innovation. After a brief introduction to computational intelligence and its application in optimization problems, the history of the NIOA was reviewed. The relevant algorithms were then categorized in different manners. Finally, one the most groundbreaking theorems regarding the nature-inspired optimization techniques was briefly discussed.
Babak Zolghadr-Asli, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 2. Cat Swarm Optimization (CSO) Algorithm
Abstract
In this chapter, a brief literature review of the Cat Swarm Optimization (CSO) algorithm is presented. Then the natural process, the basic CSO algorithm iteration procedure, and the computational steps of the algorithm are detailed. Finally, a pseudo code of CSO algorithm is also presented to demonstrate the implementation of this optimization technique.
Mahdi Bahrami, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 3. League Championship Algorithm (LCA)
Abstract
This chapter briefly describes the league championship algorithm (LCA) as one of the new evolutionary algorithms. In this chapter, a brief literature review of LCA is first presented; and then the procedure of holding a common league in sports and its rules are described. Finally, a pseudo code of LCA is presented.
Hossein Rezaei, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 4. Anarchic Society Optimization (ASO) Algorithm
Abstract
Due to limited resources and equipment in most engineering projects, it is necessary to use optimization techniques. Older optimization techniques, including derivative and other mathematical methods may not be practical to new complex problems. Therefore new optimization algorithms are needed. In the past decades many algorithms were developed and used for different optimization problems, which can be divided into three categories including classic, evolutionary and heuristic algorithms. The evolutionary and heuristic algorithms which are used widely in recent years are based on animals’ life. In this chapter, one of the heuristic algorithms named Anarchic Society Optimization (ASO) algorithm based on human societies, is introduced. After a brief literature review of the ASO algorithm, more technical details on this method and its performance are described.
Atiyeh Bozorgi, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 5. Cuckoo Optimization Algorithm (COA)
Abstract
The cuckoo optimization algorithm (COA) is used for continuous non-linear optimization. COA is inspired by the life style of a family of birds called cuckoo. These birds’ life style, egg laying features, and breeding are the basis of the development of this optimization algorithm. Like other evolutionary approaches, COA is started by an initial population. There are two types of the population of cuckoos in different societies: mature cuckoos and eggs. The basis of the algorithm is made by the attempt to survive. While competing for being survived, some of them are demised. The survived cuckoos immigrate to better areas and start reproducing and laying eggs. Finally, the survived cuckoos are converged in a way that there is a cuckoo society with the same profit rate.
Saba Jafari, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 6. Teaching-Learning-Based Optimization (TLBO) Algorithm
Abstract
This chapter is prepared to describe the Teaching-Learning-Based Optimization (TLBO) algorithm, a novel metaheuristic optimization method inspired by an educational classroom environment. It has an interesting exclusivity which may facilitate the solution process of optimization problems. In this chapter, a brief literature review of the TLBO algorithm is first presented. Then, the working process and two phases of TLBO (teacher phase and learner phase) are depicted. Eventually, a pseudocode of TLBO is presented.
Parisa Sarzaeim, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 7. Flower Pollination Algorithm (FPA)
Abstract
This chapter is designed to describe the flower pollination algorithm (FPA) which is a new metaheuristic algorithm. First, the FPA applications in different problems are summarized. Then, the natural pollination process and the flower pollination algorithm are described. Finally, a pseudocode of the FPA is presented.
Marzie Azad, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 8. Krill Herd Algorithm (KHA)
Abstract
The krill herd algorithm (KHA) is a new metaheuristic search algorithm based on simulating the herding behavior of krill individuals using a Lagrangian model. This algorithm was developed by Gandomi and Alavi (2012) and the preliminary studies illustrated its potential in solving numerous complex engineering optimization problems. In this chapter, the natural process behind a standard KHA is described.
Babak Zolghadr-Asli, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 9. Grey Wolf Optimization (GWO) Algorithm
Abstract
This chapter describes the grey wolf optimization (GWO) algorithm as one of the new meta-heuristic algorithms. First, a brief literature review is presented and then the natural process of the GWO algorithm is described. Also, the optimization process and a pseudo code of the GWO algorithm are presented in this chapter.
Hossein Rezaei, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 10. Shark Smell Optimization (SSO) Algorithm
Abstract
In this chapter, the shark smell optimization (SSO) algorithm is presented, which is inspired by the shark’s ability to hunt based on its strong smell sense. In Sect. 10.1, an overview of the implementations of SSO is presented. The underlying idea of the algorithm is discussed in Sect. 10.2. The mathematical formulation and a pseudo-code are presented in Sects. 10.3 and 10.4, respectively. Section 10.5 is devoted to conclusion.
Sahar Mohammad-Azari, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 11. Ant Lion Optimizer (ALO) Algorithm
Abstract
This chapter introduces the ant lion optimizer (ALO), which mimics the hunting behavior of antlions in the larvae stage. Specifically, this chapter includes literature review, details of the ALO algorithm, and a pseudo-code for its implementation.
Melika Mani, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 12. Gradient Evolution (GE) Algorithm
Abstract
In this chapter, a meta-heuristic optimization algorithm named gradient evolution (GE) is discussed, which is based on a gradient search method. First, the GE algorithm and the underlying idea are introduced and its applications in some studies are reviewed. Then, the mathematical formulation and a pseudo-code of GE are discussed. Finally, the conclusion is presented.
Mehri Abdi-Dehkordi, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 13. Moth-Flame Optimization (MFO) Algorithm
Abstract
This chapter introduces the Moth-Flame Optimization (MFO) algorithm, along with its applications and variations. The basic steps of the algorithm are explained in detail and a flowchart is represented. In order to better understand the algorithm, a pseudocode of the MFO is also included.
Mahdi Bahrami, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 14. Crow Search Algorithm (CSA)
Abstract
The crow search algorithm (CSA) is novel metaheuristic optimization algorithm, which is based on simulating the intelligent behavior of crow flocks. This algorithm was introduced by Askarzadeh (2016) and the preliminary results illustrated its potential to solve numerous complex engineering-related optimization problems. In this chapter, the natural process behind a standard CSA is described at length.
Babak Zolghadr-Asli, Omid Bozorg-Haddad, Xuefeng Chu
Chapter 15. Dragonfly Algorithm (DA)
Abstract
The dragonfly algorithm (DA) is a new metaheuristic optimization algorithm, which is based on simulating the swarming behavior of dragonfly individuals. This algorithm was developed by Mirjalili (2016) and the preliminary studies illustrated its potential in solving numerous benchmark optimization problems and complex computational fluid dynamics (CFD) optimization problems. In this chapter, the natural process behind a standard DA is described at length.
Babak Zolghadr-Asli, Omid Bozorg-Haddad, Xuefeng Chu
Metadaten
Titel
Advanced Optimization by Nature-Inspired Algorithms
herausgegeben von
Omid Bozorg-Haddad
Copyright-Jahr
2018
Verlag
Springer Singapore
Electronic ISBN
978-981-10-5221-7
Print ISBN
978-981-10-5220-0
DOI
https://doi.org/10.1007/978-981-10-5221-7

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