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نتیجه جستجو - Differential evolution algorithm

تعداد مقالات یافته شده: 8
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 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 Success history applied to expert system for underwater glider path planning using differential evolution
تاریخچه موفقیت برای برنامه ریزی مسیر گلایدر در زیر آب با استفاده از تکامل افتراقی برای سیستم خبره کاربردی-2019
This paper presents an application of a recently well performing evolutionary algorithm for continuous numerical optimization, Success-History Based Adaptive Differential Evolution Algorithm (SHADE) includ- ing Linear population size reduction (L-SHADE), to an expert system for underwater glider path planning (UGPP). The proposed algorithm is compared to other similar algorithms and also to results from lit- erature. The motivation of this work is to provide an alternative to the current glider mission control systems, that are based mostly on multidisciplinary human-expert teams from robotic and oceanographic areas. Initially configured as a decision-support expert system, the natural evolution of the tool is target- ing higher autonomy levels. To assess the performance of the applied optimizers, the test functions for UGPP are utilized as defined in literature, which simulate real-life oceanic mission scenarios. Based on these test functions, in this paper, the performance of the proposed application of L-SHADE to UGPP is aggregated using statistical analyis. The depicted fitness convergence graphs, final obtained fitness plots, trajectories drawn, and per-scenario analysis show that the new proposed algorithm yields stable and competitive output trajectories. Over the set of benchmark missions, the newly obtained results with a configured L-SHADE outperforms ex- isting literature results in UGPP and ranks best over the compared algorithms. Moreover, some additional previously applied algorithms have been reconfigured to yield improved performance. Thereby, this new application of evolutionary algorithms to UGPP contributes significantly to the capacity of the decision- makers, when they use the improved UGPP expert system yielding better trajectories.
Keywords: Differential evolution | Linear population size reduction | Success-history based parameter adaptation | L-SHADE | Underwater glider path planning
مقاله انگلیسی
4 Hierarchical differential evolution algorithm combined with multi-cross operation
الگوریتم تکامل تفاضلی سلسله مراتبی همراه با عمل چند تقابلی-2019
In expert systems, complex optimization problems are always characterized by nonlinearity, nonconvex- ity, multi-modality, discontinuity, and high dimensionality. Although classical optimization algorithms are mature, they readily fall into a local optimum. The differential evolution (DE) algorithm has been suc- cessfully applied to solve numerous problems with expert systems. However, balancing the global and local search capabilities of the DE algorithm remains an open issue and has attracted significant research attention. Thus, a hierarchical heterogeneous DE algorithm that incorporates multi-cross operation (MCO) is proposed in this article. In the proposed algorithm, success-history-based adaptive DE (SHADE) is im- plemented in the bottom layer, while MCO is implemented in the top layer. The MCO search is based on the SHADE results, but its search results do not affect the bottom layer. First-order stability analyses con- ducted for the presented MCO showed that the individual positions are expected to converge at a fixed point in the search space. The accuracy and convergence speed of the proposed algorithm were also ex- perimentally compared with those of eight other advanced particle swarm optimization techniques and DE variants using benchmark functions from CEC2017. The proposed algorithm yielded better solution ac- curacy for 30- and 50-dimensional problems than the other variants, and although it did not provide the fastest convergence for all of the functions, it ranked among the top three for the unimodal and simple multimodal functions and achieved fast convergence for the other functions.
Keywords: Differential evolution | Particle swarm optimization | Hierarchical structure | Multi-cross operation
مقاله انگلیسی
5 Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index
پیش بینی حجم گردشگر موثر پشتیبانی شده توسط PCA و BPNN بهبود یافته با استفاده از شاخص بایدو-2018
The precise forecasting of tourist volume is a very challenging task. This paper aims to propose an effective model named PCA-ADE-BPNN for forecasting tourist volume based on Baidu index. The principal component analysis (PCA), a dimensional reduction, is employed to decorrelate the input data before training a back propagation neural network (BPNN) architecture, and the adaptive differential evolution algorithm (ADE) is for getting global optimization of BP networks weight values and threshold values to enhance the forecasting performance of BPNN. The PCA-ADE-BPNN model is a new combination of a dimensional reduction algorithm, an optimization algorithm, and a neural network. The validity of this model is demonstrated by conducting case studies of Beijing City and Hainan Province, China. The results indicate the proposed PCA-ADE-BPNN always outperforms other models in terms of forecasting accuracies. Therefore, the proposed PCA-ADE-BPNN is a potential candidate for the effective forecasting of tourist volume.
keywords: Tourist volume forecasting |Principal component analysis |Baidu index |Back-propagation neural network |Adaptive differential evolution
مقاله انگلیسی
6 Differential Big Bang: Big Crunch algorithm for construction-engineering design optimization
Difference Big Bang: الگوریتم بزرگ Crunch برای بهینه سازی طراحی مهندسی ساخت و ساز-2018
The present study proposes the Differential Big Bang - Big Crunch (DBB-BC) algorithm. This new hybrid me taheuristic is designed to enhance the performance of the Big Bang-Big Crunch (BB-BC) algorithm. DBB-BC uses collaborative-combination hybridization to combine the BB-BC algorithm, Differential Evolution algorithm, and Neighborhood Search in order to improve the exploration and exploitation capabilities of the original BB-BC in finding global solutions. Subsequently, a number of unconstrained mathematical benchmark problems and seven practical design problems from the construction-engineering field are used to investigate the effectiveness and efficiency of DBB-BC. The results of this investigation confirm that the DBB-BC performs significantly better than the other algorithms that were tested in terms of optimal solution (efficacy) and required function evaluations (efficiency).
Keywords: Metaheuristic ، Big Bang–Big Crunch ، Benchmark functions ، Hybrid method ، Engineering design problems
مقاله انگلیسی
7 یک طرح جدید برای تشخیص خطا با استفاده از الگوریتم های خوشه بندی فازی
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 44 - تعداد صفحات فایل doc فارسی: 52
در این مقاله روشی برای طراحی سیستم های تشخیص خطای مبتنی بر داده محوری با استفاده از تکنیک های خوشه بندی فازی ارائه می شود، در این طرح، در بخش اول فرآیند دسته بندی، داده ها پیش پردازش می شوند تا داده های پرت حذف شده و درهم ریختگی کاهش یابد. بدین منظور، الگوریتم های میانگین های C تراکم گرا و خوشه بندی نویز، استفاده شدند. ثانیاً، الگوریتم میانگین های C فازی کرنل برای دستیابی به تفکیک پذیری بیشتر در میان کلاس ها و کاهش خطاهای دسته بندی مورد استفاده قرار گرفتند. نهایتاً یک گام سوم برای بهینه سازی دو پارامتر مورد استفاده در این الگوریتم ها در مرحله ی آموزش با استفاده از الگوریتم تکاملی تفاضلی ارائه شد. روش پیشنهادی با استفاده از راکتور مخزنی همزن دار پیوسته ی غیرخطی اعتبارسنجی شد. نتایج به دست آمده بیانگر عملی بودن طرح پیشنهادی است.
کلیدواژه ها: تشخیص خطا | الگوریتم های خوشه بندی فازی | الگوریتم های کرنل فازی | پارامترهای بهینه
مقاله ترجمه شده
8 A bi-level programming approach to the decision problems in a vendor-buyer eco-friendly supply chain
یک رویکرد برنامه نویسی سطحی در مورد مشکلات تصمیم گیری در یک زنجیره عرضه سازگار با محیط زیست فروشندگان و خریدار-2017
This paper focuses on a multi-product vendor-buyer supply chain considering environmental factors in the product manufacturing process. We assume that the unit price charged by the buyer, the unit adver tising expenditure and the vendor’s unit environmental improvement influence the demand of the pro duct being sold. The relationship between the vendor and the buyer is modeled by a non-cooperative game based on Stackelberg strategy framework under two power scenarios including vendor-leader model and buyer-leader model. A bi-level programming approach is applied to determine the optimal selling prices, advertising expenditures, wholesale prices, vendor’s environmental improvements and ordering policies of the vendor and the buyer. Then, a modified differential evolution algorithm is pro posed for solving the models. Numerical experiments carried out in this paper, including sensitivity anal ysis for some key parameters, evaluate the effectiveness of the models and compare the results between the different models considered. Several research findings have been obtained and explained.
Keywords: Vendor-buyer eco-friendly supply chain | Bi-level programming |Environmental protection |Pricing and ordering |Differential evolution algorithm
مقاله انگلیسی
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