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نتیجه جستجو - Artificial Bee Colony

تعداد مقالات یافته شده: 13
ردیف عنوان نوع
1 A multi-item supply chain with multi-level trade credit policy under inflation: A mixed mode ABC approach
یک زنجیره تأمین چند ماده ای با سیاست اعتبار تجاری چند سطحی تحت تورم: رویکرد ABC حالت مختلط-2021
In this study, a multi-item supplier-wholesaler-retailer-customers supply chain with partial trade credit policy at each level under inflationary effect for a fixed planning horizon is developed and analysed. Here the wholesaler receives a partial credit period from the supplier, i.e., a credit period on a portion of the amount of units purchased. Wholesaler also offers a partial credit period to its retailer and in turn the retailer also offers a partial credit period to its customers to boost the base demand of any item. Here, credit period induced base demand of any item decreases linearly with time. Demand of the items are also influenced by the respective selling prices. The retailer introduces some promotional cost against advertisement and price discount to improve the demand of the items. Here, it is established that if the wholesaler shares a portion of this promotional cost then the profits of both the retailer and the wholesaler improve. Model is formulated as a mixed-integer profit maximization problem and is analysed in crisp as well as in imprecise (fuzzy/rough) environment and some managerial insights are outlined. To find the marketing decision of such a real-life supply chain model, here, a new variant of ABC is proposed for mixed-integer optimization problems. The algorithm is tested against a set of benchmark test problems available in the literature and its efficiency to solve such problems is well established.
Key words : Supply chain | Partial trade credit period | Inflation | Promotional cost sharing | Artificial Bee Colony.
مقاله انگلیسی
2 A decomposition-based many-objective artificial bee colony algorithm with reinforcement learning
یک الگوریتم مستعمره مصنوعی زنبورعسل مبتنی بر تجزیه با یادگیری تقویتی-2020
When optimizing many-objective optimization problems (MaOPs), the optimization effect is normally related to the problem types. Therefore, enhancing the generalization ability is essential to the application of the algorithms. In this paper, a novel decomposition-based Artificial bee colony algorithm (ABC) for MaOP optimization, MaOABC/D-LA, is presented to enhance the generalization ability. A reinforcement learning-based searching strategy is designed in the MaOABC/D-LA, with which the algorithm adjusts its searching actions according to their performance. And a variant of the onlooker bee mechanism is proposed to balance the optimization quality. To investigate performance of the proposed algorithm, a comparison experiment is conducted. The experimental results show that the MaOABC/D-LA outperforms the peer algorithms in efficiency and solution quality for MaOPs with different types of features. This indicates the proposed method has a definite effect on improving generalization ability.
Keywords: Swarm intelligence | Artificial bee colony | Many-objective optimization | Reinforcement learning | Decomposition strategy
مقاله انگلیسی
3 A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization
یک الگوریتم ژنتیکی زنبورعسل مصنوعی برای بهینه سازی داده های بزرگ مبتنی بر بازسازی سیگنال-2020
In recent years, the researchers have witnessed the changes or transformations driven by the existence of the big data on the definitions, complexities and future directions of the real world optimization problems. Analyzing the capabilities of the previously introduced techniques, determining possible drawbacks of them and developing new methods by taking into consideration of the unique properties related with the big data are nowadays in urgent demands. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging behaviors of the real honey bees is one of the most successful swarm intelligence based optimization algorithms. In this study, a novel ABC algorithm based big data optimization technique was proposed. For exploring the solving abilities of the proposed technique, a set of experimental studies has been carried out by using different signal decomposition based big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies first were compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC (qABC). The results of the proposed ABC algorithm were also compared with the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase Base Optimization (PBO) algorithm, Particle Swarm Optimization (PSO) algorithm and Dragonfly algorithm (DA) based big data optimization techniques. From the experimental studies, it was understood that the newly introduced ABC algorithm based technique is capable of producing better or at least promising results compared to the mentioned big data optimization techniques for all of the benchmark instances.
Keywords: Big data optimization | Signal decomposition | Artificial Bee Colony
مقاله انگلیسی
4 Swarm intelligence techniques in recommender systems - A review of recent research
تکنیک های هوش ازدحام در سیستم های توصیه کننده - مروری بر تحقیقات اخیر-2019
One of the main current applications of Intelligent Systems are Recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them are Swarm Intelligence (SI) techniques, which are an emerging trend with various application areas. Although the interest in using Computational Intelligence in web personalization and information retrieval fostered the publication of some survey papers, these surveys so far focused on different application domains, e.g., clustering, or were too broadly focused and incorporated only a handful of SI approaches. This study provides a comprehensive review of 77 research publications applying SI in RS. The study focus on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported results. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this survey is the most comprehensive review of approaches using SI in RS. Therefore, we believe this review will be a relevant material for researchers interested in either of the domains.
