Optimization of water distribution networks and assessment of pipe deterioration by applying the harmony search algortihm

La rellevància del disseny òptim de les xarxes de distribució d’aigua rau en la seva capacitat d’aplicar-les de manera sostenible. Per tant, el seu disseny ha de ser el més eficient i econòmic possible, és a dir, proveint un nivell mínim de servei utilitzant una quantitat apropiada de recursos. En aquest context, una xarxa ideal requereix de l’ús d’elements de la menor mida possible que sigui capaç de proveir aigua amb la pressió mínima requerida a cada node de la xarxa amb el menor consum energètic. Amb aquesta finalitat, en aquest estudi es va fer ús d’un algorisme meta heurístic relativament nou, anomenat Harmony Search, per a optimitzar la xarxa de distribució d’aigua de la ciutat internacional de Cheongna (Corea del Sud), en termes de diàmetre de canonada. A més, utilitzant el mateix algorisme amb un enfocament diferent, es va realitzar una avaluació del deteriorament de les canonades al llarg del temps mitjançant l’anàlisi del coeficient de rugositat òptim per al material original de la canonada, i que proporciona una eina útil per a la presa de decisions. A aquest tipus de problemes d’optimització, en altres estudis, s’han aplicat altres algorismes heurístics i meta heurístics. Tanmateix, l’algorisme Harmony Search proporciona una implementació senzilla amb un cost computacional raonable. En aquest estudi es demostra que el Harmony Search és una eina potent per a l’optimització de xarxes de distribució d’aigua, així com per a l’avaluació del deteriorament de les canonades pel pas del temps.La relevancia del diseño óptimo de las redes de distribución de agua recae en su capacidad de aplicarlas de un modo sostenible. Por lo tanto, su diseño debe ser lo más eficiente y económico posible, es decir, proveyendo un nivel mínimo de servicio utilizando una cantidad apropiada de recursos. En este contexto, una red ideal requiere del uso de elementos del menor tamaño posible que sea capaz de abastecer agua con la presión mínima requerida en cada nodo de la red con el menor consumo energético. Con esta finalidad, en este estudio se hizo uso de un algoritmo meta heurístico relativamente nuevo, llamado Harmony Search, para optimizar la red de distribución de agua de la ciudad internacional de Cheongna (Corea del Sur), en términos de diámetro de tubería. Además, utilizando el mismo algoritmo con un enfoque diferente, se realizó una evaluación del deterioro de las tuberías a lo largo del tiempo mediante el análisis del coeficiente de rugosidad óptimo para el material original de tuberías, y que proporciona una herramienta útil para la toma de decisiones. Para este tipo de problemas de optimización, en otros estudios se han aplicado otros algoritmos heurísticos y meta heurísticos. Sin embargo, el algoritmo Harmony Search proporciona una implementación sencilla con un coste computacional razonable. En este estudio se demuestra que el Harmony Search es una potente herramienta para la optimización de redes de distribución de agua, así como para la evaluación del deterioro de las tuberías por el paso del tiempo.The relevance of the optimal design of water distribution networks lies in its sustainable applicability. Thus, the design must be as efficient and affordable as possible, meaning that it achieves a minimum level of serviceability using an appropriate amount of resources. In this context, an ideal water network requires the use of minimum-size elements to reach the minimum head pressure required for each node of the system with the lowest energy consumption. For this purpose, a relatively new meta-heuristic algorithm, called Harmony Search, was used in this study to optimize the water distribution network of Cheongna International City (S. Korea), in terms of the pipe diameter. Furthermore, using the same algorithm under a different approach, an assessment of the deterioration of conduits over time was conducted by analyzing the optimal roughness coefficient for the original pipe material, which provides a useful tool for decision making. Other heuristic and meta-heuristic algorithms have been applied to this type of problem; however, Harmony Search provides a convenient implementation at a reasonable computational cost. In this study, Harmony Search is demonstrated to be a valuable tool for water distribution networks optimization as well as for pipe aging assessment

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

Hybrid optimizer for expeditious modeling of virtual urban environments

Tese de mestrado. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 200

A Review of Particle Swarm Optimization: Feature Selection, Classification and Hybridizations

