Application of Particle Swarm Optimization to Formative E-Assessment in Project Management

The current paper describes the application of Particle Swarm Optimization algorithm to the formative e-assessment problem in project management. The proposed approach resolves the issue of personalization, by taking into account, when selecting the item tests in an e-assessment, the following elements: the ability level of the user, the targeted difficulty of the test and the learning objectives, represented by project management concepts which have to be checked. The e-assessment tool in which the Particle Swarm Optimization algorithm is integrated is also presented. Experimental results and comparison with other algorithms used in item tests selection prove the suitability of the proposed approach to the formative e-assessment domain. The study is presented in the framework of other evolutionary and genetic algorithms applied in e-education.Particle Swarm Optimization, Genetic Algorithms, Evolutionary Algorithms, Formative E-assessment, E-education

Critical analysis of angle modulated particle swarm optimisers

This dissertation presents an analysis of the angle modulated particle swarm optimisation (AMPSO) algorithm. AMPSO is a technique that enables one to solve binary optimisation problems with particle swarm optimisation (PSO), without any modifications to the PSO algorithm. While AMPSO has been successfully applied to a range of optimisation problems, there is little to no understanding of how and why the algorithm might fail. The work presented here includes in-depth theoretical and emprical analyses of the AMPSO algorithm in an attempt to understand it better. Where problems are identified, they are supported by theoretical and/or empirical evidence. Furthermore, suggestions are made as to how the identified issues could be overcome. In particular, the generating function is identified as the main cause for concern. The generating function in AMPSO is responsible for generating binary solutions. However, it is shown that the increasing frequency of the generating function hinders the algorithm’s ability to effectively exploit the search space. The problem is addressed by introducing methods to construct different generating functions, and to quantify the quality of arbitrary generating functions. In addition to this, a number of other problems are identified and addressed in various ways. The work concludes with an empirical analysis that aims to identify which of the various suggestions made throughout this dissertatioin hold substantial promise for further research.Dissertation (MSc)--University of Pretoria, 2017.Computer ScienceMScUnrestricte

Critical considerations on angle modulated particle swarm optimisers

This article investigates various aspects of angle modulated particle swarm optimisers (AMPSO). Previous attempts at improving the algorithm have only been able to produce better results in a handful of test cases. With no clear understanding of when and why the algorithm fails, improving the algorithm’s performance has proved to be a difficult and sometimes blind undertaking. Therefore, the aim of this study is to identify the circumstances under which the algorithm might fail, and to understand and provide evidence for such cases. It is shown that the general assumption that good solutions are grouped together in the search space does not hold for the standard AMPSO algorithm or any of its existing variants. The problem is explained by specific characteristics of the generating function used in AMPSO. Furthermore, it is shown that the generating function also prevents particle velocities from decreasing, hindering the algorithm’s ability to exploit the binary solution space. Methods are proposed to both confirm and potentially solve the problems found in this study. In particular, this study addresses the problem of finding suitable generating functions for the first time. It is shown that the potential of a generating function to solve arbitrary binary optimisation problems can be quantified. It is further shown that a novel generating function with a single coefficient is able to generate solutions to binary optimisation problems with fewer than four dimensions. The use of ensemble generating functions is proposed as a method to solve binary optimisation problems with more than 16 dimensions.http://link.springer.com/journal/117212016-12-31hb201

Power System Transient Stability Enhancement by Tuning of SSSC and PSS Parameters Using PSO Technique

في هذه الورقة تم اختبار التصميم المتناغم بين SSSC وPSS في زيادة تخميد تذبذبات و تحسين الاستقرارية لمنظومة القدرة. تم تصميم مشكلة التصميم للمسيطر  SSSC  و مضبط منظومة القدرة PSS  كمشكلة امثلية وباستخدام  تقنية امثلية سرب الجسيمات PSO للبحث عن مقادير او معلمات التحكم الأمثل للمسيطرين من خلال التقليل من دالة الهدف التي بناءها على أساس الانحراف في السرعة الزاوية لدوار المولد والمجال الزمني، المولد  لتحسين أداء الاستقرارية  العابرة لمنظومة القدرة.تم اختبار المسيطرات المقترحة على منظومة  قدرة ضعيفة الترابط تعرضت لاضطراب شديد. نتائج المحاكاة اللاخطية  استخدمت لإظهار فعالية المسيطرات المقترحة وقدرتها على توفير كفاءة التخميد للتذبذبات المنظومة . ويلاحظ أيضا أن المسيطرين SSSC و يحسنان و إلى حد كبير استقرارية منظومة القدرة عند تعرضها الى اضطرابات شديدة.In this paper, the tuning design of  SSSC and PSS was examined in increasing the damping of system oscillations and improve the stability of the power system during disturbances. The design problem of the SSSC controller and PSS is designed as problem of optimization and the technique uses (PSO) technique to find for optimal control parameters. By minimizing the objective function based on the speed deviation and time domain, which deliberately deviates at the oscillation angle of the alternator rotor to improve performance of transient stability of the system. The proposed controllers are tested on the system of weak bonding ability exposed to severe disturbance. Nonlinear simulation results are presented to demonstrate the proposed controller's effectiveness and its ability to give efficient damping. It is also noted that the proposed controllers  of SSSC and PSS greatly improves the power system stability

Multipath channel identification by using global optimization in ambiguity function domain

Cataloged from PDF version of article.A new transform domain array signal processing technique is proposed for identification of multipath communication channels. The received array element outputs are transformed to delay-Doppler domain by using the cross-ambiguity function (CAF) for efficient exploitation of the delay-Doppler diversity of the multipath components. Clusters of multipath components can be identified by using a simple amplitude thresholding in the delay-Doppler domain. Particle swarm optimization (PSO) can be used to identify parameters of the multipath components in each cluster. The performance of the proposed PSO-CAF technique is compared with the space alternating generalized expectation maximization (SAGE) technique and with a recently proposed PSO based technique at various SNR levels. Simulation results clearly quantify the superior performance of the PSO-CAF technique over the alternative techniques at all practically significant SNR levels. (C) 2011 Elsevier B.V. All rights reserved

Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets

Integral dose investigation of non‐coplanar treatment beam geometries in radiotherapy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134886/1/mp5055.pd