987 research outputs found

    Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

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    In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

    Metaheuristic optimization of power and energy systems: underlying principles and main issues of the 'rush to heuristics'

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    In the power and energy systems area, a progressive increase of literature contributions containing applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods based on weak comparisons. This 'rush to heuristics' does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter, but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems, and aims at providing a comprehensive view of the main issues concerning the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls found in literature contributions are identified, and specific guidelines are provided on how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

    Automated Design of Metaheuristic Algorithms: A Survey

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    Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality. Manually designing metaheuristic algorithms for solving a target problem is criticized for being laborious, error-prone, and requiring intensive specialized knowledge. This gives rise to increasing interest in automated design of metaheuristic algorithms. With computing power to fully explore potential design choices, the automated design could reach and even surpass human-level design and could make high-performance algorithms accessible to a much wider range of researchers and practitioners. This paper presents a broad picture of automated design of metaheuristic algorithms, by conducting a survey on the common grounds and representative techniques in terms of design space, design strategies, performance evaluation strategies, and target problems in this field

    Treasure hunt : a framework for cooperative, distributed parallel optimization

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    Orientador: Prof. Dr. Daniel WeingaertnerCoorientadora: Profa. Dra. Myriam Regattieri DelgadoTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 27/05/2019Inclui referências: p. 18-20Área de concentração: Ciência da ComputaçãoResumo: Este trabalho propõe um framework multinível chamado Treasure Hunt, que é capaz de distribuir algoritmos de busca independentes para um grande número de nós de processamento. Com o objetivo de obter uma convergência conjunta entre os nós, este framework propõe um mecanismo de direcionamento que controla suavemente a cooperação entre múltiplas instâncias independentes do Treasure Hunt. A topologia em árvore proposta pelo Treasure Hunt garante a rápida propagação da informação pelos nós, ao mesmo tempo em que provê simutaneamente explorações (pelos nós-pai) e intensificações (pelos nós-filho), em vários níveis de granularidade, independentemente do número de nós na árvore. O Treasure Hunt tem boa tolerância à falhas e está parcialmente preparado para uma total tolerância à falhas. Como parte dos métodos desenvolvidos durante este trabalho, um método automatizado de Particionamento Iterativo foi proposto para controlar o balanceamento entre explorações e intensificações ao longo da busca. Uma Modelagem de Estabilização de Convergência para operar em modo Online também foi proposto, com o objetivo de encontrar pontos de parada com bom custo/benefício para os algoritmos de otimização que executam dentro das instâncias do Treasure Hunt. Experimentos em benchmarks clássicos, aleatórios e de competição, de vários tamanhos e complexidades, usando os algoritmos de busca PSO, DE e CCPSO2, mostram que o Treasure Hunt melhora as características inerentes destes algoritmos de busca. O Treasure Hunt faz com que os algoritmos de baixa performance se tornem comparáveis aos de boa performance, e os algoritmos de boa performance possam estender seus limites até problemas maiores. Experimentos distribuindo instâncias do Treasure Hunt, em uma rede cooperativa de até 160 processos, demonstram a escalabilidade robusta do framework, apresentando melhoras nos resultados mesmo quando o tempo de processamento é fixado (wall-clock) para todas as instâncias distribuídas do Treasure Hunt. Resultados demonstram que o mecanismo de amostragem fornecido pelo Treasure Hunt, aliado à maior cooperação entre as múltiplas populações em evolução, reduzem a necessidade de grandes populações e de algoritmos de busca complexos. Isto é especialmente importante em problemas de mundo real que possuem funções de fitness muito custosas. Palavras-chave: Inteligência artificial. Métodos de otimização. Algoritmos distribuídos. Modelagem de convergência. Alta dimensionalidade.Abstract: This work proposes a multilevel framework called Treasure Hunt, which is capable of distributing independent search algorithms to a large number of processing nodes. Aiming to obtain joint convergences between working nodes, Treasure Hunt proposes a driving mechanism that smoothly controls the cooperation between the multiple independent Treasure Hunt instances. The tree topology proposed by Treasure Hunt ensures quick propagation of information, while providing simultaneous explorations (by parents) and exploitations (by children), on several levels of granularity, regardless the number of nodes in the tree. Treasure Hunt has good fault tolerance and is partially prepared to full fault tolerance. As part of the methods developed during this work, an automated Iterative Partitioning method is proposed to control the balance between exploration and exploitation as the search progress. A Convergence Stabilization Modeling to operate in Online mode is also proposed, aiming to find good cost/benefit stopping points for the optimization algorithms running within the Treasure Hunt instances. Experiments on classic, random and competition benchmarks of various sizes and complexities, using the search algorithms PSO, DE and CCPSO2, show that Treasure Hunt boosts the inherent characteristics of these search algorithms. Treasure Hunt makes algorithms with poor performances to become comparable to good ones, and algorithms with good performances to be capable of extending their limits to larger problems. Experiments distributing Treasure Hunt instances in a cooperative network up to 160 processes show the robust scaling of the framework, presenting improved results even when fixing a wall-clock time for the instances. Results show that the sampling mechanism provided by Treasure Hunt, allied to the increased cooperation between multiple evolving populations, reduce the need for large population sizes and complex search algorithms. This is specially important on real-world problems with time-consuming fitness functions. Keywords: Artificial intelligence. Optimization methods. Distributed algorithms. Convergence modeling. High dimensionality

