Research Area:  Metaheuristic Computing

Abstract:

Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm Optimization Algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.

Keywords:  
Chicken Swarm Optimization Algorithm
complex optimization problem
conventional gradient
unit commitment
microgrid planning
vehicle routing
feature selection
community detection
social networks

Author(s) Name:  Sanchari Deb, Xiao-Zhi Gao, Kari Tammi, Karuna Kalita & Pinakeswar Mahanta

Publisher name:  Springer

DOI:  10.1007/s10462-019-09718-3

Volume Information:  53, pages 1737–1765

Paper Link:   https://link.springer.com/article/10.1007/s10462-019-09718-3