Compound particle swarm optimization in dynamic environments

Copyright @ Springer-Verlag Berlin Heidelberg 2008.Adaptation to dynamic optimization problems is currently receiving a growing interest as one of the most important applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSO) is proposed as a new variant of particle swarm optimization to enhance its performance in dynamic environments. Within CPSO, compound particles are constructed as a novel type of particles in the search space and their motions are integrated into the swarm. A special reflection scheme is introduced in order to explore the search space more comprehensively. Furthermore, some information preserving and anti-convergence strategies are also developed to improve the performance of CPSO in a new environment. An experimental study shows the efficiency of CPSO in dynamic environments.This work was supported by the Key Program of the National Natural Science Foundation (NNSF) of China under Grant No. 70431003 and Grant No. 70671020, the Science Fund for Creative Research Group of NNSF of China under Grant No. 60521003, the National Science and Technology Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant No. EP/E060722/1

Program trace optimization

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.Paper to be presented at the Fifteenth International Conference on Parallel Problem Solving from Nature (PPSN XV), Coimbra, Portugal on 8-12 September 2018.We introduce Program Trace Optimization (PTO), a system for `universal heuristic optimization made easy'. This is achieved by strictly separating the problem from the search algorithm. New problem definitions and new generic search algorithms can be added to PTO easily and independently, and any algorithm can be used on any problem. PTO automatically extracts knowledge from the problem specifi cation and designs search operators for the problem. The operators designed by PTO for standard representations coincide with existing ones, but PTO automatically designs operators for arbitrary representations

Heartbeat type classification with optimized feature vectors

In this study, a feature vector optimization based method has been proposed for classification of the heartbeat types. Electrocardiogram (ECG) signals of five different heartbeat type were used for this aim. Firstly, wavelet transform (WT) method were applied on these ECG signals to generate all feature vectors. Optimizing these feature vectors is provided by performing particle swarm optimization (PSO), genetic search, best first, greedy stepwise and multi objective evoluationary algorithms on these vectors. These optimized feature vectors are later applied to the classifier inputs for performance evaluation. A comprehensive assessment was presented for the determination of optimized feature vectors for ECG signals and best-performing classifier for these optimized feature vectors was determined.</jats:p

HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality

A takeover time-driven adaptive evolutionary algorithm for mobile user tracking in pre-5G cellular networks

Cellular networks are one of today’s most popular means of communication. This fact has made the mobile phone industry subject to a huge scientific and economic competition, where the quality of service is key. Such a quality is measured on the basis of reliability, speed and accuracy when delivering a service to a user no matter his location or behaviour are. This fact has placed the users’ tracking process among the most difficult and determining issues in cellular network design. In this paper, we present an adaptive bi-phased evolutionary algorithm based on the takeover time to solve this problem. The proposal is thoroughly assessed by tackling twenty-five real-world instances of different sizes. Twenty-eight of the state-of-the-art techniques devised to address the users’ mobility problem have been taken as the comparison basis, and several statistical tests have been also conducted. Experiments have demonstrated that our solver outperforms most of the top-ranked algorithms.This research is partially funded by the Universidad de Málaga, Consejería de Economía y Conocimiento de la Junta de Andalucía and FEDER under grant number UMA18-FEDERJA-003 (PRECOG); MCIN/AEI/10.13039/501100011033 under grant number PID 2020-116727RB-I00 (HUmove) and under TAILOR ICT-48 Network (No952215) funded by EU Horizon 2020 research and innovation programme. Funding for open access charge is supported by the Universidad de Málaga/CBUA. The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission. We also acknowledge that some instances studied in our work were previously inspired from the CRAWDAD dataset spitz/cellular. The authors would like also to address special thanks to Mrs Malika Belaifa and Mrs Zeineb Dahi for their help in creating the realistic problem benchmarks

Geometric Particle Swarm Optimisation

Abstract. Using a geometric framework for the interpretation of crossover of recent introduction, we show an intimate connection between particle swarm optimization (PSO) and evolutionary algorithms. This connection enables us to generalize PSO to virtually any solution representation in a natural and straightforward way. We demonstrate this for the cases of Euclidean, Manhattan and Hamming spaces.