    Benchmark for Tuning Metaheuristic Optimization Technique to Optimize Traffic Light Signals Timing

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    Traffic congestion at intersections is an international problem in the cities. This problem causes more waiting time, air pollution, petrol consumption, stress of people and healthy problems. Against this background, this research presents a benchmark iterative approach for optimal use of the metaheuristic optimization techniques to optimize the traffic light signals timing problem. A good control of the traffic light signals timing on road networks may help in solving the traffic congestion problems. The aim of this research is to identify the most suitable metaheuristic optimization technique to optimize the traffic light signals timing problem, thus reducing average travel time (ATT) for each vehicle, waiting time, petrol consumption by vehicles and air pollution to the lowest possible level/degree. The central part of Nablus road network has a huge traffic congestion at the traffic light signals. It was selected as a research case study and was represented by the SUMO simulator. The researcher used a random algorithm and three different metaheuristic optimization techniques: three types of Genetic Algorithm (GA), Particle Swarm Algorithm (PS) and five types of Tabu Search Algorithm (TS). Parameters in each metaheuristic algorithm affect the efficiency of the algorithm in finding the optimal solutions. The best values of these parameters are difficult to be determined; their values were assumed in the previous traffic light signals timing optimization research. The efficiency of the metaheuristic algorithm cannot be ascertained of being good or bad. Therefore, the values of these parameters need a tuning process but this cannot be done by using SUMO simulator because of its heavy computation. The researcher used a benchmark iterative approach to tune the values of them etaheuristic algorithm parameters by using a benchmark function. The chosen function has similar characteristics to the traffic light signals timing problem. Then, through the use of this approach, the researcher arrived at the optimal use of the metaheuristic optimization algorithms to optimize traffic light signals timing problem. The efficiency of each metaheuristic optimization algorithm, tested in this research, is in finding the optimal or near optimal solution after using the benchmark iterative approach. The results of metaheuristic optimization algorithm improved at some values of the tuned parameters. The researcher validated the research results by comparing average results of the metaheuristic algorithms, used in solving the traffic light signals optimization problem after using benchmark iterative approach, with the average results of the same metaheuristic algorithms used before using the benchmark iterative approach; they were also compared with the results of Webster, HCM methods and SYNCHRO simulator. In the light of these study findings, the researcher recommends trying the benchmark iterative approach to get ore efficient solutions which are very close to the optimal solution for the traffic light signals timing optimization problem and many complex practical optimization problems that we face in real life.الازدحامات المرورية عند التقاطعات هي مشكله عالمية في المدن. هذه المشكلة تسبب المزيد من وقت االنتظار وتلوث الهواء و استهالك الوقود، و توتر الناس و مشاكل صحية. على هذه الخلفية، يقدم هذا البحث نهج المعيار المكرر لالستخدام تقنيات التحسين التخمينية في تحسين مشكلة توقيت اإلشارات الضوئية. التحكم الجيد في توقيت االشارات الضوئية على شبكات الطرق قد يساعد في حل مشاكل االزدحام المروري. يهدف هذا البحث الى تحديد أفضل و أنسب تقنية تحسين تخمينية لتحسين مشكلة توقيت االشارات الضوئية، وبالتالي تقليل متوسط الوقت الذي يستغرقه السفر (ATT(لكل مركبة، و وقت االنتظار، و استهالك الوقود المستخدم في المركبات و تلوث الهواء إلى أدنى مستوى ممكن. يعاني الجزء المركزي من شبكة طرق مدينة نابلس من ازدحام مروري كبير على االشارات الضوئية. و تم اختيار هذا الجزء كحالة البحث الدراسية و التي تم تمثيلها باستخدام برنامج المحاكاة سومو. و استخدم الباحث خوارزمية عشوائية و ثالث تقنيات تحسين تخمينية و هي: ثالث انواع من الخوارزمية الجينية، و خورزمية سرب الجسيمات، و خمسة انواع من خوارزمية التابو. و هناك متغيرات في كل خوارزمية تخمينية تؤثر على فعالية الخوارمية في ايجاد الحلول المثلى. و من الصعب تحديد افضل القيم لهذه المتغيرات؛ و قيم هذه المتغيرات كانت تفترض في ابحات تحسين توقيت االشارات الضوئية السابقة. وفي هذه الحاله فعالية اقتران التحسين التخميني ال يمكن التحقق منها اذا ما كانت جيده او سيئة. ولذلك فان قيم هذه المتغيرات بحاجه لعملية ضبط ، ولكن ال يمكننا ذلك باستخدام برنامج المحاكاه سومو النه حساباته ثقيله و طويله. استخدم الباحث طريقة مقارنة الدوال لضبط قيم متغيرات خوارزمية التحسين التخمينية باستخدام خوارزمية معيار. خوارمية المعيار المختاره لها خصائص شبيهه بمشكلة توقيت االشارات الضوئية. ثم من خالل استخدام هذه الطريقة، وصل الباحث الى افضل استخدام لخوارزميات التحسين التخمينية لتحسين مشكلة توقيت االشارات االضوئية. وفي هذا البحث تم اختبار فعالية كل خوارمية تحسين تخمينية في ايجاد الحل االمثل او حل قريب من الحل االمثل بعد ضبط خوارزمية التحسين التخمينية. لقد تحسنت نتائج خوارزمية التحسين التخمينية عند بعض قيم المتغيرات التي تم ضبطها. قام الباحث بالتحقق من نتائج البحث بمقارنة معدل نتائج خوارزميات التحسين التخمينية التي امستخدمها في تحسين مشكلة توقيت االشارات الضوئية قبل ضبط خوارزمية التحسين التخمينية، مع معدل نتائج نفس الخوارزميات التخمينية التي امستخدمها بعد ضبط خوارزمية التحسين التخمينية؛ وهذه النتائج تمت مقارنتها مع نتائج طريقتي ويبستر و HCM و برنامج السنكرو. في ضوء نتائج هذه الدراسة، يوصي الباحث بتجريب طريقة مقارنة الدوال لضبط خوارزميات التحسين التخمينية للحصول على حلول فعالة اكثر و التي تكون قريبة جدا من الحل االمثل لتحسين مشكلة توقيت االشارات الضوئية و لتحسين المشاكل العملية المعقدة التي تواجهنا في الحياة العملية

    Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method

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    Pillar stability is an important condition for safe work in room-and-pillar mines. The instability of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced stresses at different positions in the pillar is helpful for pillar design and guaranteeing pillar stability. There are many modeling methods to design pillars and evaluate their stability, including empirical and numerical method. However, empirical methods are difficult to be applied to places other than the original environmental characteristics, and numerical methods often simplify the boundary conditions and material properties, which cannot guarantee the stability of the design. Currently, machine learning (ML) algorithms have been successfully applied to pillar stability assessment with higher accuracy. Thus, the study adopted a back-propagation neural network (BPNN) and five elements including the sparrow search algorithm (SSA), gray wolf optimizer (GWO), butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), and multi-verse optimizer (MVO). Combining metaheuristic algorithms, five hybrid models were developed to predict the induced stress within the pillar. The weight and threshold of the BPNN model are optimized by metaheuristic algorithms, in which the mean absolute error (MAE) is utilized as the fitness function. A database containing 149 data samples was established, where the input variables were the angle of goafline (A), depth of the working coal seam (H), specific gravity (G), distance of the point from the center of the pillar (C), and distance of the point from goafline (D), and the output variable was the induced stress. Furthermore, the predictive performance of the proposed model is evaluated by five metrics, namely coefficient of determination (R2), root mean squared error (RMSE), variance accounted for (VAF), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results showed that the five hybrid models developed have good prediction performance, especially the GWO-BPNN model performed the best (Training set: R2 = 0.9991, RMSE = 0.1535, VAF = 99.91, MAE = 0.0884, MAPE = 0.6107; Test set: R2 = 0.9983, RMSE = 0.1783, VAF = 99.83, MAE = 0.1230, MAPE = 0.9253). Copyright © 2023 Zhou, Chen, Chen, Khandelwal, Monjezi and Peng

    Hybrid microgrid energy management and control based on metaheuristic-driven vector-decoupled algorithm considering intermittent renewable sources and electric vehicles charging lot

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    Energy management and control of hybrid microgrids is a challenging task due to the varying nature of operation between AC and DC components which leads to voltage and frequency issues. This work utilizes a metaheuristic-based vector-decoupled algorithm to balance the control and operation of hybrid microgrids in the presence of stochastic renewable energy sources and electric vehicles charging structure. The AC and DC parts of the microgrid are coupled via a bidirectional interlinking converter, with the AC side connected to a synchronous generator and portable AC loads, while the DC side is connected to a photovoltaic system and an electric vehicle charging system. To properly ensure safe and efficient exchange of power within allowable voltage and frequency levels, the vector-decoupled control parameters of the bidirectional converter are tuned via hybridization of particle swarm optimization and artificial physics optimization. The proposed control algorithm ensures the stability of both voltage and frequency levels during the severe condition of islanding operation and high pulsed demands conditions as well as the variability of renewable source production. The proposed methodology is verified in a state-of-the-art hardware-in-the-loop testbed. The results show robustness and effectiveness of the proposed algorithm in managing the real and reactive power exchange between the AC and DC parts of the microgrid within safe and acceptable voltage and frequency levels

    An Evolutionary Computational Approach for the Problem of Unit Commitment and Economic Dispatch in Microgrids under Several Operation Modes

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    In the last decades, new types of generation technologies have emerged and have been gradually integrated into the existing power systems, moving their classical architectures to distributed systems. Despite the positive features associated to this paradigm, new problems arise such as coordination and uncertainty. In this framework, microgrids constitute an effective solution to deal with the coordination and operation of these distributed energy resources. This paper proposes a Genetic Algorithm (GA) to address the combined problem of Unit Commitment (UC) and Economic Dispatch (ED). With this end, a model of a microgrid is introduced together with all the control variables and physical constraints. To optimally operate the microgrid, three operation modes are introduced. The first two attend to optimize economical and environmental factors, while the last operation mode considers the errors induced by the uncertainties in the demand forecasting. Therefore, it achieves a robust design that guarantees the power supply for different confidence levels. Finally, the algorithm was applied to an example scenario to illustrate its performance. The achieved simulation results demonstrate the validity of the proposed approach.Ministerio de Ciencia, Innovación y Universidades TEC2016-80242-PMinisterio de Economía y Competitividad PCIN-2015-043Universidad de Sevilla Programa propio de I+D+
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