Particle swarm optimization with composite particles in dynamic environments

This article is placed here with the permission of IEEE - Copyright @ 2010 IEEEIn recent years, there has been a growing interest in the study of particle swarm optimization (PSO) in dynamic environments. This paper presents a new PSO model, called PSO with composite particles (PSO-CP), to address dynamic optimization problems. PSO-CP partitions the swarm into a set of composite particles based on their similarity using a "worst first" principle. Inspired by the composite particle phenomenon in physics, the elementary members in each composite particle interact via a velocity-anisotropic reflection scheme to integrate valuable information for effectively and rapidly finding the promising optima in the search space. Each composite particle maintains the diversity by a scattering operator. In addition, an integral movement strategy is introduced to promote the swarm diversity. Experiments on a typical dynamic test benchmark problem provide a guideline for setting the involved parameters and show that PSO-CP is efficient in comparison with several state-of-the-art PSO algorithms for dynamic optimization problems.This work was supported in part by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant 70931001 and 70771021, the Science Fund for Creative Research Group of the NNSF of China under Grant 60821063 and 70721001, the Ph.D. Programs Foundation of the Ministry of education of China under Grant 200801450008, and by the Engineering and Physical Sciences Research Council of U.K. under Grant EP/E060722/1

Particle swarm optimisation in dynamically changing environments - an empirical study

Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE). However, these research efforts generally focused on optimising one variation of the PSO algorithm for one type of DE. The aim of this work is to develop a more comprehensive view of PSO for DEs. This thesis studies different schemes of characterising and taxonomising DEs, performance measures used to quantify the performance of optimisation algorithms applied to DEs, various adaptations of PSO to apply PSO to DEs, and the effectiveness of these approaches on different DE types. The standard PSO algorithm has shown limitations when applied to DEs. To overcome these limitations, the standard PSO can be modi ed using personal best reevaluation, change detection and response, diversity maintenance, or swarm sub-division and parallel tracking of optima. To investigate the strengths and weaknesses of these approaches, a representative sample of algorithms, namely, the standard PSO, re-evaluating PSO, reinitialising PSO, atomic PSO (APSO), quantum swarm optimisation (QSO), multi-swarm, and self-adapting multi-swarm (SAMS), are empirically analysed. These algorithms are analysed on a range of DE test cases, and their ability to detect and track optima are evaluated using performance measures designed for DEs. The experiments show that QSO, multi-swarm and reinitialising PSO provide the best results. However, the most effective approach to use depends on the dimensionality, modality and type of the DEs, as well as on the objective of the algorithm. A number of observations are also made regarding the behaviour of the swarms, and the influence of certain control parameters of the algorithms evaluated. CopyrightDissertation (MSc)--University of Pretoria, 2012.Computer Scienceunrestricte

Economic Load Dispatch for IEEE 30-Bus System Using PSO

ELD or economic load dispatch is a crucial aspect in any practical power network. Economic load dispatch is the technique whereby the active power outputs are allocated to generator units in the most cost-effective way in compliance with all constraints of the network. The traditional methods for solving ELD include Lambda-Iterative Technique, Newton-Raphson Method, Gradient method, etc. All these traditional algorithms need the incremental fuel cost curves of the generators to be increasing monotonically or piece-wise linear. But in practice the input-output characteristics of a generator are highly non-linear leading to a challenging non-convex optimisation problem. Methods like artificial intelligence, DP (dynamic programming), GA (genetic algorithms), and PSO (particle swarm optimisation) solve non-convex optimisation problems in an efficient manner and obtain a fast and near global and optimum solution. In this project ELD problem has been solved using Lambda-Iterative technique, GA (Genetic Algorithms) and PSO (Particle Swarm Optimisation) and the results have been compared. All the analyses have been made in MATLAB environment

Regularised feed forward neural networks for streamed data classification problems

DATA AVAILABILITY : Data will be made available on request.SUPPLEMENTARY MATERIAL : MMC S1. The supplementary material contains empirical results, performance graphs, illustrations, pseudo code and equations.Streamed data classification problems (SDCPs) require classifiers to not just find the optimal decision boundaries that describe the relationships within a data stream, but also to adapt to changes in the decision boundaries in real-time. The requirement is due to concept drift, i.e., incorrect classifications caused by decision boundaries changing over time. Changes include disappearing, appearing or shifting decision boundaries. This article proposes an online learning approach for feed forward neural networks (FFNNs) that meets the requirements of SDCPs. The approach uses regularisation to dynamically optimise the architecture, and quantum particle swarm optimisation (QPSO) to dynamically adjust the weights. The learning approach is applied to a FFNN, which uses rectified linear activation functions, to form a novel SDCP classifier. The classifier is empirically investigated on several SDCPs. Both weight decay (WD) and weight elimination (WE) are investigated as regularisers. Empirical results show that using QPSO with no regularisation causes the classifier to completely saturate. However, using QPSO with regularisation makes the classifier efficient at dynamically adapting both its architecture and weights as decision boundaries change. Furthermore, the results favour WE over WD as a regulariser for QPSO.The National Research Foundation (NRF) of South Africa.https://www.elsevier.com/locate/engappaihj2024Computer ScienceSDG-09: Industry, innovation and infrastructur

Using particle swarm optimisation to train feedforward neural networks in dynamic environments

The feedforward neural network (NN) is a mathematical model capable of representing any non-linear relationship between input and output data. It has been succesfully applied to a wide variety of classification and function approximation problems. Various neural network training algorithms were developed, including the particle swarm optimiser (PSO), which was shown to outperform the standard back propagation training algorithm on a selection of problems. However, it was usually assumed that the environment in which a NN operates is static. Such an assumption is often not valid for real life problems, and the training algorithms have to be adapted accordingly. Various dynamic versions of the PSO have already been developed. This work investigates the applicability of dynamic PSO algorithms to NN training in dynamic environments, and compares the performance of dynamic PSO algorithms to the performance of back propagation. Three popular dynamic PSO variants are considered. The extent of adaptive properties of back propagation and dynamic PSO under different kinds of dynamic environments is determined. Dynamic PSO is shown to be a viable alternative to back propagation, especially under the environments exhibiting infrequent gradual changes. Copyright 2011, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Rakitianskaia, A 2011, Using particle swarm optimisation to train feedforward neural networks in dynamic environments, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd C12/4/406/gmDissertation (MSc)--University of Pretoria, 2011.Computer ScienceUnrestricte

A comprehensive survey on cultural algorithms

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Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

Modelling microscopic clusters of sulphuric acid and water relevant to atmospheric nucleation

Classical nucleation theory has been a useful tool for predicting the phenomena of nucleation for the past seventy years. However the model has several limitations, which in some examples give rise to predicted rates that are several orders of magnitude in error. One such example is that of sulphuric acid and water nucleation which has long been framed as an important source of cloud condensation nuclei and therefore has implications for the climate, both locally and globally. In addition stratospheric aerosol injection of molecules containing sulphur, including sulphuric acid, are of interest as a potential geoengineering technique. The focus for this study is to improve upon our understanding of water and sulphuric acid nucleation. The initial phase of the project concerned performing quantum chemistry calculations which go beyond the macroscopic description employed by classical nucleation theory. Kohn-Sham density functional theory has been successfully employed in the fields of condensed matter, material physics and chemistry. However one of the assumptions of the theory is the classical treatment of the nuclei. The path integral molecular dynamics technique is used here to test this assumption on small clusters of sulphuric acid and water. We find that the introduction of zero point motion has a small effect on the equilibrium properties of certain configurations in line with expected behaviour. An interesting structure is found which serves to emphasise the importance of liquid like behaviour in this cluster at room temperature. The first study demonstrated the computational expense of treating systems at the microscopic scale using quantum chemistry approaches. The second phase of the research focused upon finding a suitable classical potential to employ within a molecular dynamics scheme, which would drastically reduce the computational expensive of performing simulations. This potential would be required to retain the ability for protons to transfer between selected species. The empirical valence bond method was chosen for its straightforward implementation and its similarity to traditional classical schemes. However some modifications were required to implement the scheme. Two algorithms were designed to identify species within the system and treat them in a fashion suitable for use in the empirical valence bond method. In addition the empirical valence bond method also needed to be parametrised for the sulphuric acid and water system. This was achieved by using the particle swarm optimisation technique, which performed force matching parametrisation using the Kohn-Sham density functional theory work from the previous phase of the project. The model was fully programmed in FORTRAN 90/95 and was incorporated into DL-POLY version 4.03. It is tested against the density functional theory data to which it is parametrised to check that the main features of the quantum chemistry are retained within the empirical valence bond technique. A puzzling issue appeared in preliminary molecular dynamics simulations performed with DL-POLY 4.03. The issue arises from a constraint imposed to fix the centre of mass. The solution to the modified Langevin equation introduced by this constraint is derived. The results are then compared to the puzzling DL-POLY simulations and found to be consistent. The constraint is then removed for all further simulations. The developed empirical valence bond potential was used to perform simulations of small clusters of sulphuric acid and water. We test the level of hydration required to ionise the system and find it to be in line with literature values. A thermodynamic integration scheme that was suitable for this system was derived. Preliminary simulations were performed using the model to compute free energies for use with classical nucleation theory in order to calculate nucleation rates

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field