Book Chapter10.1007/978-3-642-18041-5_12
Particle Swarm Optimization
Veysel Gazi,Kevin M. Passino +1 more
- 01 Jan 2011
- pp 251-279
200
TL;DR: This chapter considers the Particle Swarm Optimization (PSO) algorithm, which is another biologically inspired optimization algorithm better suited for parallel and distributed implementations, and discusses various neighborhood strategies including static and dynamic (i.e., time-varying) neighborhoods.
read more
Abstract: In this chapter we consider the Particle Swarm Optimization (PSO) algorithm, which is another biologically inspired optimization algorithm. Consider again the problem in which we want to find the minimum of a function J(x), \(x \in {\mathbb R}^{n}\). Assume that measurements or an analytical expression of the gradient \(\nabla J(x)\) are not available. Moreover, even if they are available, assume the function is very non-uniform or noisy so that this information is not useful. The PSO algorithm is another population based optimization algorithm which can be used to solve such problems. It is a direct search (non-gradient) algorithm where a population of particles “search” in parallel for the minimum of a given function in a multi-dimensional (n-dimensional) space (or region/domain) without using gradient information. Below we describe the basic PSO iteration. Then we discuss a modified decentralized and asynchronous version better suited for parallel and distributed implementations. Moreover, we discuss various neighborhood strategies including static and dynamic (i.e., time-varying) neighborhoods.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Word-level Textual Adversarial Attacking as Combinatorial Optimization
Yuan Zang,Fanchao Qi,Chenghao Yang,Zhiyuan Liu,Meng Zhang,Qun Liu,Maosong Sun +6 more
- 01 Jul 2020
TL;DR: SememePSO-based attack as mentioned in this paper incorporates the sememe-based word substitution method and particle swarm optimization-based search algorithm to solve the two problems separately, which achieved much higher attack success rates and craft more high-quality adversarial examples as compared to baseline methods.
Brief paper: Fixed-structure H∞ controller synthesis: A meta-heuristic approach using simple constrained particle swarm optimization
TL;DR: A design method of fixed-structure robust controllers satisfying multiple H"~ norm specifications by using a sort of randomized algorithms, based on a particle swarm optimization tool based on PSO.
115
Particle swarm optimization with a new update mechanism
TL;DR: Experimental results and comparisons show that PSOd outperforms PSO and its variants on solving the numerical benchmark functions in terms of solution quality and robustness.
111
An Improved Interleaved Boost Converter With PSO-Based Optimal Type-III Controller
TL;DR: In this article, an improved interleaved boost converter (IBC) with an optimal Type-III controller by utilizing voltage mode control is presented. But due to the non-minimum phase problem of IBC, closed-loop bandwidth is restricted that causes slower converter dynamics.
103
A novel approach to model selection in tourism demand modeling
TL;DR: This study considers Seasonal AutoRegressive Integrated Moving Average, ν-Support Vector Regression, and multi-layer perceptron type Neural Network models and optimize their parameters using different techniques for each and compares their performances on monthly tourist arrival data to Turkey from different countries.
95