Guiding evolutionary search towards innovative solutions

The main goal of this work is to develop a method that, operating on top of an Evolutionary Algorithm, increases its likeliness of finding innovative solutions. This likeliness is laid out to be increased with the diversity of the solutions found, provided that they are of sufficient quality. The developed method needs to be applicable in a scenario in which the search is required to be started from a single, fixed solution. Therefore, a scheme is envisioned in which the search is performed in a sequential fashion, zooming in on a locally-optimal solution, and then exploring for a new potentially high-quality region based on a memory of solutions encountered earlier in the search. Two exploration criteria, one using an archive of earlier solutions as memory and the other deriving from a surrogate model trained on earlier solutions, were established to be worthwhile for integration into quality-based search. The resulting schemes were applied to a real-world airfoil optimization task, showing both to perform better than the baseline method of multiple standard optimization runs. The model-based approach delivers the best results, in the sense that it finds more solutions, more diverse solutions, and better-quality solutions than the baseline method.Honda Research Institute Europe (HRI-EU)Algorithms and the Foundations of Software technolog

Forecasting tools and probabilistic scheduling approach incorporatins renewables uncertainty for the insular power systems industry

Nowadays, the paradigm shift in the electricity sector and the advent of the smart grid, along with the growing impositions of a gradual reduction of greenhouse gas emissions, pose numerous challenges related with the sustainable management of power systems. The insular power systems industry is heavily dependent on imported energy, namely fossil fuels, and also on seasonal tourism behavior, which strongly influences the local economy. In comparison with the mainland power system, the behavior of insular power systems is highly influenced by the stochastic nature of the renewable energy sources available. The insular electricity grid is particularly sensitive to power quality parameters, mainly to frequency and voltage deviations, and a greater integration of endogenous renewables potential in the power system may affect the overall reliability and security of energy supply, so singular care should be placed in all forecasting and system operation procedures. The goals of this thesis are focused on the development of new decision support tools, for the reliable forecasting of market prices and wind power, for the optimal economic dispatch and unit commitment considering renewable generation, and for the smart control of energy storage systems. The new methodologies developed are tested in real case studies, demonstrating their computational proficiency comparatively to the current state-of-the-art

Artificial Intelligence and Its Application in Optimization under Uncertainty

Nowadays, the increase in data acquisition and availability and complexity around optimization make it imperative to jointly use artificial intelligence (AI) and optimization for devising data-driven and intelligent decision support systems (DSS). A DSS can be successful if large amounts of interactive data proceed fast and robustly and extract useful information and knowledge to help decision-making. In this context, the data-driven approach has gained prominence due to its provision of insights for decision-making and easy implementation. The data-driven approach can discover various database patterns without relying on prior knowledge while also handling flexible objectives and multiple scenarios. This chapter reviews recent advances in data-driven optimization, highlighting the promise of data-driven optimization that integrates mathematical programming and machine learning (ML) for decision-making under uncertainty and identifies potential research opportunities. This chapter provides guidelines and implications for researchers, managers, and practitioners in operations research who want to advance their decision-making capabilities under uncertainty concerning data-driven optimization. Then, a comprehensive review and classification of the relevant publications on the data-driven stochastic program, data-driven robust optimization, and data-driven chance-constrained are presented. This chapter also identifies fertile avenues for future research that focus on deep-data-driven optimization, deep data-driven models, as well as online learning-based data-driven optimization. Perspectives on reinforcement learning (RL)-based data-driven optimization and deep RL for solving NP-hard problems are discussed. We investigate the application of data-driven optimization in different case studies to demonstrate improvements in operational performance over conventional optimization methodology. Finally, some managerial implications and some future directions are provided

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

Multidimensional approaches to performance evaluation of competing forecasting models

The purpose of my research is to contribute to the field of forecasting from a methodological perspective as well as to the field of crude oil as an application area to test the performance of my methodological contributions and assess their merits. In sum, two main methodological contributions are presented. The first contribution consists of proposing a mathematical programming based approach, commonly referred to as Data Envelopment Analysis (DEA), as a multidimensional framework for relative performance evaluation of competing forecasting models or methods. As opposed to other performance measurement and evaluation frameworks, DEA allows one to identify the weaknesses of each model, as compared to the best one(s), and suggests ways to improve their overall performance. DEA is a generic framework and as such its implementation for a specific relative performance evaluation exercise requires a number of decisions to be made such as the choice of the units to be assessed, the choice of the relevant inputs and outputs to be used, and the choice of the appropriate models. In order to present and discuss how one might adapt this framework to measure and evaluate the relative performance of competing forecasting models, we first survey and classify the literature on performance criteria and their measures – including statistical tests – commonly used in evaluating and selecting forecasting models or methods. In sum, our classification will serve as a basis for the operationalisation of DEA. Finally, we test DEA performance in evaluating and selecting models to forecast crude oil prices. The second contribution consists of proposing a Multi-Criteria Decision Analysis (MCDA) based approach as a multidimensional framework for relative performance evaluation of the competing forecasting models or methods. In order to present and discuss how one might adapt such framework, we first revisit MCDA methodology, propose a revised methodological framework that consists of a sequential decision making process with feedback adjustment mechanisms, and provide guidelines as to how to operationalise it. Finally, we adapt such a methodological framework to address the problem of performance evaluation of competing forecasting models. For illustration purposes, we have chosen the forecasting of crude oil prices as an application area

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.Zlepšení průmyslových procesů, Model založený na datech, Optimalizace procesu, Strojové učení, Průmyslové systémy, Energeticky náročná průmyslová odvětví, Umělá inteligence.

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining