A novel hybrid teaching learning based multi-objective particle swarm optimization

How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta-heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical PSO search with a teaching-learning-based optimization (TLBO) algorithm in order to promote the diversity and improve search ability. Also, CCS technique is developed to improve the diversity and spread of solutions when truncating the external elitism archive. The performance of HTL-MOPSO algorithm was tested on several well-known benchmarks problems and compared with other state-of-the-art MOO algorithms in respect of convergence and spread of final solutions to the true Pareto front. Also, the individual contributions made by the strategies of HTL-PSO and CCS are analyzed. Experimental results validate the effectiveness of HTL-MOPSO and demonstrate its superior ability to find solutions of better spread and diversity, while assuring a good convergence

Reservoir water release dynamic decision model based on spatial temporal pattern

The multi-purpose reservoir water release decision requires an expert to make a decision by assembling complex decision information that occurred in real time. The decision needs to consider adequate reservoir water balance in order to maintain reservoir multi-purpose function and provide enough space for incoming heavy rainfall and inflow. Crucially, the water release should not exceed the downstream maximum river level so that it will not cause flood. The rainfall and water level are fuzzy information, thus the decision model needs the ability to handle the fuzzy information. Moreover, the rainfalls that are recorded at different location take different time to reach into the reservoir. This situation shows that there is spatial temporal relationship hidden in between each gauging station and the reservoir. Thus, this study proposed dynamic reservoir water release decision model that utilize both spatial and temporal information in the input pattern. Based on the patterns, the model will suggest when the reservoir water should be released. The model adopts Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to deal with the fuzzy information. The data used in this study was obtained from the Perlis Department of Irrigation and Drainage. The modified Sliding Window algorithm was used to construct the rainfall temporal pattern, while the spatial information was established by simulating the mapped rainfall and reservoir water level pattern. The model performance was measured based on the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Findings from this study shows that ANFIS produces the lowest RMSE and MAE when compare to Autoregressive Integrated Moving Average (ARIMA) and Backpropagation Neural Network (BPNN) model. The model can be used by the reservoir operator to assist their decision making and support the new reservoir operator in the absence of an experience reservoir operator

Adaptive algorithms for history matching and uncertainty quantification

Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied

A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

Due to its fast convergence and population-based nature, particle swarm optimization (PSO) has been widely applied to address the multiobjective optimization problems (MOPs). However, the classical PSO has been proved to be not a global search algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective PSO-based algorithms. In this paper, making full use of the global convergence property of quantum-behaved particle swarm optimization (QPSO), a novel multiobjective QPSO algorithm based on the ring model is proposed. Based on the ring model, the position-update strategy is improved to address MOPs. The employment of a novel communication mechanism between particles effectively slows down the descent speed of the swarm diversity. Moreover, the searching ability is further improved by adjusting the position of local attractor. Experiment results show that the proposed algorithm is highly competitive on both convergence and diversity in solving the MOPs. In addition, the advantage becomes even more obvious with the number of objectives increasing