Particle swarm optimization (PSO) is a recently grown, popular, evolutionary and conceptually simple but efficient algorithm which belongs to swarm intelligence category. This paper outlines basic concepts and reviews PSO based techniques with their applications to classification and feature selection along with some of the hybridized applications of PSO with similar other techniques. DOI: 10.17762/ijritcc2321-8169.16041

A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem

[EN] The counterfort retaining wall is one of the most frequent structures used in civil engineering. In this structure, optimization of cost and CO2 emissions are important. The first is relevant in the competitiveness and efficiency of the company, the second in environmental impact. From the point of view of computational complexity, the problem is challenging due to the large number of possible combinations in the solution space. In this article, a k-means cuckoo search hybrid algorithm is proposed where the cuckoo search metaheuristic is used as an optimization mechanism in continuous spaces and the unsupervised k-means learning technique to discretize the solutions. A random operator is designed to determine the contribution of the k-means operator in the optimization process. The best values, the averages, and the interquartile ranges of the obtained distributions are compared. The hybrid algorithm was later compared to a version of harmony search that also solved the problem. The results show that the k-mean operator contributes significantly to the quality of the solutions and that our algorithm is highly competitive, surpassing the results obtained by harmony search.The first author was supported by the Grant CONICYT/FONDECYT/INICIACION/11180056, the other two authors were supported by the Spanish Ministry of Economy and Competitiveness, along with FEDER funding (Project: BIA2017-85098-R).García, J.; Yepes, V.; Martí Albiñana, JV. (2020). A Hybrid k-Means Cuckoo Search Algorithm Applied to the Counterfort Retaining Walls Problem. Mathematics. 8(4):1-22. https://doi.org/10.3390/math8040555S12284García, J., Altimiras, F., Peña, A., Astorga, G., & Peredo, O. (2018). A Binary Cuckoo Search Big Data Algorithm Applied to Large-Scale Crew Scheduling Problems. Complexity, 2018, 1-15. doi:10.1155/2018/8395193García, J., Moraga, P., Valenzuela, M., Crawford, B., Soto, R., Pinto, H., … Astorga, G. (2019). 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분산 기계 학습의 자원 효율적인 수행을 위한 동적 최적화 기술

