A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed

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

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

Multipurpose Programmable Integrated Photonics: Principles and Applications

[ES] En los últimos años, la fotónica integrada programable ha evolucionado desde considerarse un paradigma nuevo y prometedor para implementar la fotónica a una escala más amplia hacia convertirse una realidad sólida y revolucionaria, capturando la atención de numerosos grupos de investigación e industrias. Basada en el mismo fundamento teórico que las matrices de puertas lógicas programables en campo (o FPGAs, en inglés), esta tecnología se sustenta en la disposición bidimensional de bloques unitarios de lógica programable (en inglés: PUCs) que -mediante una programación adecuada de sus actuadores de fase- pueden implementar una gran variedad de funcionalidades que pueden ser elaboradas para operaciones básicas o más complejas en muchos campos de aplicación como la inteligencia artificial, el aprendizaje profundo, los sistemas de información cuántica, las telecomunicaciones 5/6-G, en redes de conmutación, formando interconexiones en centros de datos, en la aceleración de hardware o en sistemas de detección, entre otros. En este trabajo, nos dedicaremos a explorar varias aplicaciones software de estos procesadores en diferentes diseños de chips. Exploraremos diferentes enfoques de vanguardia basados en la optimización computacional y la teoría de grafos para controlar y configurar con precisión estos dispositivos. Uno de estos enfoques, la autoconfiguración, consiste en la síntesis automática de circuitos ópticos -incluso en presencia de efectos parasitarios como distribuciones de pérdidas no uniformes a lo largo del diseño hardware, o bajo interferencias ópticas y eléctricas- sin conocimiento previo sobre el estado del dispositivo. Hay ocasiones, sin embargo, en las que el acceso a esta información puede ser útil. Las herramientas de autocalibración y autocaracterización nos permiten realizar una comprobación rápida del estado de nuestro procesador fotónico, lo que nos permite extraer información útil como la corriente eléctrica que suministrar a cada actuador de fase para cambiar el estado de su PUC correspondiente, o las pérdidas de inserción de cada unidad programable y de las interconexiones ópticas que rodean a la estructura. Estos mecanismos no solo nos permiten identificar rápidamente cualquier PUC o región del chip defectuosa en nuestro diseño, sino que también revelan otra alternativa para programar circuitos fotónicos en nuestro diseño a partir de valores de corriente predefinidos. Estas estrategias constituyen un paso significativo para aprovechar todo el potencial de estos dispositivos. Proporcionan soluciones para manejar cientos de variables y gestionar simultáneamente múltiples acciones de configuración, una de las principales limitaciones que impiden que esta tecnología se extienda y se convierta en disruptiva en los próximos años.[CA] En els darrers anys, la fotònica integrada programable ha evolucionat des de considerarse un paradigma nou i prometedor per implementar la fotònica a una escala més ampla cap a convertir-se en una realitat sòlida i revolucionària, capturant l'atenció de nombrosos grups d'investigaciò i indústries. Basada en el mateix fonament teòric que les matrius de portes lògiques programable en camp (o FPGAs, en anglès), aquesta tecnología es sustenta en la disposición bidimensional de blocs units lògics programables (en anglès: PUCs) que -mitjançant una programación adequada dels seus actuadors de fase- poden implementar una gran varietat de funcionalitats que poden ser elaborades per a operacions bàsiques o més complexes en molts camps d'aplicació com la intel·ligència artificial, l'aprenentatge profund, els sistemes d'informació quàntica, les telecomunicacions 5/6-G, en xarxes de comutació, formant interconnexions en centres de dades, en l'acceleració de hardware o en sistemes de detecció, entre d'altres. En aquest treball, ens dedicarem a explorar diverses capatitats de programari d'aquests processadors en diferents dissenys de xips. Explorem diferents enfocaments de vanguardia basats en l'optimització computacional i la teoría de grafs per controlar i configurar amb precisió aquests dispositius. Un d'aquests enfocaments, l'autoconfiguració, tracta de la síntesi automática de circuits òptics -fins i tot en presencia d'efectes parasitaris com ara pèrdues no uniformes o crosstalk òptic i elèctric- sense cap coneixement previ sobre l'estat del dispositiu. Tanmateix, hi ha ocasions en les quals l'accés a aquesta información pot ser útil. Les eines d'autocalibració i autocaracterització ens permeten realizar una comprovació ràpida de l'estat del nostre procesador fotònic, el que ens permet obtener informació útil com la corrent eléctrica necessària per alimentar cada actuador de fase per canviar l'estat del seu PUC corresponent o la pèrdua d'inserció de cada unitat programable i de les interconnexions òptiques que envolten l'estructura. Aquests mecanisms no només ens permeten identificar ràpidament qualsevol PUC o área del xip defectuosa en el nostre disseny , sinó que també ens mostren una altra alternativa per programar circuits fotònics en el nostre disseny a partir de valors de corrent predefinits. Aquestes estratègies constitueixen un pas gegant per a aprofitar tot el potencial d'aquests dispositius. Proporcionen solucions per a gestionar centenars de variables i alhora administrar múltiples accions de configuració, una de les principals limitacions que impideixen que aquesta tecnología esdevingui disruptiva en els pròxims anys.[EN] In recent years, programmable integrated photonics (PIP) has evolved from a promising, new paradigm to deploy photonics to a larger scale to a solid, revolutionary reality, bringing up the attention of numerous research and industry players. Based on the same theoretical foundations than field-programmable gate arrays (FPGAs), this technology relies on common, two-dimensional integrated optical hardware configurations based on the interconnection of programmable unit cells (PUCs), which -by suitable programming of their phase actuators- can implement a variety of functionalities that can be elaborated for basic or more complex operation in many application fields, such as artificial intelligence, deep learning, quantum information systems, 5/6-G telecommunications, switching, data center interconnections, hardware acceleration and sensing, amongst others. In this work, we will dedicate ourselves to explore several software capabilities of these processors under different chip designs. We explore different cutting-edge approaches based on computational optimization and graph theory to precisely control and configure these devices. One of these, self-configuration, deals with the automated synthesis of optical circuit configurations -even in presence of parasitic effects such as nonuniform losses, optical and electrical crosstalk- without any need for prior knowledge about hardware state. There are occasions, though, in which accessing to this information may be of use. Self-calibration and self-characterization tools allow us to perform a quick check to our photonic processor's status, allowing us to retrieve useful pieces of information such as the electrical current needed to supply to each phase actuator to change its corresponding PUC state arbitrarily or the insertion loss of every unit cell and optical interconnection surrounding the structure. These mechanisms not only allow us to quickly identify any malfunctioning PUCs or chip areas in our design, but also reveal another alternative to program photonic circuits in our design from current pre-sets. These strategies constitute a gigantic step to unleash all the potential of these devices. They provide solutions to handle with hundreds of variables and simultaneously manage multiple configuration actions, one of the main limitations that prevent this technology to scale up and become disruptive in the years to come.López Hernández, A. (2023). Multipurpose Programmable Integrated Photonics: Principles and Applications [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19686

