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نتیجه جستجو - Multiobjective

تعداد مقالات یافته شده: 17
ردیف عنوان نوع
1 A smart community energy management scheme considering user dominated demand side response and P2P trading
یک طرح مدیریت انرژی هوشمند جامعه با توجه به تسلط کاربر بر پاسخ طرف تقاضا و تجارت P2P-2020
This paper proposed a Peer-to-Peer (P2P) local community energy pool and a User Dominated Demand Side Response (UDDSR) that can help energy sharing and reduce energy bills of smart community. The proposed UDDSR allows energy users within the community to submit flexible Demand Response (DR) bids to Community Energy Management Scheme (EMS) with flexible start time, stop time and response durations with regard to users’ comfort zones for electric heating systems, electric vehicles and other home appliances, which gives maximum freedom to the DR participants. The scheduling of the DR bids, originally a multiobjective optimization problem (maximize the total flexible demand and the flexible demand in every interval during the whole DR duration), is transferred to a single objective optimization problem (maximize the total demand with penalty for demand imbalance during the whole DR duration) that can significantly decrease the computational complexity. Furthermore, to facilitate efficient energy usage among neighbourhoods, a local energy pool is also proposed to enable the energy trading among users aiming to facilitate the usage of surplus energy within the community. The electricity price of energy pool is determined by the real-time demand/supply ratio, and upper/lower limit for the price is configured to ensure the profitability for all the participants within the pool. To evaluate the performance of proposed UDDSR and local energy pool, comprehensive numerical analysis is conducted. It is found that the energy pool participants without PV can get at least 6.16% savings on electricity bill (when PV penetration level equals to 20%). The energy pool participants with PV can get much better return (at least 13.4% profit increase) on the PV generation compared to the conventional Feed-in-Tariff. If energy users join the UDDSR scheme, the participants can get further return, and the proposed UDDSR can provide a constant load reduction/increase during the every time interval of the whole DR event. If Battery Energy Storage System (BESS) is included in the DR operation, the usage efficiency of customers’ flexible loads can achieve more than 85%.
مقاله انگلیسی
2 A fitness landscape ruggedness multiobjective differential evolution algorithm with a reinforcement learning strategy
یک الگوریتم تکامل افتراقی چند هدفه ناهمواری چشم انداز تناسب اندام با یک استراتژی یادگیری تقویتی-2020
Optimization is the process of finding and comparing feasible solutions and adopting the best one until no better solution can be found. Because solving real-world problems often involves simulations and multiobjective optimization, the results and solutions of these problems are conceptually different from those of single-objective problems. In single-objective optimization problems, the global optimal solution is the solution that yields the optimal value of the objective function. However, for multiobjective optimization problems, the optimal solutions are Pareto-optimal solutions produced by balancing multiple objective functions. The strategic variables calculated in multiobjective problems produce different effects on the mapping imbalance and the search redundancy in the search space. Therefore, this paper proposes a fitness landscape ruggedness multiobjective differential evolution (LRMODE) algorithm with a reinforcement learning strategy. The proposed algorithm analyses the ruggedness of landscapes using information entropy to estimate whether the local landscape has a unimodal or multimodal topology and then combines the outcome with a reinforcement learning strategy to determine the optimal probability distribution of the algorithm’s search strategy set. The experimental results showthat this novel algorithm can ameliorate the problem of search redundancy and search-space mapping imbalances, effectively improving the convergence of the search algorithm during the optimization process.
Keywords: Multiobjective | Differential Evolution | Reinforcement Learning | Fitness Landscape | Search Strategy
مقاله انگلیسی
3 Bi-level optimal sizing and energy management of hybrid electric propulsion systems
اندازه گیری بهینه دو سطح و مدیریت انرژی سیستم های پیشران برقی هیبریدی-2020
Hybrid electric propulsion systems attract considerable research interest because of their potential to reduce fuel consumption, greenhouse gas emission, and net present cost. However, independent optimization for component sizing or energy management may lead to performance degradation. The present study proposes a multiobjective bi-level optimization that performs component sizing at the upper level and energy management at the lower level simultaneously. Multiobjective particle swarm optimization is developed for the upper level because of its merits in computational time and generational distance. An adaptive equivalent consumption minimization strategy, which has a light computational load, has been modified for the lower level by updating the equivalence factor based on the battery stage of charge and engine efficiency. Real-time hardware-in-the-loop experiments are carried out to validate the effectiveness of the optimization. The results of the proposed bi-level optimization are compared with two independent single-level optimizations. The optimal solution of the proposed method is significantly superior to the single-level optimizations. Furthermore, the result of the singlelower- level optimization is closer to that of the bi-level optimization than that of the single-upper-level optimization.
