Proceedings ArticleDOI
Particle swarm optimization with Gaussian mutation
N. Higashi,Hitoshi Iba +1 more
- pp 72-79
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TLDR
This method combines the traditional velocity and position update rules with the ideas of Gaussian mutation and has succeeded in acquiring better results than those by GA and PSO alone.Abstract:
In this paper we present particle swarm optimization with Gaussian mutation combining the idea of the particle swarm with concepts from evolutionary algorithms. This method combines the traditional velocity and position update rules with the ideas of Gaussian mutation. This model is tested and compared with the standard PSO and standard GA. The comparative experiments have been conducted on unimodal functions and multimodal functions. PSO with Gaussian mutation is able to obtain a result superior to GA. We also apply the PSO with Gaussian mutation to a gene network. Consequently, it has succeeded in acquiring better results than those by GA and PSO alone.read more
Citations
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Journal ArticleDOI
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
TL;DR: A novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations to overcome the difficulties of selecting an appropriate mutation step size for different problems.
Journal ArticleDOI
Analysis of Particle Swarm Optimization Algorithm
TL;DR: The main idea of the principle of PSO is presented; the advantages and the shortcomings are summarized; and some kinds of improved versions ofPSO and research situation are presented.
Journal ArticleDOI
A Competitive Swarm Optimizer for Large Scale Optimization
Ran Cheng,Yaochu Jin +1 more
TL;DR: Empirical results demonstrate that the proposed CSO exhibits a better overall performance than five state-of-the-art metaheuristic algorithms on a set of widely used large scale optimization problems and is able to effectively solve problems of dimensionality up to 5000.
Journal ArticleDOI
A social learning particle swarm optimization algorithm for scalable optimization
Ran Cheng,Yaochu Jin +1 more
TL;DR: This paper introduces social learning mechanisms into particle swarm optimization (PSO) to develop a social learning PSO (SL-PSO), which performs well on low-dimensional problems and is promising for solving large-scale problems as well.
Journal ArticleDOI
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications
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.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Book ChapterDOI
Comparison between Genetic Algorithms and Particle Swarm Optimization
Russell C. Eberhart,Yuhui Shi +1 more
TL;DR: This paper compares two evolutionary computation paradigms: genetic algorithms and particle swarm optimization, and suggests ways in which performance might be improved by incorporating features from one paradigm into the other.
Book ChapterDOI
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
TL;DR: This paper investigates the philosophical and performance differences of particle swarm and evolutionary optimization by comparison experiments involving four non-linear functions well studied in the evolutionary optimization literature.
Book ChapterDOI
Genetic Algorithms for Real Parameter Optimization
TL;DR: It is shown that k-point crossover can be viewed as a crossover operation on the vector of parameters plus perturbations of some of the parameters, which suggests a genetic algorithm that uses real parameter vectors as chromosomes, real parameters as genes, and real numbers as alleles.
Proceedings ArticleDOI
Using selection to improve particle swarm optimization
TL;DR: A hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations is described that shows selection to provide an advantage for some (but not all) complex functions.