A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

By Yudong Zhang, Shuihua Wang, Genlin Ji. 2015

1,190 citations recorded by the source corpus.

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

View Paper: https://www.semanticscholar.org/paper/9e5e27034b2aa19a8eb51f3637f4db2b68142204

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Imitation Learning (2017, methodology): “Popular swarm intelligence methods such as Particle Swarm Optimization (PSO) [Zhang et al. 2015] and Ant Colony Optimization (ACO) [Zhang et al. 2010] are used to generate trajectories for unmanned vehicle navigation.”
Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique (2020, unknown): “An effective and important way to overcome these problems is to use chaos theory [32] .”
Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey (2018, background): “For that reason, it is performed in solving numerous real world problems (Ashuri and Tavakolan 2011; Chao 2014; Ganguly et al. 2013; Liu and Wang 2012; Mansour et al. 2012; Ming et al. 2012; Pandey et al. 2014; Thakral et al. 2013; Yousefi et al. 2012; Zhang et al. 2015, 2010).”
On the exploration and exploitation in popular swarm-based metaheuristic algorithms (2018, background): “Yudong et al. in a survey [17] also found these algorithms popular in literature.”
Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, methodology): “PSO (Particle SwarmOptimization) [98], andMBAFS (Modified Bat Algorithm for Feature Selection) [93] by conducting experiments based on these three datasets.”
Optimizing connection weights in neural networks using the whale optimization algorithm (2016, background): “The back-propagation algorithm and its variants (Wang et al. 2015; Kim and Jung 2015) are considered as standard examples of gradient-based methods and the most popular between researchers.”
Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems (2022, background): “Zhang et al. (2015) contains a detailed overview of the implementation of PSO techniques.”