Keywords: Multiobjective bi-level optimization | Hybrid electric propulsion system | Modified adaptive equivalent consumption | minimization strategy | Fuel consumption | Greenhouse gas emission | Net present cost
مقاله انگلیسی
4 A Reinforcement Learning based evolutionary multi-objective optimization algorithm for spectrum allocation in Cognitive Radio networks
یک الگوریتم بهینه سازی چند هدفه تکاملی مبتنی بر یادگیری تقویتی برای تخصیص طیف در شبکه های رادیویی شناختی-2020
To cope up with drastically increasing demand for radio resources lead to raise a challenge to the wireless community. The limited radio spectrum and fixed spectrum allocation strategy have become a bottleneck for various wireless communication. Cognitive Radio (CR) technology along with potential benefits of machine learning has attracted substantial research interest especially in the context of spectrum management. However, a variety of performance attributes as objectives draw attention during the technological preparations for spectrum management such as higher spectral efficiency, lower latency, higher network capacity, and better energy efficiency as these objectives are often conflicting with each other. Hence, this paper addresses the spectrum allocation problem concerning network capacity and spectrum efficiency as conflicting objectives and model the scenario as a multiobjective optimization problem in CR networks. An improved version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) which combines the feature of evolutionary algorithm and machine learning called Non-dominated Sorting Genetic Algorithm based on Reinforcement Learning (NSGARL) is proposed which incorporates a self-tuning parameter approach to handle multiple conflicting objectives. The numerical findings validate the effectiveness of the proposed algorithm through the Pareto optimal set and obtain optimal solution efficiently to satisfy various requirements of spectrum allocation in CR networks.
Keywords: Cognitive Radio (CR) networks | Multi-objective optimization | NSGA-II | NSGA-RL | Reinforcement learning | Spectrum allocation
مقاله انگلیسی
5 On initial population generation in feature subset selection
تولید جمعیت اولیه در انتخاب زیر مجموعه ویژگی-2019
Performance of evolutionary algorithms depends on many factors such as population size, number of generations, crossover or mutation probability, etc. Generating the initial population is one of the impor- tant steps in evolutionary algorithms. A poor initial population may unnecessarily increase the number of searches or it may cause the algorithm to converge at local optima. In this study, we aim to find a promis- ing method for generating the initial population, in the Feature Subset Selection (FSS) domain. FSS is not considered as an expert system by itself, yet it constitutes a significant step in many expert systems. It eliminates redundancy in data, which decreases training time and improves solution quality. To achieve our goal, we compare a total of five different initial population generation methods; Information Gain Ranking (IGR), greedy approach and three types of random approaches. We evaluate these methods using a specialized Teaching Learning Based Optimization searching algorithm (MTLBO-MD), and three super- vised learning classifiers: Logistic Regression, Support Vector Machines, and Extreme Learning Machine. In our experiments, we employ 12 publicly available datasets, mostly obtained from the well-known UCI Machine Learning Repository. According to their feature sizes and instance counts, we manually classify these datasets as small, medium, or large-sized. Experimental results indicate that all tested methods achieve similar solutions on small-sized datasets. For medium-sized and large-sized datasets, however, the IGR method provides a better starting point in terms of execution time and learning performance. Finally, when compared with other studies in literature, the IGR method proves to be a viable option for initial population generation.
Keywords: Feature subset selection | Initial population | Multiobjective optimization
مقاله انگلیسی
6 Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization
داده کاوی مبتنی بر خوشه بندی و تجزیه و تحلیل قاعده انجمن برای کشف دانش در بهینه سازی توپولوژی چند رده-2019
Optimum design problems, including structural optimization problems, often include multiple objec- tives. A multiobjective optimization problem usually provides a number of optimal solutions, called non- dominated solutions or Pareto-optimal solutions. In multiobjective topology optimization scenarios, deci- sion makers face the challenging task of choosing the most effective solution that meets their needs; se- rial comparisons among a set of Pareto-optimal solution are cumbersome, as are trial-and-error attempts to find an appropriate solution among a host of alternatives. On the other hand, the recent integration of data mining techniques in multiobjective optimization methods can provide decision makers with impor- tant, highly pertinent, and useful knowledge. In this paper, we propose a data mining technique for knowledge discovery in multiobjective topol- ogy optimization. The proposed method sequentially applies clustering and association rule analysis to a Pareto-optimal solution set. First, clustering is applied in the design space and the result is then vi- sualized in the objective space. After clustering, detailed features in each cluster are analyzed based on the concept of association rule analysis, so that characteristic substructures can be extracted from each cluster of solutions. In four numerical examples, we demonstrate that the proposed method provides per- tinent knowledge that aids comprehension of the key substructures responsible for one or more desired performances, thereby giving decision makers a useful tool for discovery of particularly effective design solutions.
Keywords: Data mining | Optimum design | Clustering | Association analysis | Topology optimization
مقاله انگلیسی
7 Multiobjective e-commerce recommendations based on hypergraph ranking
توصیه های تجارت الکترونیکی چندوجهی مبتنی بر رتبه بندی هایپرگراف-2019
Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single- objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple ob- jectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different in- formation resources and high-order relationships for the construction of a multipartite hy- pergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical ex- periments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommenda- tions. The experiments also demonstrate that this framework is still compatible for tradi- tional single-objective recommendations and can improve accuracy significantly. In conclu- sion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers.
Keywords: Recommender systems | E-commerce | User personalization | Hypergraph
مقاله انگلیسی
8 Prediction of irrigation event occurrence at farm level using optimal decision trees
پیش بینی وقوع رویداد آبیاری در سطح مزرعه با استفاده از درختان تصمیم بهینه-2019
Irrigation water demand is highly variable and depends on farmers’ decision about when to irrigate. Their decision affects the performance of the irrigation networks. An accurate daily prediction of irrigation events occurrence at farm scale is a key factor to improve the management of the irrigation districts and consequently the sustainability of the irrigated agriculture. In this work, a hybrid heuristic methodology that combines Decision Trees and Genetic Algorithm has been developed to find the optimal decision tree to model farmer’s behaviour, predicting the occurrence of irrigation events. The methodology has been tested in a real irrigation district and results showed that the optimal models developed have been able to predict between 68% and 100% of the positive irrigation events and between 93% and 100% of the negative irrigation events.
Keywords: Artificial intelligence | Multiobjective genetic algorithm | Irrigation scheduling | Expert systems
مقاله انگلیسی
9 Dynamic multi-objective optimisation using deep reinforcement learning: benchmark, algorithm and an application to identify vulnerable zones based on water quality
بهینه سازی چند منظوره پویا با استفاده از یادگیری تقویت عمیق: معیار ، الگوریتم و برنامه کاربردی برای شناسایی مناطق آسیب پذیر بر اساس کیفیت آب-2019
Dynamic multi-objective optimisation problem (DMOP) has brought a great challenge to the reinforcement learning (RL) research area due to its dynamic nature such as objective functions, constraints and problem parameters that may change over time. This study aims to identify the lacking in the existing benchmarks for multi-objective optimisation for the dynamic environment in the RL settings. Hence, a dynamic multiobjective testbed has been created which is a modified version of the conventional deep-sea treasure (DST) hunt testbed. This modified testbed fulfils the changing aspects of the dynamic environment in terms of the characteristics where the changes occur based on time. To the authors’ knowledge, this is the first dynamic multi-objective testbed for RL research, especially for deep reinforcement learning. In addition to that, a generic algorithm is proposed to solve the multi-objective optimisation problem in a dynamic constrained environment that maintains equilibrium by mapping different objectives simultaneously to provide the most compromised solution that closed to the true Pareto front (PF). As a proof of concept, the developed algorithm has been implemented to build an expert system for a real-world scenario using Markov decision process to identify the vulnerable zones based on water quality resilience in São Paulo, Brazil. The outcome of the implementation reveals that the proposed parity-Q deep Q network (PQDQN) algorithm is an efficient way to optimise the decision in a dynamic environment. Moreover, the result shows PQDQN algorithm performs better compared to the other state-of-the-art solutions both in the simulated and the real-world scenario.
Keywords: Dynamic environment | Reinforcement learning | Deep Q network | Water quality resilience | Meta-policy selection | Artificial intelligence
مقاله انگلیسی
10 MRQAR: a generic MapReduce framework to discover Quantitative Association Rules in Big Data problems
MRQAR: یک چارچوب کلی MapReduce برای کشف قوانین کمی در مشکلات داده های بزرگ-2018
Many algorithms have emerged to address the discovery of quantitative association rules from datasets in the last years. However, this task is becoming a challenge because the processing power of most existing techniques is not enough to handle the large amount of data generated nowadays. These vast amounts of data are known as Big Data. A number of previous studies have been focused on mining boolean or nominal association rules from Big Data problems, nevertheless, the data in real-world applications usually consist of quantitative values and designing data mining algorithms able to extract quantitative association rules presents a challenge to workers in this research field. In spite of the fact that we can find classical methods to discover boolean or nominal association rules in the most well-known repositories of Big Data algorithms, such repositories do not provide methods to discover quantitative association rules. Indeed, no methodologies have been proposed in the literature without prior discretization in Big Data. Hence, this work proposes MRQAR, a new generic parallel framework to discover quantitative association rules in large amounts of data, designed following the MapReduce paradigm using Apache Spark. MRQAR performs an incremental learning able to run any sequential quantitative association rule algorithm in Big Data problems without needing to redesign such algorithms. As a case study, we have integrated the multiobjective evolutionary algorithm MOPNAR into MRQAR to validate the generic MapReduce framework proposed in this work. The results obtained in the experimental study performed on five Big Data problems prove the capability of MRQAR to obtain reduced set of high quality rules in reasonable time.
Keywords: Quantitative association rules , multiobjective evolutionary algorithms , Big Data, MapReduce, Spark
مقاله انگلیسی
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