METHODOLOGIES FOR RESERVOIR SYSTEMS ANALYSIS: APPLICATION OF OPTIMIZATION AND DEEP LEARNING

Reservoir systems operations are challenging given that they must function to meet conflicting goals. Using mathematical programming and deep learning techniques, this dissertation presents innovative methodologies to address some of the challenges. The first chapter focuses on development of a mathematical programming framework for assessing sub-daily hydropower hydropeaking operation and flow regime outcomes of a system of five large sequential hydropower facilities on the mainstem Connecticut River under various operation scenarios. A formulation for the pumped-storage Northfield reservoir is presented that uses binary decision variables to properly model the reservoir operations. The results closely match annual historical power values that indicates the model can replicate the operations. The second chapter presents a novel multiple objective optimization methodology for trade-off analysis of river basins. The novelties include a weighting scheme that normalize different objectives having different range of variabilities and formulations for quantification of ecological and flood control objectives as frequencies of meeting desirable conditions. The methodology is applied to the Connecticut River basin. In this chapter, formulations are developed that use binary decision variables to quantify ecological and flood control objectives along with other operational goals. The key trade-offs of the system objectives are identified. The results indicate hydropower revenue objective highly conflict with any other objective than flood control. Moreover, it is concluded that a balanced operation that equally weight different objectives has the potential to improve all the objectives. The third chapter presents a methodology for designing reservoir operation policy using optimization and deep learning. This chapter addresses the challenge of designing of an operation policy for a reservoir with conflicting objectives under uncertainty of hydrological and energy prices data. A deep neural network is developed to infer near-optimal operation policies under different foresight scenarios using the optimization modeling results. The methodology is applied to the Wilder reservoir on the mainstem Connecticut River. A base method is also developed that uses linear regression and is applied to the problem and the associated results are used as a comparison basis. Results indicate that the designed policies using neural networks perform better than the base method used while having foresight for a longer time improves the performance

Multi-agent system for flood forecasting in Tropical River Basin

It is well known, the problems related to the generation of floods, their control, and management, have been treated with traditional hydrologic modeling tools focused on the study and the analysis of the precipitation-runoff relationship, a physical process which is driven by the hydrological cycle and the climate regime and that is directly proportional to the generation of floodwaters. Within the hydrological discipline, they classify these traditional modeling tools according to three principal groups, being the first group defined as trial-and-error models (e.g., "black-models"), the second group are the conceptual models, which are categorized in three main sub-groups as "lumped", "semi-lumped" and "semi-distributed", according to the special distribution, and finally, models that are based on physical processes, known as "white-box models" are the so-called "distributed-models". On the other hand, in engineering applications, there are two types of models used in streamflow forecasting, and which are classified concerning the type of measurements and variables required as "physically based models", as well as "data-driven models". The Physically oriented prototypes present an in-depth account of the dynamics related to the physical aspects that occur internally among the different systems of a given hydrographic basin. However, aside from being laborious to implement, they rely thoroughly on mathematical algorithms, and an understanding of these interactions requires the abstraction of mathematical concepts and the conceptualization of the physical processes that are intertwined among these systems. Besides, models determined by data necessitates an a-priori understanding of the physical laws controlling the process within the system, and they are bound to mathematical formulations, which require a lot of numeric information for field adjustments. Therefore, these models are remarkably different from each other because of their needs for data, and their interpretation of physical phenomena. Although there is considerable progress in hydrologic modeling for flood forecasting, several significant setbacks remain unresolved, given the stochastic nature of the hydrological phenomena, is the challenge to implement user-friendly, re-usable, robust, and reliable forecasting systems, the amount of uncertainty they must deal with when trying to solve the flood forecasting problem. However, in the past decades, with the growing environment and development of the artificial intelligence (AI) field, some researchers have seldomly attempted to deal with the stochastic nature of hydrologic events with the application of some of these techniques. Given the setbacks to hydrologic flood forecasting previously described this thesis research aims to integrate the physics-based hydrologic, hydraulic, and data-driven models under the paradigm of Multi-agent Systems for flood forecasting by designing and developing a multi-agent system (MAS) framework for flood forecasting events within the scope of tropical watersheds. With the emergence of the agent technologies, the "agent-based modeling" and "multiagent systems" simulation methods have provided applications for some areas of hydro base management like flood protection, planning, control, management, mitigation, and forecasting to combat the shocks produced by floods on society; however, all these focused on evacuation drills, and the latter not aimed at the tropical river basin, whose hydrological regime is extremely unique. In this catchment modeling environment approach, it was applied the multi-agent systems approach as a surrogate of the conventional hydrologic model to build a system that operates at the catchment level displayed with hydrometric stations, that use the data from hydrometric sensors networks (e.g., rainfall, river stage, river flow) captured, stored and administered by an organization of interacting agents whose main aim is to perform flow forecasting and awareness, and in so doing enhance the policy-making process at the watershed level. Section one of this document surveys the status of the current research in hydrologic modeling for the flood forecasting task. It is a journey through the background of related concerns to the hydrological process, flood ontologies, management, and forecasting. The section covers, to a certain extent, the techniques, methods, and theoretical aspects and methods of hydrological modeling and their types, from the conventional models to the present-day artificial intelligence prototypes, making special emphasis on the multi-agent systems, as most recent modeling methodology in the hydrological sciences. However, it is also underlined here that the section does not contribute to an all-inclusive revision, rather its purpose is to serve as a framework for this sort of work and a path to underline the significant aspects of the works. In section two of the document, it is detailed the conceptual framework for the suggested Multiagent system in support of flood forecasting. To accomplish this task, several works need to be carried out such as the sketching and implementation of the system’s framework with the (Belief-Desire-Intention model) architecture for flood forecasting events within the concept of the tropical river basin. Contributions of this proposed architecture are the replacement of the conventional hydrologic modeling with the use of multi-agent systems, which makes it quick for hydrometric time-series data administration and modeling of the precipitation-runoff process which conveys to flood in a river course. Another advantage is the user-friendly environment provided by the proposed multi-agent system platform graphical interface, the real-time generation of graphs, charts, and monitors with the information on the immediate event taking place in the catchment, which makes it easy for the viewer with some or no background in data analysis and their interpretation to get a visual idea of the information at hand regarding the flood awareness. The required agents developed in this multi-agent system modeling framework for flood forecasting have been trained, tested, and validated under a series of experimental tasks, using the hydrometric series information of rainfall, river stage, and streamflow data collected by the hydrometric sensor agents from the hydrometric sensors.Como se sabe, los problemas relacionados con la generación de inundaciones, su control y manejo, han sido tratados con herramientas tradicionales de modelado hidrológico enfocados al estudio y análisis de la relación precipitación-escorrentía, proceso físico que es impulsado por el ciclo hidrológico y el régimen climático y este esta directamente proporcional a la generación de crecidas. Dentro de la disciplina hidrológica, clasifican estas herramientas de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos empíricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o semi-distribuidos) dependiendo de la distribución espacial y, por último, los basados en la física, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido, clasifican las aplicaciones de predicción de caudal fluvial en la ingeniería de recursos hídricos en dos tipos con respecto a los valores y parámetros que requieren en: modelos de procesos basados en la física y la categoría de modelos impulsados por datos. Los modelos basados en la física proporcionan una descripción detallada de la dinámica relacionada con los aspectos físicos que ocurren internamente entre los diferentes sistemas de una cuenca hidrográfica determinada. Sin embargo, aparte de ser complejos de implementar, se basan completamente en algoritmos matemáticos, y la comprensión de estas interacciones requiere la abstracción de conceptos matemáticos y la conceptualización de los procesos físicos que se entrelazan entre estos sistemas. Además, los modelos impulsados por datos no requieren conocimiento de los procesos físicos que gobiernan, sino que se basan únicamente en ecuaciones empíricas que necesitan una gran cantidad de datos y requieren calibración de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos de datos y de cómo expresan los fenómenos físicos. La elaboración de modelos hidrológicos para el pronóstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos contratiempos importantes, dada la naturaleza estocástica de los fenómenos hidrológicos, es el desafío de implementar sistemas de pronóstico fáciles de usar, reutilizables, robustos y confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema de la predicción de inundaciones. Sin embargo, en las últimas décadas, con el entorno creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores rara vez han intentado abordar la naturaleza estocástica de los eventos hidrológicos con la aplicación de algunas de estas técnicas. Dados los contratiempos en el pronóstico de inundaciones hidrológicas descritos anteriormente, esta investigación de tesis tiene como objetivo integrar los modelos hidrológicos, basados en la física, hidráulicos e impulsados por datos bajo el paradigma de Sistemas de múltiples agentes para el pronóstico de inundaciones por medio del bosquejo y desarrollo del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicción de inundaciones en el contexto de cuenca hidrográfica tropical. Con la aparición de las tecnologías de agentes, se han emprendido algunos enfoques de simulación recientes en la investigación hidrológica con modelos basados en agentes y sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificación de inundaciones, control y gestión de inundaciones y pronóstico de inundaciones, todos estos enfocado a simulacros de evacuación, y este último no dirigido a la cuenca tropical, cuyo régimen hidrológico es extremadamente único. En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas multi-agente como un sustituto del modelado hidrológico convencional para construir un sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan los datos de redes de sensores hidrométricos (por ejemplo, lluvia , nivel del río, caudal del río) capturado, almacenado y administrado por una organización de agentes interactuantes cuyo objetivo principal es realizar pronósticos de caudal y concientización para mejorar las capacidades de soporte en la formulación de políticas a nivel de cuenca hidrográfica. La primera sección de este documento analiza el estado del arte sobre la investigación actual en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través de los antecedentes preocupantes relacionadas con el proceso hidrológico, las ontologías de inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas, métodos y aspectos teóricos y métodos del modelado hidrológico y sus tipologías, desde los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo hincapié en los sistemas multi-agente, como un enfoque de simulación reciente en la investigación hidrológica. Sin embargo, se destaca que esta sección no contribuye a una revisión integral, sino que su propósito es servir de marco para este tipo de trabajos y una guía para subrayar los aspectos significativos de los trabajos. En la sección dos del documento, se detalla el marco de trabajo propuesto para el sistema multi-agente para el pronóstico de inundaciones. Los trabajos realizados comprendieron el diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo Creencia-Deseo-Intención) para la predicción de eventos de crecidas dentro del concepto de cuenca hidrográfica tropical. Las contribuciones de esta arquitectura propuesta son el reemplazo del modelado hidrológico convencional con el uso de sistemas multi-agente, lo que agiliza la administración de las series de tiempo de datos hidrométricos y el modelado del proceso de precipitación-escorrentía que conduce a la inundación en el curso de un río. Otra ventaja es el entorno amigable proporcionado por la interfaz gráfica de la plataforma del sistema multi-agente propuesto, la generación en tiempo real de gráficos, cuadros y monitores con la información sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace fácil para el espectador con algo o sin experiencia en análisis de datos y su interpretación para tener una idea visual de la información disponible con respecto a la cognición de las inundaciones. Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente para el pronóstico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la información de la serie hidrométrica de datos de lluvia, nivel del río y flujo del curso de agua recolectados por los agentes sensores hidrométricos de los sensores hidrométricos de campo.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: María Araceli Sanchis de Miguel.- Secretario: Juan Gómez Romero.- Vocal: Juan Carlos Corrale

Contributions on evolutionary computation for statistical inference

Evolutionary Computation (EC) techniques have been introduced in the 1960s for dealing with complex situations. One possible example is an optimization problems not having an analytical solution or being computationally intractable; in many cases such methods, named Evolutionary Algorithms (EAs), have been successfully implemented. In statistics there are many situations where complex problems arise, in particular concerning optimization. A general example is when the statistician needs to select, inside a prohibitively large discrete set, just one element, which could be a model, a partition, an experiment, or such: this would be the case of model selection, cluster analysis or design of experiment. In other situations there could be an intractable function of data, such as a likelihood, which needs to be maximized, as it happens in model parameter estimation. These kind of problems are naturally well suited for EAs, and in the last 20 years a large number of papers has been concerned with applications of EAs in tackling statistical issues. The present dissertation is set in this part of literature, as it reports several implementations of EAs in statistics, although being mainly focused on statistical inference problems. Original results are proposed, as well as overviews and surveys on several topics. EAs are employed and analyzed considering various statistical points of view, showing and confirming their efficiency and flexibility. The first proposal is devoted to parametric estimation problems. When EAs are employed in such analysis a novel form of variability related to their stochastic elements is introduced. We shall analyze both variability due to sampling, associated with selected estimator, and variability due to the EA. This analysis is set in a framework of statistical and computational tradeoff question, crucial in nowadays problems, by introducing cost functions related to both data acquisition and EA iterations. The proposed method will be illustrated by means of model building problem examples. Subsequent chapter is concerned with EAs employed in Markov Chain Monte Carlo (MCMC) sampling. When sampling from multimodal or highly correlated distribution is concerned, in fact, a possible strategy suggests to run several chains in parallel, in order to improve their mixing. If these chains are allowed to interact with each other then many analogies with EC techniques can be observed, and this has led to research in many fields. The chapter aims at reviewing various methods found in literature which conjugates EC techniques and MCMC sampling, in order to identify specific and common procedures, and unifying them in a framework of EC. In the last proposal we present a complex time series model and an identification procedure based on Genetic Algorithms (GAs). The model is capable of dealing with seasonality, by Periodic AutoRegressive (PAR) modelling, and structural changes in time, leading to a nonstationary structure. As far as a very large number of parameters and possibilites of change points are concerned, GAs are appropriate for identifying such model. Effectiveness of procedure is shown on both simulated data and real examples, these latter referred to river flow data in hydrology. The thesis concludes with some final remarks, concerning also future work

Contributions on evolutionary computation for statistical inference

Evolutionary Computation (EC) techniques have been introduced in the 1960s for dealing with complex situations. One possible example is an optimization problems not having an analytical solution or being computationally intractable; in many cases such methods, named Evolutionary Algorithms (EAs), have been successfully implemented. In statistics there are many situations where complex problems arise, in particular concerning optimization. A general example is when the statistician needs to select, inside a prohibitively large discrete set, just one element, which could be a model, a partition, an experiment, or such: this would be the case of model selection, cluster analysis or design of experiment. In other situations there could be an intractable function of data, such as a likelihood, which needs to be maximized, as it happens in model parameter estimation. These kind of problems are naturally well suited for EAs, and in the last 20 years a large number of papers has been concerned with applications of EAs in tackling statistical issues. The present dissertation is set in this part of literature, as it reports several implementations of EAs in statistics, although being mainly focused on statistical inference problems. Original results are proposed, as well as overviews and surveys on several topics. EAs are employed and analyzed considering various statistical points of view, showing and confirming their efficiency and flexibility. The first proposal is devoted to parametric estimation problems. When EAs are employed in such analysis a novel form of variability related to their stochastic elements is introduced. We shall analyze both variability due to sampling, associated with selected estimator, and variability due to the EA. This analysis is set in a framework of statistical and computational tradeoff question, crucial in nowadays problems, by introducing cost functions related to both data acquisition and EA iterations. The proposed method will be illustrated by means of model building problem examples. Subsequent chapter is concerned with EAs employed in Markov Chain Monte Carlo (MCMC) sampling. When sampling from multimodal or highly correlated distribution is concerned, in fact, a possible strategy suggests to run several chains in parallel, in order to improve their mixing. If these chains are allowed to interact with each other then many analogies with EC techniques can be observed, and this has led to research in many fields. The chapter aims at reviewing various methods found in literature which conjugates EC techniques and MCMC sampling, in order to identify specific and common procedures, and unifying them in a framework of EC. In the last proposal we present a complex time series model and an identification procedure based on Genetic Algorithms (GAs). The model is capable of dealing with seasonality, by Periodic AutoRegressive (PAR) modelling, and structural changes in time, leading to a nonstationary structure. As far as a very large number of parameters and possibilites of change points are concerned, GAs are appropriate for identifying such model. Effectiveness of procedure is shown on both simulated data and real examples, these latter referred to river flow data in hydrology. The thesis concludes with some final remarks, concerning also future work

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017