Analysis of physiological signals using machine learning methods

Technological advances in data collection enable scientists to suggest novel approaches, such as Machine Learning algorithms, to process and make sense of this information. However, during this process of collection, data loss and damage can occur for reasons such as faulty device sensors or miscommunication. In the context of time-series data such as multi-channel bio-signals, there is a possibility of losing a whole channel. In such cases, existing research suggests imputing the missing parts when the majority of data is available. One way of understanding and classifying complex signals is by using deep neural networks. The hyper-parameters of such models have been optimised using the process of back propagation. Over time, improvements have been suggested to enhance this algorithm. However, an essential drawback of the back propagation can be the sensitivity to noisy data. This thesis proposes two novel approaches to address the missing data challenge and back propagation drawbacks: First, suggesting a gradient-free model in order to discover the optimal hyper-parameters of a deep neural network. The complexity of deep networks and high-dimensional optimisation parameters presents challenges to find a suitable network structure and hyper-parameter configuration. This thesis proposes the use of a minimalist swarm optimiser, Dispersive Flies Optimisation(DFO), to enable the selected model to achieve better results in comparison with the traditional back propagation algorithm in certain conditions such as limited number of training samples. The DFO algorithm offers a robust search process for finding and determining the hyper-parameter configurations. Second, imputing whole missing bio-signals within a multi-channel sample. This approach comprises two experiments, namely the two-signal and five-signal imputation models. The first experiment attempts to implement and evaluate the performance of a model mapping bio-signals from A toB and vice versa. Conceptually, this is an extension to transfer learning using CycleGenerative Adversarial Networks (CycleGANs). The second experiment attempts to suggest a mechanism imputing missing signals in instances where multiple data channels are available for each sample. The capability to map to a target signal through multiple source domains achieves a more accurate estimate for the target domain. The results of the experiments performed indicate that in certain circumstances, such as having a limited number of samples, finding the optimal hyper-parameters of a neural network using gradient-free algorithms outperforms traditional gradient-based algorithms, leading to more accurate classification results. In addition, Generative Adversarial Networks could be used to impute the missing data channels in multi-channel bio-signals, and the generated data used for further analysis and classification tasks

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing