Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Baseline demand responsiveness framework for the conventional grid through appliance scheduling by evolutionary metaheuristics.

Doctoral Degree. University of KwaZulu-Natal, Durban.A major problem of many energy environments nowadays, is an obsolete and highly inefficient electricity supply system, the Conventional Grid (CG), characterized by a high peak to average ratio, out of an uncontrollable demand, worsened by a native lack of communications infrastructures and resources for performing a proper automated demand side management, which has resulted in blackouts, harsh user discomfort, high electricity cost, huge economic losses and a high carbon footprint. Designed to tackle this problem is the emerging Smart Grid (SG). Most research works are devoted to providing automation and efficiency to the SG (or the intermediate SG-like) environments. There is a scarcity of research devoted to providing automated demand responsiveness to the information layer deprived CG environments, although as evident, an Automated Demand Response (ADR) is badly required, since there is still a long way until we get to the SG, all the more when developing world is concerned. Such context, set our focus towards the CG. So, this research work, developed a framework for providing a "blind" baseline Demand Responsiveness (bbDR) for CG environments, wherein, a pseudo real time electricity pricing function, built from a country load profile, is used as a guiding function for the autonomous scheduling of controllable appliances, which seeks to improve electricity consumption patterns, while also preserving user satisfaction by complying to their preferences. For performance evaluation, the optimized energy consumption patterns (peak load, peak to average ratio, load and cost profiles and mean energy rate) of the controlled use of appliances, are compared to those ones produced by their uncontrolled use. The controlled usage schedules are produced by an evolutionary metaheuristics, whilst the uncontrolled usage is stochastically generated from appliances’ rate-of-use probabilities sourced from the literature. The results proved that, such framework is capable of, without DR communications, delivering meaningful, ADR-like, performances to a communications deprived CG environment. As part of the work for simulating the above bbDR framework, we developed and demonstrated a Real Parameter Blackbox Optimization Approach to Appliance Scheduling (RPBBOAS) model, which describes the household, and provides the logical interface with the optimization algorithms. This real parameter model, vis-a-vis its discrete parameter counterpart, tackles combinatorial explosion by, in a novel way, reducing the problem dimension that is traded with the external blackbox optimization algorithms, in such a way that boosts performance and widens the window of applicable algorithms. While developing the above RPBBOAS model, readily available state-of-the-art metaheuristics showed a lackluster performance, which propelled us to design a novel hybrid evolutionary metaheuristics (Hy- PERGDx) that was eventually used in the bbDR simulations. It showed a better all-around performance and robustness vs the state-of-the-art, when benchmarked on a wide range of non-linear problems. Overall, such deliveries, demonstrated the potential of the proposed bbDR framework for improving demand patterns and quality of service figures, in a communication free way, which with an appropriate follow-up development, makes it suitable for application in severely affected, communications deprived (or communications limited), energy networks such as South Africa or worse energy ecosystems.Author's Keywords : Appliance, Appliance Scheduling, Household Energy Management, Demand Side Management, Demand Response, Conventional Grid, Smart Grid, Smart Load, Smart Appliance, Grid Friendly Appliance, Swarm Intelligence, Evolutionary Computation, Heuristic optimization, Blackbox Optimization, Metaheuristics, Hybrid Algorithms

A sequential quadratic programming based strategy for particle swarm optimization on single-objective numerical optimization

Over the last decade, particle swarm optimization has become increasingly sophisticated because well-balanced exploration and exploitation mechanisms have been proposed. The sequential quadratic programming method, which is widely used for real-parameter optimization problems, demonstrates its outstanding local search capability. In this study, two mechanisms are proposed and integrated into particle swarm optimization for single-objective numerical optimization. A novel ratio adaptation scheme is utilized for calculating the proportion of subpopulations and intermittently invoking the sequential quadratic programming for local search start from the best particle to seek a better solution. The novel particle swarm optimization variant was validated on CEC2013, CEC2014, and CEC2017 benchmark functions. The experimental results demonstrate impressive performance compared with the state-of-the-art particle swarm optimization-based algorithms. Furthermore, the results also illustrate the effectiveness of the two mechanisms when cooperating to achieve significant improvement

A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.This work was supported by grants from the Spanish Ministry of Science (TIN2016-8113-R, TIN2017-89517-P and TIN2017-83132-C2- 2-R) and Universidad Politécnica de Madrid (PINV-18-XEOGHQ-19- 4QTEBP). Eneko Osaba and Javier Del Ser-would also like to thank the Basque Government for its funding support through the ELKARTEK and EMAITEK programs. Javier Del Ser-receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government