Journal ArticleDOI
GEPSO: A new generalized particle swarm optimization algorithm
TLDR
The Generalized Particle Swarm Optimization (GEPSO) algorithm is introduced as a new version of the PSO algorithm for continuous space optimization, which enriches the original PSO by incorporating two new terms into the velocity updating equation, which aim to deepen the interrelations of particles and their knowledge sharing, increase variety in the swarm, and provide a better search in unexplored areas of the search space.About:
This article is published in Mathematics and Computers in Simulation.The article was published on 2021-01-01. It has received 56 citations till now. The article focuses on the topics: Particle swarm optimization & Swarm intelligence.read more
Citations
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Journal ArticleDOI
Particle Swarm Optimization: A Comprehensive Survey
TL;DR: Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature as mentioned in this paper , and many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance.
Journal ArticleDOI
A new hybrid algorithm for path planning of mobile robot
TL;DR: Applying the new hybrid algorithm to path planning can improve the robot’s reaction ability and computing power in path planning and improve the accuracy and performance of the FA.
Journal ArticleDOI
EMCS-SVR: Hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis
TL;DR: In this article , a novel dynamical adaptive enhanced simulation method coupled with support vector regression (SVR) is proposed for structural reliability analysis, robust and efficient sampling methods that address low failure probabilities are vital challenges.
Journal ArticleDOI
A risk-averse decision based on IGDT/stochastic approach for smart distribution network operation under extreme uncertainties
TL;DR: In this paper, a risk-averse strategy-based decision-making tool is proposed to help the smart distribution network operator (SDNO) in day-ahead operational practices including optimal unit commitment (UC) and optimal distribution feeder reconfiguration (DFR).
Journal ArticleDOI
Manta ray foraging optimization algorithm and hybrid Taguchi salp swarm-Nelder–Mead algorithm for the structural design of engineering components
Ali Rıza Yıldız,Pranav Mehta +1 more
TL;DR: This research uses both the hybrid Taguchi salp swarm algorithm-Nelder–Mead (HTSSA-NM) and the manta ray foraging optimization algorithm (MRFO) to optimize the structure and shape of the automobile brake pedal.
References
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Proceedings ArticleDOI
A new optimizer using particle swarm theory
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Journal ArticleDOI
No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Book
Metaheuristics: From Design to Implementation
TL;DR: This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking and bioinformatics to engineering design, routing, and scheduling.
Proceedings ArticleDOI
Fuzzy adaptive particle swarm optimization
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization method, which is especially useful for optimization problems with a dynamic environment.
Journal ArticleDOI
A review of particle swarm optimization. Part I: background and development
TL;DR: This paper aims to offer a compendious and timely review of the field and the challenges and opportunities offered by this welcome addition to the optimization toolbox.