An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data

Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data. A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used. PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Analysis and Synthesis of Magnetically Negative (MNG) Material using Softcomputing Techniques

Unique properties of Metamaterial are widely used in Electromagnetic Engineering, and the metamaterial has gained significant attention to be a major research area. Some of its recent research areas are carpet cloaking and metasurface design. The unique properties of these materials include simultaneous negative electromagnetic property, i.e., both permeability and permittivity are negative, because of which a negative refractive index is generated.Thus there are three primary classes of metamaterials. When only the permittivity is negative, the material is called ENG (Electrical Negative). Similarly material with only negative permeability is known as MNG (Magnetic Negative). Further when both are negative the material is regarded as DNG (Double Negative). Out of these three, the analysis and synthesis of MNG is very complicated and difficult. Therefore, the focus in this work is only on MNG, and the word "metamaterial" refers to MNG unless otherwise mentioned specifically. These type of materials don’t occur in nature and hence manufactured by making array of small unit cells of specific structure(s) made up of conductors. Although the concept of the existence of negative refractive index was proposed in the 1960s by Veselago, it took around 40 years to be verified practically when smith et al. did the experiment in 2001. They used an array of unit cell structures as Split-Ring-Resonators (SRR) and thin wires to verify the concept. Thereafter researchers are working to develop different forms of metamaterial unit cells and for which metamaterial is still an open area of research. However, while designing a metamaterial unit cell, absence of an empirical formula makes the model analysis and synthesis difficult. Although with the help of EM simulation tools this is possible, it usually is too difficult, time consuming and costly. Due to this researchers are motivated to look for alternative methods. In this work, some techniques to develop CAD models are presented based on soft computing techniques for metamaterial analysis and synthesis. Use of different soft computing techniques in the field of microwave engineering is documented in the literature. However, unconventional unit cell structures are difficult to analysis because of unavailability of predefined mathematical formulas and equivalent analysis. This can be done by the complex Modified Nicolson-Ross-Weir (NRW) method with the support of EM simulation tools which are expensive. Frequency dependency of metamaterial characteristics for any kind of unit cell structure follows a similar pattern which is obtained from Lorentz model. The basic idea in this work, which develops CAD Models for metamaterial unit cell of unconventional structures is based on the assumption that each type of unit cell can be mapped to an equivalent SRR structure, for which empirical formula is available. This is done by implementing the concept of Space Mapping technique or surrogate based modeling. Most important contribution of the work is the development of Space Mapped CAD model for analysis of an Ω atom. The developed model is validated with a Deformed-Ω atom, which is developed by integrating the concept of Space Mapping (SM) and Artificial Neural Network. Thereafter, the work progresses with proposing CAD models for synthesis of SRR. The objective is to find the design parameters of SRR for a desired material characteristic and frequency. With the availability of only a complex non-linear analysis formula, the synthesis becomes a reverse engineering problem, which is difficult to process. Three different models are proposed to solve the problem. The first approach is use of Inverse Artificial Neural Network concept, which uses a trained neural network (IANN) to perform output-to-input mapping. The developed CAD model using this approach includes integration of three concepts: IANN, Prior Knowledge Input-Difference (PKI-D) and SM. Although the model is capable of synthesizing a metamaterial unit cell, still it has some disadvantages. To overcome the disadvantages (such as lower convergence rate, lower accuracy and complex programming), use of Evolutionary Algorithms (Genetic Algorithm and Differential Evolution) is proposed. While developing CAD model based on EA, the methodology is first tested by synthesizing Rectangular Microstrip Antenna (RMPA) and then using the same concept, an SRR is synthesized. A comparison shows DE based model to be more efficient than IANN and GA based models in terms of convergence speed, accuracy and robustness

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Short-Range Super-Resolution Feature Extraction of Complex Edged Contours for Object Recognition by Ultra-Wideband Radar

