Feature selection using enhanced particle swarm optimisation for classification models.

In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets

Optimización de ecuaciones con restricciones no lineales: comparativo entre técnicas heurística y convexa

In this article, different optimization techniques were explored through different methodologies. It is important to highlight that optimization problems are found in a large number of academic disciplines and the paths proposed to solve them are found first in the so-called strong mathematical techniques (global optimum) through existence and uniqueness theorems, and the second way, the so-called heuristic or metaheuristic techniques, inspired mostly by biological, social, and cultural processes which allow expanding the search spaces for solutions or relaxing the functions to be optimized from continuous to non-continuous as well as constraints. The metaheuristic technique studied is the particle swarm optimization, (PSO) based on the complete model (cognitive and social components) which is a metaheuristic technique inspired by biology, comparatively with the convex mathematical technique using the behavior of positive semi-definite matrices, for the formulation and modeling of problems with objective functions and convex feasible regions. The problem solved by these two methods consists of knowing the values of the resources of two variables within an objective function. Finally, the answers obtained are evaluated under the assumption that the local minima are global minima within the neighborhood.En el presente artículo se exploran diversas técnicas de optimización a través de metodologías diferentes; es importante resaltar que los problemas de optimización se encuentran en una gran multitud de disciplinas académicas, y los caminos propuestos para resolverlos se encuentran, el primero, en las técnicas matemáticas denominadas fuertes (óptimo global) a través de teoremas de existencia y unicidad, y el segundo camino, en las denominadas técnicas heurísticas o metaheurísticas inspiradas en su mayoría en procesos biológicos, sociales, culturales, las cuales permiten ampliar los espacios de búsqueda de las soluciones o relajar las funciones por optimizar de continuas a no continuas, al igual que las restricciones. La técnica metaheurística estudiada es el enjambre de partículas, (PSO) basada en el modelo completo (componentes cognitiva y social), el cual es una técnica metaheurística inspirada en la biología, comparativamente con la técnica matemática convexa utilizando el comportamiento de las matrices semidefinidas positivas, para el planteamiento y modelado de problemas con funciones objetivo y regiones factibles convexas. El problema resuelto por estos dos métodos consiste en conocer los valores de los recursos de dos variables dentro de una función objetivo. Por último, se evalúan las respuestas obtenidas bajo la suposición de que los mínimos locales son mínimos globales dentro de la vecindad

Evolving machine learning and deep learning models using evolutionary algorithms

Despite the great success in data mining, machine learning and deep learning models are yet subject to material obstacles when tackling real-life challenges, such as feature selection, initialization sensitivity, as well as hyperparameter optimization. The prevalence of these obstacles has severely constrained conventional machine learning and deep learning methods from fulfilling their potentials. In this research, three evolving machine learning and one evolving deep learning models are proposed to eliminate above bottlenecks, i.e. improving model initialization, enhancing feature representation, as well as optimizing model configuration, respectively, through hybridization between the advanced evolutionary algorithms and the conventional ML and DL methods. Specifically, two Firefly Algorithm based evolutionary clustering models are proposed to optimize cluster centroids in K-means and overcome initialization sensitivity as well as local stagnation. Secondly, a Particle Swarm Optimization based evolving feature selection model is developed for automatic identification of the most effective feature subset and reduction of feature dimensionality for tackling classification problems. Lastly, a Grey Wolf Optimizer based evolving Convolutional Neural Network-Long Short-Term Memory method is devised for automatic generation of the optimal topological and learning configurations for Convolutional Neural Network-Long Short-Term Memory networks to undertake multivariate time series prediction problems. Moreover, a variety of tailored search strategies are proposed to eliminate the intrinsic limitations embedded in the search mechanisms of the three employed evolutionary algorithms, i.e. the dictation of the global best signal in Particle Swarm Optimization, the constraint of the diagonal movement in Firefly Algorithm, as well as the acute contraction of search territory in Grey Wolf Optimizer, respectively. The remedy strategies include the diversification of guiding signals, the adaptive nonlinear search parameters, the hybrid position updating mechanisms, as well as the enhancement of population leaders. As such, the enhanced Particle Swarm Optimization, Firefly Algorithm, and Grey Wolf Optimizer variants are more likely to attain global optimality on complex search landscapes embedded in data mining problems, owing to the elevated search diversity as well as the achievement of advanced trade-offs between exploration and exploitation

Evaluation and optimisation of traction system for hybrid railway vehicles

Over the past decade, energy and environmental sustainability in urban rail transport have become increasingly important. Hybrid transportation systems present a multifaceted challenge, encompassing aspects such as hydrogen production, refuelling station infrastructure, propulsion system topology, power source sizing, and control. The evaluation and optimisation of these aspects are critical for the adaptation and commercialisation of hybrid railway vehicles. While there has been significant progress in the development of hybrid railway vehicles, further improvements in propulsion system design are necessary. This thesis explores strategies to achieve this ambitious goal by substituting diesel trains with hybrid trains. However, limited research has assessed the operational performance of replacing diesel trains with hybrid trains on the same tracks. This thesis develops various optimisation techniques for evaluating and refining the hybrid traction system to address this gap. In this research's first phase, the author developed a novel Hybrid Train Simulator designed to analyse driving performance and energy flow among multiple power sources, such as internal combustion engines, electrification, fuel cells, and batteries. The simulator incorporates a novel Automatic Smart Switching Control technique, which scales power among multiple power sources based on the route gradient for hybrid trains. This smart switching approach enhances battery and fuel cell life and reduces maintenance costs by employing it as needed, thereby eliminating the forced charging and discharging of excessively high currents. Simulation results demonstrate a 6% reduction in energy consumption for hybrid trains equipped with smart switching compared to those without it. In the second phase of this research, the author presents a novel technique to solve the optimisation problem of hybrid railway vehicle traction systems by utilising evolutionary and numerical optimisation techniques. The optimisation method employs a nonlinear programming solver, interpreting the problem via a non-convex function combined with an efficient "Mayfly algorithm." The developed hybrid optimisation algorithm minimises traction energy while using limited power to prevent unnecessary load on power sources, ensuring their prolonged life. The algorithm takes into account linear and non-linear variables, such as velocity, acceleration, traction forces, distance, time, power, and energy, to address the hybrid railway vehicle optimisation problem, focusing on the energy-time trade-off. The optimised trajectories exhibit an average reduction of 16.85% in total energy consumption, illustrating the algorithm's effectiveness across diverse routes and conditions, with an average increase in journey times of only 0.40% and a 15.18% reduction in traction power. The algorithm achieves a well-balanced energy-time trade-off, prioritising energy efficiency without significantly impacting journey duration, a critical aspect of sustainable transportation systems. In the third phase of this thesis, the author introduced artificial neural network models to solve the optimisation problem for hybrid railway vehicles. Based on time and power-based architecture, two ANN models are presented, capable of predicting optimal hybrid train trajectories. These models tackle the challenge of analysing large datasets of hybrid railway vehicles. Both models demonstrate the potential for efficiently predicting hybrid train target parameters. The results indicate that both ANN models effectively predict a hybrid train's critical parameters and trajectory, with mean errors ranging from 0.19% to 0.21%. However, the cascade-forward neural network topology in the time-based architecture outperforms the feed-forward neural network topology in terms of mean squared error and maximum error in the power-based architecture. Specifically, the cascade-forward neural network topology within the time-based structure exhibits a slightly lower MSE and maximum error than its power-based counterpart. Moreover, the study reveals the average percentage difference between the benchmark and FFNN/CNFN trajectories, highlighting that the time-based architecture exhibits lower differences (0.18% and 0.85%) compared to the power-based architecture (0.46% and 0.92%)