A Review on Representative Swarm Intelligence Algorithms for Solving Optimization Problems: Applications and Trends
Jun Tang,Gang Liu,Qingtao Pan +2 more
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TLDR
A review of swarm intelligence algorithms can be found in this paper, where the authors highlight the functions and strengths from 127 research literatures and briefly provide the description of their successful applications in optimization problems of engineering fields.Abstract:
Swarm intelligence algorithms are a subset of the artificial intelligence (AI) field, which is increasing popularity in resolving different optimization problems and has been widely utilized in various applications. In the past decades, numerous swarm intelligence algorithms have been developed, including ant colony optimization (ACO), particle swarm optimization (PSO), artificial fish swarm (AFS), bacterial foraging optimization (BFO), and artificial bee colony (ABC). This review tries to review the most representative swarm intelligence algorithms in chronological order by highlighting the functions and strengths from 127 research literatures. It provides an overview of the various swarm intelligence algorithms and their advanced developments, and briefly provides the description of their successful applications in optimization problems of engineering fields. Finally, opinions and perspectives on the trends and prospects in this relatively new research domain are represented to support future developments.read more
Citations
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Journal ArticleDOI
Pyramid particle swarm optimization with novel strategies of competition and cooperation
TL;DR: In this paper , a pyramid PSO (PPSO) with novel competitive and cooperative strategies to update particles' information is proposed, which has superior performance in terms of accuracy, Wilcoxon signed-rank test and convergence speed, yet achieves comparable running time in most cases.
Journal ArticleDOI
A novel intelligent transport system charging scheduling for electric vehicles using Grey Wolf Optimizer and Sail Fish Optimization algorithms
Rajasekaran Rajamoorthy,Gokul Karthik Arunachalam,Padmanathan Kasinathan,Ramkumar Devendiran,Pouria Ahmadi,Santhiya Pandiyan,Sureshkumar Muthusamy,Hitesh Panchal,Hussein A. Kazem,Prabhakar Sharma +9 more
TL;DR: An effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO) based on the fitness function, and the performance was improved with a traffic density improved when many vehicles were considered.
Journal ArticleDOI
Objective Space-Based Population Generation to Accelerate Evolutionary Algorithms for Large-Scale Many-Objective Optimization
TL;DR: In this paper , an objective space-based population generation method was proposed to obtain new individuals in the objective space and then map them to decision variable space and synthesize new solutions.
Journal ArticleDOI
A Novel Hybrid Particle Swarm Optimization Algorithm for Path Planning of UAVs
TL;DR: In this paper , a hybrid particle swarm optimization (PSO) algorithm is proposed to address the problem of automatic path planning by unmanned aerial vehicles (UAVs), which needs to access the optimal path rapidly in the complicated field.
Journal ArticleDOI
A Novel Hybrid Particle Swarm Optimization Algorithm for Path Planning of UAVs
TL;DR: In this paper , a hybrid particle swarm optimization (PSO) algorithm is proposed to address the problem of automatic path planning by unmanned aerial vehicles (UAVs), which needs to access the optimal path rapidly in the complicated field.
References
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Book
System Identification: Theory for the User
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Book ChapterDOI
Parameter Selection in Particle Swarm Optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters.