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نتیجه جستجو - الگوریتم های تکاملی

تعداد مقالات یافته شده: 13
ردیف عنوان نوع
1 A two-stage multi-operator differential evolution algorithm for solving Resource Constrained Project Scheduling problems
یک الگوریتم تکامل دیفرانسیل چند مرحله ای چند کاره برای حل مشکلات برنامه ریزی پروژه محدود شده از منابع-2020
The Resource Constrained Project Scheduling problem (RCPSP) is a complex and combinatorial optimization problem mostly relates with project management, construction industries, production planning and manufacturing domains. Although several solution methods have been proposed, no single method has been shown to be the best. Further, optimal solution of this type of problem requires different requirements of the exploration and exploitation at different stages of the optimization process. Considering these requirements, in this paper, a two-stage multi-operator differential evolution (DE) algorithm, called TS-MODE, has been developed to solve RCPSP. TS-MODE starts with the exploration stage, and based on the diversity of population and the quality of solutions, this approach dynamically place more importance on the most-suitable DE, and then repeats the same process during the exploitation phase. A complete evaluation of the components and parameters of the algorithms by a Design of Experiments technique is also presented. A number of single-mode RCPSP data sets from the project scheduling library (PSPLIB) have been considered to test the effectiveness and performance of the proposed TS-MODE against selected recent well-known state-of-the-art algorithms. Those results reveal the efficiency and competitiveness of the proposed TS-MODE approach.
Keywords: Evolutionary algorithms | Differential evolution | Adaptive operator selection | Resource constrained project scheduling | problems
مقاله انگلیسی
2 Optimum management of power and energy in low voltage microgrids using evolutionary algorithms and energy storage
مدیریت بهینه انرژی و برق در ریز شبکه های ولتاژ کم با استفاده از الگوریتم های تکاملی و ذخیره انرژی-2020
Microgrids are subsystems in which some loads and distributed energy resources are controlled in a coordinated manner. In recent years, microgrids have been proposed as a solution to enhance critical infrastructures’ resilience and the integration of distributed energy resources. There are many solutions on microgrid planning, as well as some practical experience on microgrids’ implementation. However, choosing microgrid optimal control strategy is strongly related to the individual structure, components and configuration of microgrid. Among others, the advantages of microgrids include improved energy efficiencies, minimized operating costs and improved environmental impacts. Achieving these targets necessitates optimal control of all energy components in the microgrid. Main contribution of this paper are two control strategies of power and energy management for synchronous microgrid operation, which have been analyzed for a specific low voltage microgrid configuration. The first strategy reduces power and energy losses, thus improving the entire microgrid system’s efficiency. The second minimizes operating costs. An evolutionary algorithm was developed to control the components of the microgrid, including e.g. micro-sources and energy storage. The method of technical and economic energy storage system sizing for microgrid optimal operation is also proposed.
Keywords: Battery energy storage unit | Distributed generation | Evolutionary algorithm | Microgrid optimization | Power and energy management
مقاله انگلیسی
3 Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods
نقشه برداری بالقوه چشمه آب های زیرزمینی با استفاده از الگوریتم های تکاملی مبتنی بر جمعیت و روش های داده کاوی-2019
Water scarcity inmany regions of theworld has become an unpleasant reality. Groundwater appears to be one of the main natural resources capable to reverse this situation. Uncovering the spatial patterns of groundwater occurrence is a crucial factor that could assist in carrying out successful water resources management projects. The main objective of the current study was to provide a novel methodology approach which utilized Genetic Algorithm( GA) in order to performa feature selection procedure and data mining methods for generating a groundwater spring potential map. Three data mining methods, Naïve Bayes (NB), Support Vector Machine (SVM) and RandomForest (RF) were utilized to construct a groundwater spring potential map that had over 0.81 probability of occurrence for the Wuqi County, Shaanxi Province, China. Groundwater spring locations and sixteen related variables were analyzed, namely: lithology, soil cover, land use cover, normalized difference vegetation index (NDVI), elevation, slope angle, aspect, planform curvature, profile curvature, curvature, stream power index (SPI), stream transport index (STI), topographic wetness index (TWI), mean annual rainfall, distance from river network and distance from road network. The Frequency ratio method was used to weight the variables, whereas a multi-collinearity analysis was performed to identify the relation between the parameters and to decide about their usage. The optimal set of parameters, which was determined by the GA, reduced the number of parameters into twelve removing planformcurvature, profile curvature, curvature and STI. The Receiver Operating Characteristic curve and the area under the curve (AUROC) were estimated so as to evaluate the predictive power of eachmodel. The results indicated that the optimizedmodels were superior in accuracy than the original models. The optimized RF model produced the best results (0.9572), followed by the optimized SVM (0.9529) and the optimized NB (0.8235). Overall, the current study highlights the necessity of applying feature selection techniques in groundwater spring assessments and also that data miningmethods may be a highly powerful investigation approach for groundwater spring potential mapping.
Keywords: Groundwater spring potential mapping | Genetic algorithm | Naïve Bayes | Support Vector Machine | Random Forest | China
مقاله انگلیسی
4 Decision tree underfitting in mining of gene expression data. An evolutionary multi-test tree approach
درخت تصمیم گیری در زمینه استخراج داده های بیان ژن یک رویکرد درخت چند آزمون تکاملی-2019
The problem of underfitting and overfitting in machine learning is often associated with a bias-variance trade-off. The underfitting most clearly manifests in the tree-based inducers when used to classify the gene expression data. To improve the generalization ability of decision trees, we are introducing an evo- lutionary, multi-test tree approach tailored to this specific application domain. The general idea is to apply gene clusters of varying size, which consist of functionally related genes in each splitting rule. It is achieved by using a few simple tests that mimic each other’s predictions and built-in information about the discriminatory power of genes. The tendencies to underfit and overfit are limited by the multi- objective fitness function that minimizes tree error, split divergence and attribute costs. Evolutionary search for multi-tests in internal nodes, as well as the overall tree structure, is performed simultaneously. This novel approach called Evolutionary Multi-Test Tree (EMTTree) may bring far-reaching benefits to the domain of molecular biology including biomarker discovery, finding new gene-gene interactions and high-quality prediction. Extensive experiments carried out on 35 publicly available gene expression datasets show that we managed to significantly improve the accuracy and stability of decision tree. Im- portantly, EMTTree does not substantially increase the overall complexity of the tree, so that the patterns in the predictive structures are kept comprehensible.
Keywords: Data mining | Evolutionary algorithms | Decision trees | Underfitting | Gene expression data
مقاله انگلیسی
5 Application of multi-objective optimization to blind source separation
استفاده از بهینه سازی چند هدفه برای جداسازی منبع کور-2019
Several problems in signal processing are addressed by expert systems which take into account a set of priors on the sought signals and systems. For instance, blind source separation is often tackled by means of a mono-objective formulation which relies on a separation criterion associated with a given property of the sought signals (sources). However, in many practical situations, there are more than one property to be exploited and, as a consequence, a set of separation criteria may be used to recover the original signals. In this context, this paper addresses the separation problem by means of an approach based on multi-objective optimization. Differently from the existing methods, which provide only one estimate for the original signals, our proposal leads to a set of solutions that can be utilized by the system user to take his/her decision. Results obtained through numerical experiments over a set of biomedical signals highlight the viability of the proposed approach, which provides estimations closer to the mean squared error solutions compared to the ones achieved via a mono-objective formulation. Moreover, since our proposal is quite general, this work also contributes to encourage future researches to develop expert systems that exploit the multi-objective formulation in different source separation problems.
Keywords: Blind source separation | Multi-objective optimization | Evolutionary algorithms
مقاله انگلیسی
6 A weighted multi-attribute-based recommender system using extended user behavior analysis
یک سیستم توصیه گر وزنی مبتنی بر چند برخوردی با استفاده از تحلیل مبسوط رفتار کاربر-2018
A new weighted multi-attribute based recommender system (WMARS) has been developed using extended user behavior analysis. WMARS obtained data from number of clicked items in the recommendation list, sequence of the clicked items in recommendation the list, duration of tracking, number of tracking same item, likes/dislikes, association rules of clicked items, remarks for items. WMARS has been applied to a movie web site. The experimental results have been obtained from a total of 567 heterogeneous users, including employers in different sectors, different demographic groups, and undergraduate and graduate students. Using different weighted sets of the attributes’ parameters, WMARS has been tested and compared extensively with collaborative filtering. The experimental results show that WMARS is more successful than collaborative filtering for the data set that was used.
keywords: Collaborative filtering |Evolutionary algorithms |Recommender systems |Relevance feedback |User behavior analysis
مقاله انگلیسی
7 A multi-objective evolutionary approach for mining frequent and high utility itemsets
یک روش تکاملی چند هدفه برای استخراج مجموعه اقلام مکرر و سودمند-2018
Mining interesting itemsets with both high support and utility values from transactional database is an important task in data mining. In this paper, we consider the two measures support and utility in a unified framework from a multi-objective view. Specifically, the task of mining frequent and high utility itemsets is modeled as a multi-objective problem. Then, a multi-objective itemset mining algorithm is proposed for solving the transformed problem, which can provide multiple itemsets recommendation for decision makers in only one run. One key advantage of the proposed multi-objective algorithm is that it does not need to specify the prior parameters such as minimal support threshold min sup and minimal utility threshold min uti, which brings much convenience to users. The experimental results on several real datasets demonstrate the effectiveness of the proposed algorithm. In addition, comparison results show that the proposed algorithm can provide more diverse yet frequent and high utility itemsets in only one run.
Keywords: Frequent itemset mining ، High utility itemset mining ، Data mining ، Multi-objective optimization ، Evolutionary algorithms
مقاله انگلیسی
8 Active control for traffic lights in regions and corridors: an approach based on evolutionary computation
کنترل فعال برای چراغ های راهنمایی در مناطق و راهرو: یک رویکرد مبتنی بر محاسبات تکاملی-2017
The growth of vehicles’ fleet circulating on urban streets constitutes a very strong tendency in recent years. The main consequence of this phenomenon refers to the increase of urban congestions, of average delays caused by vehicles waiting on traffic lights and of number of stops. Finding strategies to achieve efficient active traffic control in urban centers is a challenge for engineers and analysts. Recently, important research on dynamic networks and Intelligent Transportation Systems using computational intelligence modeling techniques has been done. This paper proposes a new scheme of active control, using optimization algorithms, to dynamically find traffic signal control plans that optimize traffic conditions in delimited networks and corridors. The proposed system includes a time delay predictive model, used in conjunction with evolutionary approaches like genetic algorithms and differential evolution techniques. Conceptual and applied computational representations necessary for the construction of models are presented. Data collected from a big city in Brazil were fed into the commercial microscopic simulator AIMSUN and were used for the practical experiments. Two main experiments were undertaken and statistically compared in order to decide which method is more efficient in optimizing the active traffic signal timing control for the region under study.
Keywords: intelligent transportation systems | traffic lights programming | evolutionary algorithms | optimization | active traffic control
مقاله انگلیسی
9 الگوریتم Krill herd برای محل بهینه تولید توزیع شده در سیستم توزیع شعاعی
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 14 - تعداد صفحات فایل doc فارسی: 48
تولید پراکنده (DG) به عنوان یک راه حل مناسب برای کنترل تلفات خط، ولتاژ گذر، ثبات ولتاژ و غیره به رسمیت شناخته شده و نشان دهنده یک عصر جدید برای سیستم های توزیع است. این مقاله در حال توسعه رویکردی برای قرار دادن DG به منظور به حداقل رساندن از دست دادن قدرت و انرژی فعال خطوط توزیع است، در حالی که ولتاژ باس و شاخص پایداری ولتاژ را در محدوده مشخص یک سیستم قدرت معین حفظ می کند. بهینه سازی بر اساس محل مطلوب و اندازه بهینه از DG انجام شده است. این مقاله یک روش الگوریتم کریل گله جدید، کارآمد (KHA) را برای حل مشکل تخصیص بهینه DG شبکه های توزیع توسعه داده است. برای تست امکان سنجی و اثربخشی، الگوریتم KH ارائه شده بر روی 33 باس، 69-باس و 118 باس شبکه های توزیع شعاعی استاندارد تست شده است. نتایج شبیه سازی نشان می دهد که نصب DG در محل مطلوب به طور قابل توجهی می تواند سبب کاهش از دست دادن قدرت سیستم برق توزیع شده شود. علاوه بر این، نتایج عددی، در مقایسه با دیگر الگوریتم های جستجوی تصادفی مانند الگوریتم ژنتیک (GA)، بهینه سازی ازدحام ذرات (PSO)، همراه GA و PSO (GA / PSO و عامل حساسیت از دست دادن شبیه سازی آنیلینگ (LSFSA)، نشان می دهد که KHA می تواند راه حل هایی با کیفیت بهتر را پیدا کند.
