با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Tabu search for min-max edge crossing in graphs
جستجوی تابو برای عبور از لبه های حداقل حداکثر در گراف ها -2020
Graph drawing is a key issue in the field of data analysis, given the ever-growing amount of information available today that require the use of automatic tools to represent it. Graph Drawing Problems (GDP) are hard combinatorial problems whose applications have been widely relevant in fields such as social network analysis and project management. While classically in GDPs the main aesthetic concern is re- lated to the minimization of the total sum of crossing in the graph (min-sum), in this paper we focus on a particular variant of the problem, the Min-Max GDP, consisting in the minimization of the maximum crossing among all egdes. Recently proposed in scientific literature, the Min-Max GDP is a challenging variant of the original min-sum GDP arising in the optimization of VLSI circuits and the design of in- teractive graph drawing tools. We propose a heuristic algorithm based on the tabu search methodology to obtain high-quality solutions. Extensive experimentation on an established benchmark set with both previous heuristics and optimal solutions shows that our method is able to obtain excellent solutions in short computation time.
Keywords: Combinatorial optimization | Graph drawing | Metaheuristics
A survey of hybrid metaheuristics for the resource-constrained project scheduling problem
بررسی استعاره ترکیبی برای مشکل برنامه ریزی پروژه با محدودیت منابع-2020
The Resource-Constrained Project Scheduling Problem (RCPSP) is a general problem in scheduling that has a wide variety of applications in manufacturing, production planning, project management, and var- ious other areas. The RCPSP has been studied since the 1960s and is an NP-hard problem. As being an NP-hard problem, solution methods are primarily heuristics. Over the last two decades, the increasing interest in operations research for metaheuristics has resulted in a general tendency of moving from pure metaheuristic methods for solving the RCPSP to hybrid methods that rely on different metaheuristic strategies. The purpose of this paper is to survey these hybrid approaches. For the primary hybrid meta- heuristics that have been proposed to solve the RCPSP over the last two decades, a description of the basic principles of the hybrid metaheuristics is given, followed by a comparison of the results of the dif- ferent hybrids on the well-known PSPLIB data instances. The distinguishing features of the best hybrids are also discussed.
Keywords: Project scheduling| Resource constraints | RCPSP | Metaheuristics | Hybrids
Hybrid multi-objective evolutionary algorithm based on Search Manager framework for big data optimization problems
الگوریتم تکاملی چند منظوره ترکیبی مبتنی بر چارچوب مدیر جستجو برای مسائل بهینه سازی داده های بزرگ-2020
Big Data optimization (Big-Opt) refers to optimization problems which require to manage the properties of big data analytics. In the present paper, the Search Manager (SM), a recently proposed framework for hybridizing metaheuristics to improve the performance of optimization algorithms, is extended for multi-objective problems (MOSM), and then five configurations of it by combination of different search strategies are proposed to solve the EEG signal analysis problem which is a member of the big data optimization problems class. Experimental results demonstrate that the proposed configurations of MOSM are efficient in this kind of problems. The configurations are also compared with NSGA-III with uniform crossover and adaptive mutation operators (NSGA-III UCAM), which is a recently proposed method for Big-Opt problems.
Keywords: Big Data optimization | Hybrid multi-objective evolutionary algorithm | Search Manager framework | Evolutionary operators
Implementation of nature-inspired optimization algorithms in some data mining tasks
اجرای الگوریتم های بهینه سازی با الهام از طبیعت در برخی از کارهای داده کاوی-2019
Data mining optimization received much attention in the last decades due to introducing new optimization techniques, which were applied successfully to solve such stochastic mining problems. This paper addresses implementation of evolutionary optimization algorithms (EOAs) for mining two famous data sets in machine learning by implementing four different optimization techniques. The selected data sets used for evaluating the proposed optimization algorithms are Iris dataset and Breast Cancer dataset. In the classification problem of this paper, the neural network (NN) is used with four optimization techniques, which are whale optimization algorithm (WOA), dragonfly algorithm (DA), multiverse optimization (MVA), and grey wolf optimization (GWO). Different control parameters were considered for accurate judgments of the suggested optimization techniques. The comparitive study proves that, the GWO, and MVO provide accurate results over both WO, and DA in terms of convergence, runtime, classification rate, and MSE.
Keywords: Data mining | Optimization | Evolutionary computation | Multi-layer perceptron | Metaheuristics
Improving collection flows in a public postal network with contractors obligation considerations
بهبود جریان های مجموعه ای در یک شبکه پستی دولتی با ملاحظات الزام طرف قرارداد-2018
We examine a problem that arises in the postal industry of developing goods collection routes that are subjected to a bidding process where the price is positively correlated to routes operating complexities. The route complexity would delay the arrival of goods to distribution centers and prevent their deliveries to processing plants within the required time windows. We formulate the problem as a variant of the multiple depot vehicle routing problem with time windows. The ant colony optimization algorithm is discussed as a solution methodology and evaluated in a case study that involves a real-life problem faced by a public postal service organization.
keywords: Postal last mile collection problem |Multiple depot vehicle routing problem with time windows |Ant colony optimization |Metaheuristics
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
Static force capability optimization of humanoids robots based on modified self-adaptive differential evolution
بهینه سازی قابلیت نیروی استاتیک از ربات های انسان نما بر مبنای تکامل تفاضلی خودساخته ی اصلاح شده-2017
Article history:Received 7 November 2015Revised 20 October 2016Accepted 24 October 2016Available online 27 October 2016Keywords:Optimization metaheuristic Differential evolution Constrained optimization Humanoid robotStatic force capabilityThe current society requires solutions for many problems in safety, economy, and health. The social con- cerns on the high rate of repetitive strain injury, work-related osteomuscular disturbances, and domestic issues involving the elderly and handicapped are some examples. Therefore, studies on complex machines with structures similar to humans, known as humanoids robots, as well as emerging optimization meta- heuristics have been increasing. The combination of these technologies may result in robust, safe, reliable, and ﬂexible machines that can substitute humans in multiple tasks. In order to contribute to this topic, the static modeling of a humanoid robot and the optimization of its static force capability through a modiﬁed self-adaptive differential evolution (MSaDE) approach is proposed and evaluated in this study. Unlike the original SaDE, MSaDE employs a new combination of strategies and an adaptive scaling factor mechanism. In order to verify the effectiveness of the proposed MSaDE, a series of controlled experiments are performed. Moreover, some statistical tests are applied, an analysis of the results is carried out, and a comparative study of the MSaDE performance with other metaheuristics is presented. The results show that the proposed MSaDE is robust, and its performance is better than other powerful algorithms in the literature when applied to a humanoid robot model for the pushing and pulling tasks.© 2016 Elsevier Ltd. All rights reserved.
Keywords: Optimization metaheuristic | Differential evolution | Constrained optimization | Humanoid robot | Static force capability
Parallel Genetic Algorithm for Capacitated P-median Problem
الگوریتم ژنتیک موازی برای مسئله P-median-2017
This paper presents an implementation of a specific genetic algorithm on a high performance computing cluster. The algorithm is designed to approximately solve the capacitated p-median problem. Since the p-median problem has been proven to be NP-hard, exact algorithms are not efficient in solving it in reasonable time. Therefore it is helpful to use metaheuristics like genetic algorithm. In an endeavor to obtain the best solution, even for large instances, we look for best ways how to use all computing power that is in our disposal. The obvious method to achieve that is to design parallel algorithm and let it run on a high performance computing cluster.
Keywords: Capacitated p-median problem | Genetic algorithm | Parellel computing | HPC cluster | Heuristic
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
Real-time metaheuristic-based urban crossroad management with multi-variant planning
مدیریت زمان تقاطع شهری مبتنی بر روش مکاشفه ای در زمان واقعی با برنامه ریزی چند متغیره-2017
This paper presents a multi-variant planning method for the problem of multi-lane crossroad manage ment. The method leverages a metaheuristic system which is aimed at real-time usage. Being the basis of the experiments shown, its implementation is scalable and can efficiently use a basic multi-core hard ware infrastructure. The whole system can provide a sub-optimal yet useful crossroad management plan and is perceived to be superior to the classic and competitive methods previously tested.
Keywords: Urban traffic planning | Multi-variant planning | Optimization | Metaheuristics