Keywords: Swarm intelligence | Particle swarm optimization | Ant colony optimization | Invasive weed optimization | Artificial bee colony | Recommender systems | Personalization
مقاله انگلیسی
5 Toward modeling and optimization of features selection in Big Data based social Internet of Things
به سوی مدل سازی و بهینه سازی انتخاب ویژگی ها در داده های بزرگ مبتنی بر اینترنت اشیا اجتماعی-2018
The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where a selection of appropriate features and High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years. Therefore, keeping in view the needs above, there is a requirement for a system that can efficiently select features and analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.
Keywords: SIoT ، Big Data ، ABC algorithm، Feature selection
مقاله انگلیسی
6 Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand
پیش بینی سری های زمانی کسب و کارهای کشاورزی با استفاده از شبکه های عصبی موج کوچک و بهینه سازی اکتشافی ذهنی متا: یک تحلیل روی قیمت یک گونی سویبان و تقاضای محصولات فاسد شدنی-2018
Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long-term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short-term and long-term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long-term forecasting, which is considered more difficult than the short-term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.
keywords: Agribusiness |Artificial neural networks |Time series forecasting |Metaheuristics |Natural computing |Optimization
مقاله انگلیسی
7 الگوریتم خوشه‌بندی برای پروتکل مسیریابی AODV مبتنی بر کلونی مصنوعی زنبور عسل در MANET
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 26
یکی از مهم‌ترین چالش‌هایی که MANET با آن مواجه است، چگونگی اتصال گره‌ها به یکدیگر و همچنین چگونگی تغییرات دینامیکی در توپولوژی شبکه است. یک الگوریتم خوشه‌بندی جدید که افزایش ثبات و سازگاری MANET را تضمین می‌کند پیشنهاد شده است، این مبتنی بر کلونی مصنوعی زنبور عسل (ABC) برای تعیین سرخوشه (CH) در هر خوشه با توجه به گروهی از پارامترها برای محاسبه تابع تناسب پیشنهادی همچنین برای مدیریت پیام‌های کنترل ترافیک است.
مفاهیم CCS
شبکه‌ها ← شبکه‌های متحرک اد هاک
کلیدواژه‌ها: شبکه‌های متحرک اد هاک | کلونی مصنوعی زنبور عسل | AODV | تحرک | انرژی | خوشه.
مقاله ترجمه شده
8 A novel data clustering algorithm based on modified gravitational search algorithm
یک الگوریتم نوین برای خوشه بندی داده ها برمبنای الگوریتم جستجوی گرانشی اصلاح شده-2017
Data clustering is a popular analysis tool for data statistics in many fields such as pattern recognition, data mining, machine learning, image analysis, and bioinformatics. The aim of data clustering is to represent large datasets by a fewer number of prototypes or clusters, which brings simplicity in modeling data and thus plays a central role in the process of knowledge discovery and data mining. In this paper, a novel data clustering algorithm based on modified Gravitational Search Algorithm is proposed, which is called Bird Flock Gravitational Search Algorithm (BFGSA). The BFGSA introduces a new mechanism into GSA to add diversity, a mechanism which is inspired by the collective response behavior of birds. This mechanism performs its diversity enhancement through three main steps including initialization, identification of the nearest neighbors, and orientation change. The initialization is to generate candidate populations for the second steps and the orientation change updates the position of objects based on the nearest neighbors. Due to the collective response mechanism, the BFGSA explores a wider range of the search space and thus escapes suboptimal solutions. The performance of the proposed algorithm is evaluated through 13 real benchmark datasets from the well-known UCI Machine Learning Repository. Its performance is compared with the standard GSA, the Artificial Bee Colony (ABC), the Particle Swarm Optimization (PSO), the Firefly Algorithm (FA), K-means, and other four clustering algorithms from the literature. The simulation results indicate that the BFGSA can effectively be used for data clustering.
Keywords: Gravitational search algorithm | Learning algorithm | Collective behavior | Data clustering | Clustering Validation | Nature-inspired algorithm
مقاله انگلیسی
9 A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence
یک دیدگاه ترکیبی جدید برای انتخاب ویژگی و انتخاب مدل ماشینی برداری پشتیبانی برمبنای هوش هم گروه خود - منطبق-2017
This research proposes a new hybrid approach for feature selection and Support Vector Machine (SVM) model selection based on a new variation of Cohort Intelligence (CI) algorithm. Feature selection can improve the accuracy of classification algorithms and reduce their computation complexity by removing the irrelevant and redundant features. SVM is a classification algorithm that has been used in many ar eas, such as bioinformatics and pattern recognition. However, the classification accuracy of SVM depends mainly on tuning its hyperparameters (i.e., SVM model selection). This paper presents a framework that is comprised of the following two major components. First, Self-Adaptive Cohort Intelligence (SACI) algo rithm is proposed, which is a new variation of the emerging metaheuristic algorithm, Cohort Intelligence (CI). Second, SACI is integrated with SVM resulting in a new hybrid approach referred to as SVM–SACI for simultaneous feature selection and SVM model selection. SACI differs from CI by employing tournament based mutation and self-adaptive scheme for sampling interval and mutation rate. Furthermore, SACI is both real-coded and binary-coded, which makes it directly applicable to both binary and continuous do mains. The performance of SACI for feature selection and SVM model selection was examined using ten benchmark datasets from the literature and compared with those of CI and five well-known metaheuris tics, namely, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) and Artificial Bee Colony (ABC). The comparative results demonstrate that SACI outperformed CI and compa rable to or better than the other compared metaheuristics in terms of the SVM classification accuracy and dimensionality reduction. In addition, SACI requires less tuning efforts as the number of its control parameters is less than those of the other compared metaheuristics due to adopting the self-adaptive scheme in SACI. Finally, this research suggests employing more efficient methods for high-dimensional or large datasets due to the relatively high training time required by search strategies based on metaheuris tics when applied to such datasets.
Keywords: Feature selection | SVM | Classification | Cohort intelligence | Metaheuristic
مقاله انگلیسی
10 خوشه‌بندی پویا با الگوریتم کلونی زنبور عسل مصنوعی بهبودیافته
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 32
یکی از معروف‌ترین نسخه‌های دودویی (گسسته ی) الگوریتم کلونی زنبور عسل مصنوعی عبارتست از کلونی زنبور عسال مصنوعی مبتنی بر اندازه‌گیری شباهت، که برای اولین بار برای مسئله‌ی مکان‌یابی مراکزی با ظرفیت سرویس نامحدود (UFLP) پیشنهاد شد. این کلونی زنبور عسل مصنوعی گسسته بطور ساده به اندازه‌گیری شباهت بین بردارهای دودویی از طریق ضریب (تشابه) جاکارد، بستگی دارد. اگرچه مکانیزم اعمال شده برای تولید راه‌حل‌های جدید در رابطه با اطلاعات تشابه بین راه‌حل‌ها، به عنوان یک نسخه‌ی دودویی ساده، جدید و کارآ از کلونی زنبور عسل مصنوعی چذیرفته شده است، ولی آن فقط یک مورد شباهت را مدّنظر قرار می‌دهد؛ یعنی، همه‌ی حالت‌های شباهت را درنظر نمی‌گیرد. برای پوشش این مسئله، مکانیزم تولید راه‌حل جدید مربوط به کلونی زنبور عسل مصنوعی با استفاده از حالت‌های شباهت و از طریق اجزای الهام گرفته شده از ژنتیک، بهبود یافته است. علاوه بر این، مزیت الگوریتم پیشنهادی با مقایسه‌ی آن با کلونی زنبور عسل مصنوعی پایه‌ای، بهینه‌سازی ازدحام ذرات دودویی، و الگوریتم ژنتیک در خوشه‌بندی پویا (خودکار) اثبات می‌شود که در این نوع خوشه‌بندی تعداد خوشه‌ها بصورت خودکار تعیین می‌شود، یعنی برخلاف تکنیک‌های کلاسیک، نیازی نیست که این تعداد را در همان ابتدای کار مشخص کرد. نه تنها الگوریتم‌های مبتنی بر محاسبات تکاملی، بلکه رویکردهای کلاسیک مانند C-means فازی و K-means نیز برای اثبات اثربخشی رویکرد پیشنهادی در خوشه‌بندی، مورد استفاده قرار می‌گیرند. نتایج بدست آمده حاکی از این هستند که کلونی زنبور عسل مصنوعی گسسته با عنصر تولید‌کننده‌ی راه‌حل بهبودیافته قادر به دستیابی به راه‌حل‌هایی معتبرتر از دیگر الگوریتم‌ها در خوشه‌بندی پویا می‌باشد، در حالیکه این مسئله توسط محققان به عنوان یکی از سخت‌ترین مسائل NP-hard به شدت مورد پذیرش قرار گرفته است.
کلمات کلیدی: آنالیز خوشه ای | خوشه بندی خودکار | بهینه سازی گسسته | الگوریتم کلونی زنبور عسل مصنوعی دودویی
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