This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to ...
详细信息
This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additionally, weather disturbances such as solar heat gain can be anticipated and compensated for, while taking into account the slow dynamics of the floor. Together with a genetic algorithm, they provide a way to search for optimal future set-point sequences, when convexity and continuity in the solution space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
New crystal phases of osmium carbide are presented in this work. These results were found with the CA code, an evolutionary algorithm (EA) presented in a previous paper which takes full advantage of crystal symmetry b...
详细信息
New crystal phases of osmium carbide are presented in this work. These results were found with the CA code, an evolutionary algorithm (EA) presented in a previous paper which takes full advantage of crystal symmetry by using an ad hoc search space and genetic operators. The new OsC2 and Os2C structures have a lower enthalpy than any known so far. Moreover, the layered pattern of OsC2 serves as a blueprint for building new crystals by adding or removing layers of carbon and/or osmium and generating many other Os + C structures like Os2C, OsC, OsC2 and OsC4. These again have a lower enthalpy than all the investigated structures, including those of the present work. The mechanical, vibrational and electronic properties are discussed as well.
Smart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes w...
详细信息
ISBN:
(纸本)9781450349390
Smart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes while minimizing operational costs. Due to the nature of the problem, which includes mixed-integer variables and non-linear constraints, evolutionary algorithms (EA) are considered a good tool to find optimal and near-optimal solutions to large-scale problems. In this paper, we analyze the application of Differential Evolution (DE) to solve the large-scale ERM problem in SGs through extensive experimentation on a case study using a 33-Bus power network with high penetration of Distributed Energy Resources (DER) and Electric Vehicles (EVs), as well as advanced features such as energy stock exchanges and Demand Response (DR) programs. We analyze the impact of DE parameter setting on four state-of-the-art DE strategies. Moreover, DE strategies are compared with other well-known EAs and a deterministic approach based on MINLP. Results suggest that, even when DE strategies are very sensitive to the setting of their parameters, they can find better solutions than other EAs, and near-optimal solutions in acceptable times compared with an MINLP approach.
Over the past two decades, various research works have been going on Multiple Sequence Alignment (MSA) and it becomes an important domain in bioinformatics. This is an NPhard problem. For this purpose, various traditi...
详细信息
ISBN:
(纸本)9781538618608
Over the past two decades, various research works have been going on Multiple Sequence Alignment (MSA) and it becomes an important domain in bioinformatics. This is an NPhard problem. For this purpose, various traditional, heuristics and metaheuristic methods have been applied. Among these methods, metaheuristics show an effective output to overcome the bottleneck of MSA problem. Different metaheuristic methods and software have been developed to overcome the speed and accuracy problem of MSA, while the number of sequences increases. In this article, we have surveyed widely used metaheuristic methods and alignment tools applied for solving MSA problem. However, after reviewing we can conclude that the time complexity is still a big challenge for MSA problem.
This paper concerns the problem of portfolio optimization in the context of ultra-high frequency environment with dynamic and frequent changes in statistics of financial assets. It aims at providing Pareto fronts of o...
详细信息
ISBN:
(纸本)9783319558493;9783319558486
This paper concerns the problem of portfolio optimization in the context of ultra-high frequency environment with dynamic and frequent changes in statistics of financial assets. It aims at providing Pareto fronts of optimal portfolios and updating them when estimated return rates or risks of financial assets change. The problem is defined in terms of dynamic optimization and solved online with a proposed evolutionary algorithm. Experiments concern ultra-high frequency time series coming from the London Stock Exchange Rebuilt Order Book database and the FTSE100 index.
Elitism is a common feature of many-objective optimizers and has a strong impact on the performance of the algorithms. The way elitism is implemented vary among the various approaches to many-objective optimization an...
详细信息
ISBN:
(纸本)9781509046010
Elitism is a common feature of many-objective optimizers and has a strong impact on the performance of the algorithms. The way elitism is implemented vary among the various approaches to many-objective optimization and there are no detailed studies about their effects. In this work we focus on a multi-and many-objective optimization approach based on epsilon-dominance. We track the number of generations a solution remains in the population to bias survival selection or the creation of neighborhoods for parent selection. We investigate how elitist strategies affect performance of the algorithm and show that convergence and diversity can be enhanced by using different strategies for elitism on many-objective uni-modal and multi-modal problems with 4, 5, and 6 objectives.
In this paper, we study the problem of vehicle scheduling in urban public transport systems taking into account the vehicle-type (different capacity and operating cost) known as VTSP. It is modeled as a multiobjective...
详细信息
ISBN:
(纸本)9780769561493
In this paper, we study the problem of vehicle scheduling in urban public transport systems taking into account the vehicle-type (different capacity and operating cost) known as VTSP. It is modeled as a multiobjective optimization problem (MOP). We propose a heuristic based on MOCell (Multi-Objective Cellular evolutionary algorithm) to solve the problem considering restrictions of government agencies in context of smart cities to improve the Intelligent Transportation Systems (ITS). A set of non-dominated solutions represents different assignments of vehicles to cover trips of a specific route. The conflicting objectives of provider and users (passenger) are to minimize the total operating cost, and maximize the quality of service, reducing the waiting time and congestion in buses. We present experimental analysis and conclude that the proposed heuristic provides a good performance and competitive results in terms of convergence and diversity of the solutions along the Pareto front.
Deep learning has continued to gain momentum in applications across many critical areas of research in computer vision and machine learning. In particular, deep learning networks have had much success in image classif...
详细信息
ISBN:
(数字)9781510609006
ISBN:
(纸本)9781510608993;9781510609006
Deep learning has continued to gain momentum in applications across many critical areas of research in computer vision and machine learning. In particular, deep learning networks have had much success in image classification, especially when training data are abundantly available, as is the case with the ImageNet project. However, several researchers have exposed potential vulnerabilities of these networks to carefully crafted adversarial imagery. Additionally, researchers have shown the sensitivity of these networks to some types of noise and distortion. In this paper, we investigate the use of no-reference image quality metrics to identify adversarial imagery and images of poor quality that could potentially Fool a deep learning network Or dramatically reduce its accuracy. Results are shown on several adversarial image databases with comparisons to popular image classification databases.
This paper focuses on an operation optimisation problem for a class of multi-head surface mounting machines in printed circuit board assembly lines. The problem involves five interrelated sub-problems: assigning nozzl...
详细信息
This paper focuses on an operation optimisation problem for a class of multi-head surface mounting machines in printed circuit board assembly lines. The problem involves five interrelated sub-problems: assigning nozzle types as well as components to heads, assigning feeders to slots and determining component pickup and placement sequences. According to the depth of making decisions, the sub-problems are first classified into two layers. Based on the classification, a two-stage mixed-integer linear programming (MILP) is developed to describe it and a two-stage problem-solving frame with a hybrid evolutionary algorithm (HEA) is proposed. In the first stage, a constructive heuristic is developed to determine the set of nozzle types assigned to each head and the total number of assembly cycles;in the second stage, constructive heuristics, an evolutionary algorithm with two evolutionary operators and a tabu search (TS) with multiple neighbourhoods are combined to solve all the sub-problems simultaneously, where the results obtained in the first stage are taken as constraints. Computational experiments show that the HEA can obtain good near-optimal solutions for small size instances when compared with an optimal solver, Cplex, and can provide better results when compared with a TS and an EA for actual instances.
The optimization of pump operations has been widely studied, as it can decrease operational and maintenance costs and can reduce greenhouse gas emissions caused by the energy consumption from fossil fueled electricity...
详细信息
The optimization of pump operations has been widely studied, as it can decrease operational and maintenance costs and can reduce greenhouse gas emissions caused by the energy consumption from fossil fueled electricity sources. However, only the optimization of pump scheduling (where pumps are controlled based on times) and the optimization of simple controls (where pumps are controlled based on one condition only, e.g. the level of one tank) were previously able to be used in the EPANET2 toolkit. This paper uses a modified version of the hydraulic solver EPANET2 that enables rule-based controls (i.e. controls based on more than one condition) to be automatically changed by an optimization algorithm. This modification is particularly useful in cases where the pump operations need to be decided based on multiple conditions: typical examples are the cases where the pumps are controlled according to the water levels of multiple tanks or when both tank levels and time of day are taken into account to reduce the pumping in the peak tariff period. The new toolkit, called ETTAR (EPANET2 Toolkit to Alter Rules), is applied to a large case study, where different types of pump operations will be tested. Results show that the optimization of rule-based controls can decrease operational costs while guaranteeing robust pump controls. (C) 2016 The Authors. Published by Elsevier Ltd.
暂无评论