Keywords

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1 Introduction

About 20 years ago, the term “Computational Intelligence” (CI), coined by Bezdek (1992, 1994), triggered the development of a new field dedicated to computer-based intelligence which can be regarded as a timely intent to avoid some of the tough issues. In principle, CI consists of any science-supported approaches and technologies for analyzing, creating, and developing intelligent systems. The broad usage of this term was formalized by the IEEE Neural Network Council and the IEEE World Congress on Computational Intelligence in Orlando, Florida in the summer of 1994. With the advances of many advanced theories and methodologies, many obstacles that may previously hindered the development of CI research have now been overcome. During the past two decades, as evidenced by the promising results of numerous researches, CI has enjoyed a widely acceptance and an unprecedented popularity. By applying it in various application settings, CI has opened many brand new dimensions for scientific research.

1.1 Traditional CI

Traditional CI primarily concentrates on artificial neural network (ANN), fuzzy logic (FL), multi-agent system (MAS), evolutionary algorithms (EA) [e.g., genetic algorithm (GA), genetic programming (GP), evolutionary programming (EP), and evolutionary strategy (ES)], artificial immune systems (AIS), simulated annealing (SA), Tabu search (TS), as well as two variants of swarm intelligence (SI), i.e., ant colony optimization (ACO) and particle swarm optimization (PSO). In the literature, there are thousands of (if not more) books, conference proceedings, and edited monographs are devoted to CI and its corresponding vast amount of applications, e.g., (Wang and Kusiak 2001; Engelbrecht 2007; Fink and Rothlauf 2008; Marwala 2009, 2010, 2012; Rutkowski 2008; Sumathi and Paneerselvam 2010; Marwala and Lagazio 2011; Xing and Gao 2014; Chatterjee and Siarry 2013; Yang 2008, 2010a; Eberhart and Shi 2007; Fulcher and Jain 2008; Mumford and Jain 2009; Michalewicz 1996), to name just a few.

1.2 Innovative CI

Innovative CI, unlike its counterparts, i.e., highly developed and refined traditional CI, is a new CI category introduced by the authors of this book. Although most innovative CI algorithms introduced in this book hold considerable promise, majority of them are still in their infancy. There are currently very few books specifically dedicated to these novel CI paradigms. Therefore it is the authors’ hope that this book will inspire other far better qualified researchers to bring these new CI family members to their full potential.

2 Organization of the Book

In this book, we will cover a vast amount of innovative algorithms (more specifically, 134 in total) that all involving some aspect of CI, but each one taking a somewhat pragmatic view. In order to present a clear picture over these approaches, we have organized them into four main classes, namely, biology-, physics-, chemistry-, and mathematics-based CI algorithms.

2.1 Biology-based CI Algorithms

Briefly, biology can be defined as a comprehensive science concerning all functions of living systems (Glaser 2012). From an evolutionary process point of view, biological systems possess many appealing characteristics such as sophistication, robustness, and adaptability (Floreano and Mattiussi 2008). These features represent a strong motivation for imitating the mechanisms of natural evolution in an attempt to create CI algorithms with merits comparable to those of biological systems.

In this class, we have covered 99 novel biology-based CI algorithms which are outlined as follows. Each algorithm’s original reference has been attached for readers’ convenience to trace their origination.

  • Amoeboid Organism Algorithm (Zhang et al. 2013).

  • Artificial Bee Colony (Karaboga and Basturk 2007).

  • Artificial Beehive Algorithm (Muñoz et al. 2009).

  • Artificial Fish Swarm Algorithm (Li 2003).

  • Artificial Searching Swarm Algorithm (Chen 2009).

  • Artificial Tribe Algorithm (Chen et al. 2012).

  • Backtracking Search Algorithm (Civicioglu 2013).

  • Bacterial Colony Chemotaxis (Müller et al. 2002).

  • Bacterial Colony Optimization (Niu and Wang 2012).

  • Bacterial Foraging Algorithm (Passino 2002).

  • Bar Systems (Acebo and Rosa 2008).

  • Bat Algorithm (Yang 2010b).

  • Bat Intelligence (Malakooti et al. 2013).

  • Bean Optimization Algorithm (Zhang et al. 2010).

  • Bee Colony Optimization (Teodorović and Dell’Orco 2005).

  • Bee Colony-inspired Algorithm (Häckel and Dippold 2009).

  • Bee Swarm Optimization (Akbari et al. 2009).

  • Bee System (Sato and Hagiwara 1997).

  • BeeHive (Wedde et al. 2004).

  • Bees Algorithm (Pham et al. 2006).

  • Bees Life Algorithm (Bitam and Mellouk 2013).

  • Biogeography-based optimization (Simon 2008).

  • Bioluminescent Swarm Optimization (Oliveira et al. 2011).

  • Bionic Optimization (Steinbuch 2011).

  • Blind, Naked Mole-Rats (Taherdangkoo et al. 2012).

  • Brain Storm Optimization (Shi 2011).

  • Bumblebees Algorithm (Comellas and Martínez-Navarro 2009).

  • Cat Swarm Optimization (Chu and Tsai 2007).

