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Chicken Swarm Optimization - Modifications and Application
| Content Provider | Scilit |
|---|---|
| Author | Moldovan, Dorin Slowik, Adam |
| Copyright Year | 2020 |
| Description | 76Chicken Swarm Optimization (CSO) was introduced in 2014 in [1] and it is a part of the family of algorithms that are generically called nature inspired algorithms. Some illustrative algorithms from this family of algorithms are Particle Swarm Optimization (PSO) [2], Ant Colony Optimization (ACO) [3], Cuckoo Search (CS) [4], Lion Optimization Algorithm (LOA) [5], Kangaroo Mob Optimization (KMO) [6] and Crab Mating Optimization (CMO) [7]. Some of these algorithms are well known and they represent the source of inspiration for other bio-inspired algorithms, while other algorithms are relatively new and they are adaptations of the original PSO algorithm for different types of animal behaviors. The CSO algorithm is inspired by the behavior of the chickens when they search for food and it is a bio-inspired algorithm that can be applied for solving various types of engineering problems that are characterized by a search space with many dimensions. Each solution is represented by a chicken that has a position and there are three types of chickens namely, roosters, hens and chicks . The algorithm has as a main objective the identification of the best chicken according to an objective function which depends on the optimization problem that is solved. Some hybrid algorithms from literature that are based on the CSO algorithm are: Bat-Chicken Swarm Optimization (B-CSO) [8], Cuckoo Search-Chicken Swarm Optimization (CS-CSO) [9] and Chicken Swarm Optimization-Teaching Learning Based Optimization (CSO-TLBO) [10]. The chapter is organized as follows: Section 6.2 presents a short description of the global version of the CSO algorithm, Section 6.3 illustrates modifications of the CSO algorithm, Section 6.4 presents the application of the CSO algorithm for falls detection in daily living activities [11], [12] and Section 6.5 presents the main conclusions. Book Name: Swarm Intelligence Algorithms |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2018-0-88806-8&isbn=9780429422607&doi=10.1201/9780429422607-6&format=pdf |
| Ending Page | 90 |
| Page Count | 16 |
| Starting Page | 75 |
| DOI | 10.1201/9780429422607-6 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-08-25 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Swarm Intelligence Algorithms Cybernetical Science Adaptations Swarm Optimization Behaviors Inspired Algorithms Chicken Swarm |
| Content Type | Text |
| Resource Type | Chapter |
