Socio-Inspired Optimization Using Cohort Intelligence

• Published in: Cohort Intelligence: A Socio-inspired Optimization Method Vol. 114; pp. 9 - 24
• Main Authors: Krishnasamy, Ganesh, Kulkarni, Anand Jayant, Abraham, Ajith
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 01.01.2017; Springer International Publishing
• Series: Intelligent Systems Reference Library
• Subjects: Artificial intelligence; Continuous Optimization Problem; Genetic Algorithm; Modify Particle Swarm Optimization; Multimodal Problem; Particle Swarm Optimization
• ISBN: 3319442538; 9783319442532
• ISSN: 1868-4394; 1868-4408
• DOI: 10.1007/978-3-319-44254-9_2

The nature-/bio-inspired optimization techniques such as genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), simulated annealing (SA), Tabu search, etc., have become popular due to their simplicity to implement and working based on rules. The GA is population based which is evolved using the operators such as selection, crossover, mutation, etc. According to Deb (Methods Appl Mech Eng, 186:311–338, 2000, [1]) and Ray et al. (Eng Optim, 33:399–424, 2001, [2]) the performance of GA is governed by the quality of the population being evaluated and may often reach very close to the global optimal solution and necessitates local improvement techniques to incorporate into it. The paradigm of Swarm Intelligence (SI) is a decentralized self organizing optimization approach inspired from social behavior of living organisms such as insects, fishes, etc. which can communicate with one another either directly or indirectly. The techniques such as Particle Swarm Optimization (PSO) is inspired from the social behavior of bird flocking and school of fish searching for food.