کلمات کلیدی: سیستم توزیع شعاعی | ژنراتور توزیع | کاهش تلفات | الگوریتم های تکاملی | الگوریتم کریل هرد | تکامل تفاضلی
مقاله ترجمه شده
10 پوشش منطقه صرفه جویی انرژی با استفاده از الگوریتم های زمانبندی گره دوربین تکاملی در شبکه های حسگر بصری
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 31
پوشش منطقه یک مسئله تحقیق مهم در زمینه شبکه های حسگر بصری (VSN) ها به علت محدودیت های ذاتی VSN ها، مانند منابع غیر قابل شارژ انرژی و جهت محدوده حس گر گره های دوربین است. استقرار انبوه گره های دوربین، امکان ارائه یک پوشش منطقه رضایت بخش برای مدت طولانی را فراهم می کند. در عین حال بقیه گره های دوربین را می توان خاموش کرد و زمانی که لازم باشد، گره های فعال جایگزین می شود. در این مقاله، مساله پوشش منطقه در VSN ها را تعریف می کنیم که هدف آنها به حداقل رساندن سلول های شبکه ای خالی و اضافی از یک منطقه مطلوب و اعوجاج انرژی گره های دوربین است. سپس ما دو الگوریتم زمانبندی برای گره های دوربین پیشنهاد می کنیم که به طور تصادفی به k-پوشش منطقه مورد نظر اعمال می شود. در اولین الگوریتم به نام زمانبندی گره دوربین تکامل یافته (ECNS)، هدف ما به دست آوردن حداکثر پوشش منطقه با قرار دادن کوچکترین (کمترین) تعداد گره های دوربین در حالت فعال و به حداقل رساندن سلول های شبکه ای خالی و بیش از حد است. از آنجایی که اهداف در نظر گرفته شده در ECNS، یکدیگر را متضاد می شمارند(با یکدیگر تضاد و ناسازگاری دارند)، از روش جمع بندی وزن متعادل استفاده می کنیم تا اهدافمان را به یک معادله خطی ترسیم کنیم و سپس یک الگوریتم ژنتیکی برای یافتن حداقل مقدار معادله خطی یکپارچه پیشنهاد می کنیم. در الگوریتم دوم زمانبندی گره دوربین تکاملی آگاه از انرژی (EAECNS)، ما یک روش برای تعادل بین مصرف انرژی تمام گره های دوربین را پیشنهاد می دهیم در حالی که آن پوشش رضایت بخش منطقه مورد نظر را فراهم می کند و تعداد سلول های شبکه ای پوشش داده شده اضافی را پایین نگه می دارد. ما عملکرد هر دو الگوریتم را از نظر پوشش، تعداد گره های زنده و افزونگی با شبیه سازی های بعدی ارزیابی می کنیم. همچنین نشان می دهد که EAECNS دارای عملکرد برتر در مقایسه با ECNS و دیگر الگوریتم های پیشرفته تر است.
کلمات کلیدی: شبکه های حسگر بصری (WSN) | پوشش منطقه | الگوریتم های تکاملی | زمانبندی گره های دوربین
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