This thesis contributes to the field of short-range ultra-wideband (UWB) Radar. In particular, an object recognition approach performed by a bi-static UWB Radar has been investigated in this thesis. The investigated objects consist of simple canonical and some polygonal complex objects which are scanned on a circular track at about 1 m distance. Geometrical features, texture features and moment based features are extracted from the Radar data to carry out the recognition. Yet, the precise temporal evolution is subject to massive distortions, mainly caused by severe interference conditions and transient effects of the hardware. Thus, super-resolution algorithms have been developed which go far beyond the classical bandwidth given resolution and asked for research on various fields: (i) An innovative wavefront extraction algorithm with polarimetric diversity exploitation has been developed to separate pulses which overlap almost the whole pulse duration; (ii) a highly precise feature extraction algorithm has been developed which localises significant scattering centres by processing the previously extracted wavefronts; (iii) a novel UWB object recognition algorithm has been developed to classify and discriminate the resulting microwave images. When scanning objects from all sides, exceptional recognition of objects was achieved by a minimum mean squared error classifier. Further improvement in recognition was obtained, especially at severly restricted tracks, by the application of Bayes theory which constitutes a superior classifier to the above. In addition to the main field of research, a novel stereoscopic 3D UWB imaging algorithm, based on a spatially spanned synthetic aperture in conjunction with ellipsoidal shaped wavefronts, has been developed. The ultimate test of any model and system is an experimental validation. Consequently in this thesis, all developed algorithms and the object recognition as a whole system are experimentally validated within an elaborate measurement campaign

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Differential Evolution: A Survey and Analysis

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.http://dx.doi.org/10.3390/app810194

Proportional resonant control of three-phase grid-connected inverter during abnormal grid conditions

PhD ThesisThe development of using grid-connected three-phase inverter has augmented the standing of realizing muted distortion along with high-quality current waveform. The standard three-phase grid-connected inverter is the full-bridge voltage source inverter. This inverter is usually controlled by proportional integral (PI) controller in order to ensure sinusoidal current injection to the grid. Although the PI controller is well established and easy to use under normal grid conditions, it leads to system instability under abnormal grid conditions. When abnormal grid conditions are likely to occur, the control system with PI controller can be configured to include two separate PI controllers for the positive and negative sequence components of the grid current. However, this increases control complexity and total harmonic distortion (THD). More recently, the proportional resonant (PR) controller started to replace PI controller in a different application including grid-connected current control. In this thesis, a comprehensive theoretical and experimental comparison between the PI and PR controllers is presented. The comparison shows that the PR controller offers lower total harmonic distortion (THD) in the current signal spectrum and is simpler to implement as it uses only the positive sequence component of the grid current and consequently only one PR controller is needed. For these reasons, the PR controller is adopted in this thesis. Despite the PR controller offering enhanced functioning under abnormal grid conditions compared to PI controller, a sudden change in the grid voltage could additionally raise the error between the reference signal and the controlled signal which results in causing significant divergence from its ostensible value. In this case, the performance of the conventional PR controller will not keep up with the increase in the error which weakens controller performance. To overcome this problem, a new design concept for controlling the current of the three-phase grid connected inverter during normal and abnormal conditions is presented in this thesis. The proposed technique replaces the static control parameters by adaptive control parameters based on a look-up table. This adaptive PR, controller has been investigated and demonstrated with different normal and abnormal grid conditions. The proposed control technique is capable of providing low THD in the injected current even during the occurrence of abnormal grid conditions compared with PI and PR controllers. It also achieves lower overshoot and settling time as well as smaller steady-state error. Proportional Resonance Control of Three-Phase Grid-Connected Inverter II Additionally, despite the fact that both PI and PR controllers are relatively straightforward to tune, and are sometimes capable of dealing with many time-varying grid conditions. This research also presented an adaptive controller tuned using advanced optimization techniques based on particle swarm optimisation (PSO). PSO is presented to optimize the control parameters of both PI and PR controllers for the three-phase grid-connected inverter. There are many advantages of using PSO, such as no additional hardware being required. Thus, it can be extended to other applications and control methods. In addition, the proposed method is a self-tuning method and can thus be suitable for industrial applications where manual tuning is not recommended for time and cost reasons. Simulation and experimental test were carried out to investigate the performance of the proposed techniques. In the simulation, the system was tested under 100 kW model using Matlab/Simulink environment. In addition, the system was also investigated through a practical implementation of the control system using a Digital Signal Processor (DSP) and grid-connected three-phase inverter. This practical system was demonstrated a 300 W scaled-down prototype. As a result, the comparisons between experimental and simulation results show the behaviour and performance of the control to be accurately evaluated