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حرية الوصول المقاله
1 - Support Vector Regression Parameters Optimization using Golden Sine Algorithm and Its Application in Stock Market
Mohammadreza Ghanbari Mahdi GoldaniStock price prediction is one of the most important concerns of stockholders. This prediction, independent of the method which is used or the assumptions which are applied, is welcomed and trusted if it can guarantee a high fitting. So due to the high performance predic أکثرStock price prediction is one of the most important concerns of stockholders. This prediction, independent of the method which is used or the assumptions which are applied, is welcomed and trusted if it can guarantee a high fitting. So due to the high performance prediction, using some complicated models as Machine Learning family such as Support Vector Regression (SVR) was recommended instead of older and lower performance approaches such as multiple discriminant technique. SVR model have achieved high performance on forecasting problems, however, its performance is highly dependent on the appropriate selection of SVR parameters. In this study, a novel GSA-SVR model based on Golden Sine Algorithm is presented. The performance of the proposed model is compared with eleven other meta-heuristic algorithms on some stocks from NASDAQ. The results indicate that the given model here is capable of optimizing the SVR parameters very well and indeed is one of the best models judged by both prediction performance accuracy and time consumption. تفاصيل المقالة -
حرية الوصول المقاله
2 - برنامه ریزی خطوط هواپیمایی با در نظر گرفتن محدودیت‌های عملیاتی
شکوه خردمند محمد محمدی بهمن نادریاز گذشته تا به امروز به علت بالا بودن هزینه‌های مربوط به صنعت هوایی و هواپیماها، برنامه‌ریزی برای این صنعت از اهمیت زیادی برخوردار بوده است. یکی از مسائل مربوط به صنعت هوایی، مسئله‌ی مسیریابی هواپیماها و تعیین تعداد هواپیماهای مورد نیاز در هر برنامه‌ی پروازی است. این مس أکثراز گذشته تا به امروز به علت بالا بودن هزینه‌های مربوط به صنعت هوایی و هواپیماها، برنامه‌ریزی برای این صنعت از اهمیت زیادی برخوردار بوده است. یکی از مسائل مربوط به صنعت هوایی، مسئله‌ی مسیریابی هواپیماها و تعیین تعداد هواپیماهای مورد نیاز در هر برنامه‌ی پروازی است. این مسائل، بخش قابل توجهی از هزینه‌های صنعت هوایی را برعهده دارند. از طرفی با توجه به یک سری محدودیت‌ها و قوانینی که شرکت‌های هواپیمایی در برنامه‌ها‌ی پروازی برای هر هواپیما دارند، به دست آوردن یک برنامه‌ی پروازی مناسب به منظور تعیین تعداد هواپیماهای مورد نیاز، کمینه سازی هزینه‌های مربوط به هواپیماها و هزینه‌های ناشی از فروش از دست رفته دارای اهمیت زیادی است. از طرفی با توجه به NP-hard بودن این مسئله، حل دقیق مدل در سایزهای بزرگ امکان پذیر نمی‌باشد؛ لذا با کمک الگوریتم‌های فراابتکاری چون ژنتیک و شبیه سازی تبرید به حل مسئله در سایزهای بزرگ پرداختیم و نتایج حاصل از آن‌ها را با یکدیگر مقایسه کردیم و در نتیجه به برتری الگوریتم شبیه سازی تبرید نسبت به الگوریتم ژنتیک رسیدیم. تفاصيل المقالة -
حرية الوصول المقاله
3 - Participative Biogeography-Based Optimization
Abbas Salehi Behrooz MasoumiBiogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This popu أکثرBiogeography-Based Optimization (BBO) has recently gained interest of researchers due to its simplicity in implementation, efficiency and existence of very few parameters. The BBO algorithm is a new type of optimization technique based on biogeography concept. This population-based algorithm uses the idea of the migration strategy of animals or other species for solving optimization problems. the original BBO sometimes has not resulted in desirable outcomes. Migration, mutation and elitism are three Principal operators in BBO. The migration operator plays an important role in sharing information among candidate habitats. This paper proposes a novel migration operator in Original BBO. The proposed BBO is named as PBBO and new migration operator is examined over 12 test problems. Also, results are compared with original BBO and others Meta-heuristic algorithms. Results show that PBBO outperforms over basic BBO and other considered variants of BBO. تفاصيل المقالة -
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4 - An Evolutionary Algorithm Based on a Hybrid Multi-Attribute Decision Making Method for the Multi-Mode Multi-Skilled Resource-constrained Project Scheduling Problem
Amir Hossein Hosseinian Vahid BaradaranThis paper addresses the multi-mode multi-skilled resource-constrained project scheduling problem. Activities of real world projects often require more than one skill to be accomplished. Besides, in many real-world situations, the resources are multi-skilled workforces. أکثرThis paper addresses the multi-mode multi-skilled resource-constrained project scheduling problem. Activities of real world projects often require more than one skill to be accomplished. Besides, in many real-world situations, the resources are multi-skilled workforces. In presence of multi-skilled resources, it is required to determine the combination of workforces assigned to each activity. Hence, in this paper, a mixed-integer formulation called the MMSRCPSP is proposed to minimize the completion time of project. Since the MMSRCPSP is strongly NP-hard, a new genetic algorithm is developed to find optimal or near-optimal solutions in a reasonable computation time. The proposed genetic algorithm (PGA) employs two new strategies to explore the solution space in order to find diverse and high-quality individuals. Furthermore, the PGA uses a hybrid multi-attribute decision making (MADM) approach consisting of the Shannon’s entropy method and the VIKOR method to select the candidate individuals for reproduction. The effectiveness of the PGA is evaluated by conducting numerical experiments on several test instances. The outputs of the proposed algorithm is compared to the results obtained by the classical genetic algorithm, harmony search algorithm, and Neurogenetic algorithm. The results show the superiority of the PGA over the other three methods. To test the efficiency of the PGA in finding optimal solutions, the make-span of small size benchmark problems are compared to the optimal solutions obtained by the GAMS software. The outputs show that the proposed genetic algorithm has obtained optimal solutions for 70% of test problems. تفاصيل المقالة -
حرية الوصول المقاله
5 - A Comparison of NSGA II and MOSA for Solving Multi-depots Time-dependent Vehicle Routing Problem with Heterogeneous Fleet
Behrouz Afshar-nadjafi Arian Razmi-faroojiTime-dependent Vehicle Routing Problem is one of the most applicable but least-studied variants of routing and scheduling problems. In this paper, a novel mathematical formulation of time-dependent vehicle routing problems with heterogeneous fleet, hard time widows and أکثرTime-dependent Vehicle Routing Problem is one of the most applicable but least-studied variants of routing and scheduling problems. In this paper, a novel mathematical formulation of time-dependent vehicle routing problems with heterogeneous fleet, hard time widows and multiple depots, is proposed. To deal with the traffic congestions, we also considered that the vehicles are not forced to come back to the depots, from which they were departed. In order to solve our bi-objective formulation, we presented two well-known Meta-heuristic algorithms, namely NSGA II and MOSA and compared their performance based on a set of randomly generated test problems. The results confirm that our MILP model is valid and both NSGA II and MOSA work properly. While NSGA II finds closer solutions to the true Pareto front, MOSA finds evenly- distributed solutions which allows the algorithm to search the space more diversely. تفاصيل المقالة -
حرية الوصول المقاله
6 - Improved teaching–learning-based and JAYA optimization algorithms for solving flexible flow shop scheduling problems
Raviteja Buddala Siba Sankar MahapatraFlexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having ‘g’ operations is performed on ‘g’ operation centres (stages) with each sta أکثرFlexible flow shop (or a hybrid flow shop) scheduling problem is an extension of classical flow shop scheduling problem. In a simple flow shop configuration, a job having ‘g’ operations is performed on ‘g’ operation centres (stages) with each stage having only one machine. If any stage contains more than one machine for providing alternate processing facility, then the problem becomes a flexible flow shop problem (FFSP). FFSP which contains all the complexities involved in a simple flow shop and parallel machine scheduling problems is a well-known NP-hard (Non-deterministic polynomial time) problem. Owing to high computational complexity involved in solving these problems, it is not always possible to obtain an optimal solution in a reasonable computation time. To obtain near-optimal solutions in a reasonable computation time, a large variety of meta-heuristics have been proposed in the past. However, tuning algorithm-specific parameters for solving FFSP is rather tricky and time consuming. To address this limitation, teaching–learning-based optimization (TLBO) and JAYA algorithm are chosen for the study because these are not only recent meta-heuristics but they do not require tuning of algorithm-specific parameters. Although these algorithms seem to be elegant, they lose solution diversity after few iterations and get trapped at the local optima. To alleviate such drawback, a new local search procedure is proposed in this paper to improve the solution quality. Further, mutation strategy (inspired from genetic algorithm) is incorporated in the basic algorithm to maintain solution diversity in the population. Computational experiments have been conducted on standard benchmark problems to calculate makespan and computational time. It is found that the rate of convergence of TLBO is superior to JAYA. From the results, it is found that TLBO and JAYA outperform many algorithms reported in the literature and can be treated as efficient methods for solving the FFSP. تفاصيل المقالة -
حرية الوصول المقاله
7 - Effective heuristics and meta-heuristics for the quadratic assignment problem with tuned parameters and analytical comparisons
Mahdi Bashiri Hossein KarimiQuadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, gree أکثرQuadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic methods and tabu search (TS), simulated annealing, and particle swarm optimization as meta-heuristic methods for the QAP. This research is dedicated to compare the relative percentage deviation of these solution qualities from the best known solution which is introduced in QAPLIB. Furthermore, a tuning method is applied for meta-heuristic parameters. Results indicate that TS is the best in 31% of QAPs, and the IFLS method, which is in the literature, is the best in 58 % of QAPs; these two methods are the same in 11% of test problems. Also, TS has a better computational time among heuristic and meta-heuristic methods. تفاصيل المقالة -
حرية الوصول المقاله
8 - An integrated approach for scheduling flexible job-shop using teaching–learning-based optimization method
Raviteja Buddala Siba Sankar MahapatraIn this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There أکثرIn this paper, teaching–learning-based optimization (TLBO) is proposed to solve flexible job shop scheduling problem (FJSP) based on the integrated approach with an objective to minimize makespan. An FJSP is an extension of basic job-shop scheduling problem. There are two sub problems in FJSP. They are routing problem and sequencing problem. If both the sub problems are solved simultaneously, then the FJSP comes under integrated approach. Otherwise, it becomes a hierarchical approach. Very less research has been done in the past on FJSP problem as it is an NP-hard (non-deterministic polynomial time hard) problem and very difficult to solve till date. Further, very less focus has been given to solve the FJSP using an integrated approach. So an attempt has been made to solve FJSP based on integrated approach using TLBO. Teaching–learning-based optimization is a meta-heuristic algorithm which does not have any algorithm-specific parameters that are to be tuned in comparison to other meta-heuristics. Therefore, it can be considered as an efficient algorithm. As best student of the class is considered as teacher, after few iterations all the students learn and reach the same knowledge level, due to which there is a loss in diversity in the population. So, like many meta-heuristics, TLBO also has a tendency to get trapped at the local optimum. To avoid this limitation, a new local search technique followed by a mutation strategy (from genetic algorithm) is incorporated to TLBO to improve the quality of the solution and to maintain diversity, respectively, in the population. Tests have been carried out on all Kacem’s instances and Brandimarte's data instances to calculate makespan. Results show that TLBO outperformed many other algorithms and can be a competitive method for solving the FJSP. تفاصيل المقالة
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