  • Clonal Selection Algorithm (Castro and Zuben 2000).

  • Cockroach Swarm Optimization (Chen and Tang 2010).

  • Collective Animal Behaviour (Cuevas et al. 2013b).

  • Cuckoo Optimization Algorithm (Rajabioun 2011).

  • Cuckoo Search (Yang and Deb 2009).

  • Cultural Algorithm (Reynolds 1994).

  • Differential Search (Civicioglu 2012).

  • Dove Swarm Optimization (Su et al. 2009).

  • Eagle Strategy (Yang and Deb 2010).

  • Firefly Algorithm (Łukasik and Żak 2009).

  • Fireworks Algorithm (Tan and Zhu 2010).

  • Fish School Search (Bastos-Filho et al. 2008).

  • FlockbyLeader (Bellaachia and Bari 2012).

  • Flocking-based Algorithm (Cui et al. 2006).

  • Flower Pollinating Algorithm (Yang 2012).

  • Frog Calling Algorithm (Mutazono et al. 2012).

  • Fruit Fly Optimization Algorithm (Pan 2012).

  • Glowworm Swarm Optimization (Krishnanand and Ghose 2005).

  • Goose Optimization Algorithm (Sun and Lei 2009).

  • Great Deluge Algorithm (Dueck 1993).

  • Grenade Explosion Algorithm (Ahrari et al. 2009).

  • Group Escaping Algorithm (Min and Wang 2010).

  • Group Leaders Optimization Algorithm (Daskin and Kais 2011).

  • Group Search Optimizer (He et al. 2006).

  • Harmony Elements Algorithm (Cui et al. 2008).

  • Harmony Search (Geem et al. 2001).

  • Honeybee Social Foraging (Quijano and Passino 2010).

  • Honeybees Mating Optimization (Abbass 2001).

  • Human Group Formation (Thammano and Moolwong 2010).

  • Hunting Search (Oftadeh et al. 2010).

  • Imperialist Competition Algorithm (Atashpaz-Gargari and Lucas 2007).

  • Invasive Weed Optimization (Mehrabian and Lucas 2006).

  • Krill Herd (Gandomi and Alavi 2012).

  • League Championship Algorithm (Kashan 2009).

  • Melody Search (Ashrafi and Dariane 2011).

  • Membrane Algorithm (Nishida 2005).

  • Method of Musical Composition (Mora-Gutiérrez et al. 2012).

  • Migrating Birds Optimization (Duman et al. 2012).

  • Mine Blast Algorithm (Sadollah et al. 2012).

  • Monkey Search (Mucherino and Seref 2007).

  • Mosquito Host-Seeking Algorithm (Feng et al. 2009).

  • OptBees (Maia et al. 2012).

  • Oriented Search Algorithm (Zhang et al. 2008).

  • Paddy Field Algorithm (Premaratne et al. 2009).

  • Photosynthetic Algorithm (Murase 2000).

  • Population Migration Algorithm (Zhang et al. 2009).

  • Roach Infestation Optimization (Havens et al. 2008).

  • Saplings Growing Up Algorithm (Karci and Alatas 2006).

  • Seeker Optimization Algorithm (Dai et al. 2007).

  • Self-Organizing Migrating Algorithm (Davendra et al. 2013).

  • Shark-Search Algorithm (Hersovici et al. 1998).

  • Sheep Flock Heredity Model (Nara et al. 1999).

  • Shuffled Frog Leaping Algorithm (Eusuff and Lansey 2003).

  • Simple Optimization (Hasançebi and Azad 2012).

  • Simulated Bee Colony (McCaffrey and Dierking 2009).

  • Slime Mould Algorithm (Shann 2008).

  • Social Emotional Optimization Algorithm (Wei et al. 2010).

  • Social Spider Optimization Algorithm (Cuevas et al. 2013a).

  • Society and Civilization Algorithm (Ray and Liew 2003).

  • Stem Cells Algorithm (Taherdangkoo et al. 2011).

  • Stochastic Focusing Search (Zheng et al. 2009)

  • Superbug Algorithm (Anandaraman et al. 2012).

  • Swallow Swarm Optimization (Neshat et al. 2013).

  • Teaching–learning-based Optimization (Rao et al. 2011).

  • Termite-hill Algorithm (Zungeru et al. 2012).

  • Unconscious Search (Ardjmand and Amin-Naseri 2012).

  • Viral System (Cortés et al. 2008).

  • Virtual Bees Algorithm (Yang 2005).

  • Wasp Swarm Optimization (Theraulaz et al. 1991).

  • Wisdom of Artificial Crowds (Ashby and Yampolskiy 2011).

  • Wolf Colony Algorithm (Liu et al. 2011).

  • Wolf Pack Search (Yang et al. 2007).

2.2 Physics-based CI Algorithms

The word physics is derived from the Greek word physika, which means “natural things” (Holzner 2011). As the most fundamental science, physics is concerned with the basic principles of the universe. It is therefore the foundation of many other sciences such as biology, chemistry, and geology. In physics, just a small number of concepts and models can dramatically alter and expand our view of the world around us. Typically, the research of physics can be classified into the following areas such as classical mechanics, relativity, thermodynamics, electromagnetism, optics, and quantum mechanics (Serway and Jewett 2014). The simplicity of all these fundamental principles is not only the real beauty of physics, but also the main momentum in developing innovative CI algorithms.

