Journal ArticleDOI
Self-adaptive mutation differential evolution algorithm based on particle swarm optimization
Reads0
Chats0
TLDR
A self-adaptive mutation differential evolution algorithm based on particle swarm optimization (DEPSO) is proposed to improve the optimization performance of DE and can significantly improve the global convergence performance of the conventional DE and thus avoid premature convergence.About:
This article is published in Applied Soft Computing.The article was published on 2019-08-01. It has received 72 citations till now. The article focuses on the topics: Premature convergence & Evolutionary algorithm.read more
Citations
More filters
Journal ArticleDOI
Major Advances in Particle Swarm Optimization: Theory, Analysis, and Application
TL;DR: A rigorous yet systematic review is presented to organize and summarize the information on the PSO algorithm and the developments and trends of its most basic as well as of some of the very notable implementations that have been introduced recently, bearing in mind the coverage of paradigm, theory, hybridization, parallelization, complex optimization, and the diverse applications of the algorithm.
Journal ArticleDOI
A full migration BBO algorithm with enhanced population quality bounds for multimodal biomedical image registration
TL;DR: An elite learning operator that is based on social comparison theory to improve the upper bound of the whole population’s quality and the accuracy and the convergence speed of the multimodal medical registration can be greatly enhanced.
Journal ArticleDOI
Differential evolution: A recent review based on state-of-the-art works
TL;DR: This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years.
Journal ArticleDOI
A modified particle swarm optimization using adaptive strategy
TL;DR: In MPSO, in order to well balance the global exploration and local exploitation capabilities of the PSO, a chaos-based non-linear inertia weight is proposed and stochastic and mainstream learning strategies are adopted to enhance PSO’s ability to solve complex optimization problems in expert systems.
Journal ArticleDOI
Differential evolution: A recent review based on state-of-the-art works
TL;DR: Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin's theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997 as discussed by the authors .
References
More filters
Journal ArticleDOI
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Journal ArticleDOI
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Journal ArticleDOI
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms
TL;DR: The basics are discussed and a survey of a complete set of nonparametric procedures developed to perform both pairwise and multiple comparisons, for multi-problem analysis are given.
Journal ArticleDOI
Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
Related Papers (5)
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
JADE: Adaptive Differential Evolution With Optional External Archive
Jingqiao Zhang,A.C. Sanderson +1 more