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.

中文翻译：

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鸡群优化算法的最新研究：综述（2014-2018）

在有限的时间内解决复杂的优化问题是一项乏味的任务。传统的基于梯度的优化算法在解决社交网络中的单元组合、微电网规划、车辆路由、特征选择和社区检测等复杂问题方面存在局限性。近年来，基于人口的仿生算法在广泛的优化问题上表现出具有竞争力的性能。鸡群优化算法 (CSO) 是一种模仿鸡群行为的仿生元启发式算法。许多文献报道，CSO 在广泛的基准问题中优于许多著名的元启发式算法。本文综述了与 CSO 相关的各种问题，如一般生物学、基础知识、CSO 的变体、CSO 的绩效、