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 전병곤.Machine Learning(ML) systems are widely used to extract insights from data. Ever increasing dataset sizes and model complexity gave rise to many efforts towards efficient distributed machine learning systems. One of the popular approaches to support large scale data and complicated models is the parameter server (PS) approach. In this approach, a training job runs with distributed worker and server tasks, where workers iteratively compute gradients to update the global model parameters that are kept in servers. To improve the PS system performance, this dissertation proposes two solutions that automatically optimize resource efficiency and system performance. First, we propose a solution that optimizes the resource configuration and workload partitioning of distributed ML training on PS system. To find the best configuration, we build an Optimizer based on a cost model that works with online metrics. To efficiently apply decisions by Optimizer, we design our runtime elastic to perform reconfiguration in the background with minimal overhead. The second solution optimizes the scheduling of resources and tasks of multiple ML training jobs in a shared cluster. Specifically, we co-locate jobs with complementary resource use to increase resource utilization, while executing their tasks with fine-grained unit to avoid resource contention. To alleviate memory pressure by co-located jobs, we enable dynamic spill/reload of data, which adaptively changes the ratio of data between disk and memory. We build a working system that implements our approaches. The above two solutions are implemented in the same system and share the runtime part that can dynamically migrate jobs between machines and reallocate machine resources. We evaluate our system with popular ML applications to verify the effectiveness of our solutions.기계 학습 시스템은 데이터에 숨겨진 의미를 뽑아내기 위해 널리 사용되고 있다. 데이터셋의 크기와 모델의 복잡도가 어느때보다 커짐에 따라 효율적인 분산 기계 학습 시스템을위한 많은 노력들이 이루어지고 있다. 파라미터 서버 방식은 거대한 스케일의 데이터와 복잡한 모델을 지원하기 위한 유명한 방법들 중 하나이다. 이 방식에서, 학습 작업은 분산 워커와 서버들로 구성되고, 워커들은 할당된 입력 데이터로부터 반복적으로 그레디언트를 계산하여 서버들에 보관된 글로벌 모델 파 라미터들을 업데이트한다. 파라미터 서버 시스템의 성능을 향상시키기 위해, 이 논문에서는 자동적으로 자원 효율성과 시스템 성능을 최적화하는 두가지의 해법을 제안한다. 첫번째 해법은, 파라미터 시스템에서 분산 기계 학습을 수행시에 자원 설정 및 워크로드 분배를 자동화하는 것이다. 최고의 설정을 찾기 위해 우리는 온라인 메트릭을 사용하는 비용 모델을 기반으로 하는 Optimizer를 만들었다. Optimizer의 결정을 효율적으로 적용하기 위해, 우리는 런타임을 동적 재설정을 최소의 오버헤드로 백그라운드에서 수행하도록 디자인했다. 두번째 해법은 공유 클러스터 상황에서 여러 개의 기계 학습 작업의 세부 작업 과 자원의 스케쥴링을 최적화한 것이다. 구체적으로, 우리는 세부 작업들을 세밀한 단위로 수행함으로써 자원 경쟁을 억제하고, 서로를 보완하는 자원 사용 패턴을 보이는 작업들을 같은 자원에 함께 위치시켜 자원 활용율을 끌어올렸다. 함께 위치한 작업들의 메모리 압력을 경감시키기 위해 우리는 동적으로 데이터를 디스크로 내렸다가 다시 메모리로 읽어오는 기능을 지원함과 동시에, 디스크와 메모리간의 데이터 비율을 상황에 맞게 시스템이 자동으로 맞추도록 하였다. 위의 해법들을 실체화하기 위해, 실제 동작하는 시스템을 만들었다. 두가지의 해법을 하나의 시스템에 구현함으로써, 동적으로 작업을 머신 간에 옮기고 자원을 재할당할 수 있는 런타임을 공유한다. 해당 솔루션들의 효과를 보여주기 위해, 이 시스템을 많이 사용되는 기계 학습 어플리케이션으로 실험하였고 기존 시스템들 대비 뛰어난 성능 향상을 보여주었다.Chapter1. Introduction 1 1.1 Distributed Machine Learning on Parameter Servers 1 1.2 Automating System Conguration of Distributed Machine Learning 2 1.3 Scheduling of Multiple Distributed Machine Learning Jobs 3 1.4 Contributions 5 1.5 Dissertation Structure 6 Chapter2. Background 7 Chapter3. Automating System Conguration of Distributed Machine Learning 10 3.1 System Conguration Challenges 11 3.2 Finding Good System Conguration 13 3.2.1 Cost Model 13 3.2.2 Cost Formulation 15 3.2.3 Optimization 16 3.3 Cruise 18 3.3.1 Optimizer 19 3.3.2 Elastic Runtime 21 3.4 Evaluation 26 3.4.1 Experimental Setup 26 3.4.2 Finding Baselines with Grid Search 28 3.4.3 Optimization in the Homogeneous Environment 28 3.4.4 Utilizing Opportunistic Resources 30 3.4.5 Optimization in the Heterogeneous Environment 31 3.4.6 Reconguration Speed 32 3.5 Related Work 33 3.6 Summary 34 Chapter4 A Scheduling Framework Optimized for Multiple Distributed Machine Learning Jobs 36 4.1 Resource Under-utilization Problems in PS ML Training 37 4.2 Harmony Overview 42 4.3 Multiplexing ML Jobs 43 4.3.1 Fine-grained Execution with Subtasks 44 4.3.2 Dynamic Grouping of Jobs 45 4.3.3 Dynamic Data Reloading 54 4.4 Evaluation 56 4.4.1 Baselines 56 4.4.2 Experimental Setup 57 4.4.3 Performance Comparison 59 4.4.4 Performance Breakdown 59 4.4.5 Workload Sensitivity Analysis 61 4.4.6 Accuracy of the Performance Model 63 4.4.7 Performance and Scalability of the Scheduling Algorithm 64 4.4.8 Dynamic Data Reloading 66 4.5 Discussion 67 4.6 Related Work 67 4.7 Summary 70 Chapter5 Conclusion 71 5.1 Summary 71 5.2 Future Work 71 5.2.1 Other Communication Architecture Support 71 5.2.2 Deep Learning & GPU Resource Support 72 요약 81Docto

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Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/1/etep12366.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155928/2/etep12366_am.pd

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems