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A Survey of Computational Intelligence Algorithms and Their Applications

A Survey of Computational Intelligence Algorithms and Their Applications
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Author(s): Hadj Ahmed Bouarara (GeCoDe laboratory, Department of Computer Sciences, Dr. Tahar Moulay University of Saida, Algeria)
Copyright: 2017
Pages: 44
Source title: Handbook of Research on Soft Computing and Nature-Inspired Algorithms
Source Author(s)/Editor(s): Shishir K. Shandilya (Bansal Institute of Research and Technology, India), Smita Shandilya (Sagar Institute of Research Technology and Science, India), Kusum Deep (Indian Institute of Technology Roorkee, India)and Atulya K. Nagar (Liverpool Hope University, UK)
DOI: 10.4018/978-1-5225-2128-0.ch005

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Abstract

This chapter subscribes in the framework of an analytical study about the computational intelligence algorithms. These algorithms are numerous and can be classified in two great families: evolutionary algorithms (genetic algorithms, genetic programming, evolutionary strategy, differential evolutionary, paddy field algorithm) and swarm optimization algorithms (particle swarm optimisation PSO, ant colony optimization (ACO), bacteria foraging optimisation, wolf colony algorithm, fireworks algorithm, bat algorithm, cockroaches colony algorithm, social spiders algorithm, cuckoo search algorithm, wasp swarm optimisation, mosquito optimisation algorithm). We have detailed each algorithm following a structured organization (the origin of the algorithm, the inspiration source, the summary, and the general process). This paper is the fruit of many years of research in the form of synthesis which groups the contributions proposed by various researchers in this field. It can be the starting point for the designing and modelling new algorithms or improving existing algorithms.

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