In this class, we have included 28 novel physics-based CI algorithms which are listed as follows. Each algorithm’s original reference has also been attached for readers’ convenience to trace their origination.

  • Artificial Physics Optimization (Xie and Zeng 2009).

  • Atmosphere Clouds Model Optimization (Yan and Hao 2012).

  • Big Bang-Big Crunch (Erol and Eksin 2006).

  • Central Force Optimization (Formato 2007).

  • Chaos Optimization Algorithm (Li and Jiang 1998).

  • Charged System Search (Kaveh and Talatahari 2010).

  • Cloud Model-based Algorithm (Zhu and Ni 2012).

  • Electromagnetism-like Mechanism (Birbil and Fang 2003).

  • Extremal Optimization (Boettcher and Percus 2000).

  • Galaxy-based Search Algorithm (Shah-Hosseini 2011).

  • Gravitation Field Algorithm (Zheng et al. 2010).

  • Gravitational Clustering Algorithm (Kundu 1999).

  • Gravitational Emulation Local Search (Barzegar et al. 2009).

  • Gravitational Interactions Optimization (Flores et al. 2011).

  • Gravitational Search Algorithm (Rashedi et al. 2009).

  • Hysteretic Optimization (Zaránd et al. 2002).

  • Integrated Radiation Optimization (Chuang and Jiang 2007).

  • Intelligent Water Drops (Shah-Hosseini 2007).

  • Light Ray Optimization (Shen and Li 2009).

  • Magnetic Optimization Algorithm (Tayarani et al. 2008).

  • Particle Collision Algorithm (Sacco and Oliveira 2005).

  • Ray Optimization (Kaveh and Khayatazad 2012).

  • River Formation Dynamics Algorithm (Rabanal et al. 2007).

  • Space Gravitational Optimization (Hsiao et al. 2005).

  • Spiral Optimization Algorithm (Jin and Tran 2010).

  • Water Cycle Algorithm (Eskandar et al. 2012).

  • Water Flow Algorithm (Basu et al. 2007).

  • Water Flow-like Algorithm (Yang and Wang 2007).

2.3 Chemistry-based CI Algorithms

Chemistry can be usually viewed as a branch of physical science, but it is distinct from physics. In fact, the chemistry can be defined as a molecular view of matter. The major concern of chemistry is about the matters’ properties, the changes that matter undergoes, and the energy changes that accompany those processes (Whitten et al. 2014). In other words, to understand living systems fully, an important question to consider at this point is which factors that control and affect the chemical behaviours, such as photochemical reactions, oxidation–reduction reactions, combination reactions, decomposition reactions, displacement reactions, gas-formation reactions, and metathesis reactions. The analyzing of all these types of chemical reactions is not only the real beauty of chemistry, but also the main momentum in developing innovative CI algorithms.

In this class, we have included 5 novel chemistry-based CI algorithms which are listed as follows. Each algorithm’s original reference has also been attached for readers’ convenience to trace their origination.

  • Artificial Chemical Process (Irizarry 2005).

  • Artificial Chemical Reaction Optimization Algorithm (Alatas 2011).

  • Chemical Reaction Algorithm (Melin et al. 2013).

  • Chemical-Reaction Optimization Algorithm (Lam and Li 2010).

  • Gases Brownian Motion Optimization (Abdechiri et al. 2013).

2.4 Mathematics-based CI Algorithms

During the past decades, we have witnessed a proliferation of personal computers, smart phones, high-speed Internet, to name a few. The rapid development of various technologies has reduce the necessity for human beings to perform manual tasks which are either tedious or dangerous in nature, as computers may now accomplish most of them. As one of the most important building blocks, mathematics plays a crucial role in realizing all these technologies. The history of mathematics is no doubt tremendous long. According to Anglin (1994), Aristotle thought that is the priests in Egypt who actually started mathematics since the priestly class was allowed leisure. Whereas, Herodotus, believed that geometry was created to re-determine land boundaries due to the annual flooding of the Nile. The accurate beginning of mathematics is of course out of the scope of this book, but the widely employed mathematical modelling approaches indeed help us to gain insight and make reasonable accurate predictions towards the targeted problems (Yang 2013). Apart from that, the real beauty of mathematics also forms the main thrust in developing innovative CI algorithms.

In this class, we have included 2 novel mathematics-based CI algorithms which are listed as follows. Each algorithm’s original reference has also been attached for readers’ convenience to trace their origination.

  • Base Optimization Algorithm (Salem 2012).

  • Matheuristics (Maniezzo et al. 2009).

3 Conclusions

Our natural world conceals many characteristics of different creatures, and all of them have some unique behaviour or features to keep them survive. In this chapter, a brief background of CI (both in terms of traditional and innovative perspectives) has been discussed from an introductory perspective. The organizational structure of this book has also been explained. Interested readers are referred to them as a starting point for a further exploration and exploitation of any of these 134 algorithms that may draw their attention.