meta-heuristic algorithm
در نشریات گروه فناوری اطلاعات-
Journal of Advances in Computer Engineering and Technology, Volume:7 Issue: 1, Winter 2021, PP 81 -92In recent years, social networks' growth has led to an increase in these networks' content. Therefore, text mining methods became important. As part of text mining, Sentiment analysis means finding the author's perspective on a particular topic. Social networks allow users to express their opinions and use others' opinions in other people's opinions to make decisions. Since the comments are in the form of text and reading them is time-consuming. Therefore, it is essential to provide methods that can provide us with this knowledge usefully. Black Widow Optimization (BWO) is inspired by black widow spiders' unique mating behavior. This method involves an exclusive stage, namely, cannibalism. For this reason, at this stage, species with an inappropriate evaluation function are removed from the circle, thus leading to premature convergence. In this paper, we first introduced the BWO algorithm into a binary algorithm to solving discrete problems. Then, to reach the optimal answer quickly, we base its inputs on the opposition. Finally, to use the algorithm in the property selection problem, which is a multi-objective problem, we convert the algorithm into a multi-objective algorithm. The 23 well-known functions were evaluated to evaluate the performance of the proposed method, and good results were obtained. Also, in evaluating the practical example, the proposed method was applied to several emotion datasets, and the results indicate that the proposed method works very well in the psychology of texts.Keywords: text psychology, Meta-Heuristic Algorithm, Feature Selection, black widow optimization algorithm
-
حجم بسیار و روبه رشد اطلاعات بر روی اینترنت، فرآیند تصمیم گیری و انتخاب اطلاعات، داده یا کالاهای موردنیاز را، برای بسیاری از کاربران وب دشوار کرده است. سامانه های پیشنهاددهنده (توصیه گر)1 ، باهدف رفع این چالش به وجود آمده اند و تلاش می کنند تا از میان حجم عظیم اطلاعات، اطلاعات خاص و مفید را با توجه به علاقه و سلیقه کاربر و تجربیات کاربران گذشته به وی پیشنهاد دهند. تاکنون سامانه های پیشنهاددهنده زیادی در زمینه های کاربردی متنوع ازجمله فیلم، موسیقی، کتاب و... ایجادشده اند. انتخاب یک سفر مناسب، پیشنهاد هتل و... با توجه به بودجه ی فرد، معمولا سختی ها و نگرانی های زیادی را برای کاربران به همراه دارد و عموما با صرف زمان و انرژی زیادی انجام می گیرد. لذا در این مقاله یک سیستم پیشنهاددهنده سفر و هتل ارایه می شود که از ترکیب روش فیلترهای مختلف ساخته شده است تا دقت آن دوچندان شود. این سیستم برای ارایه پیشنهادهای نهایی خود، سلایق کاربر جاری، کیفیت مجموعه های خدمات دهنده و تجربیات گذشته کاربران مشابه با کاربر جاری را مدنظر قرار داده و بدین ترتیب علاوه بر ارایه پیشنهادهای دقیق تر، مشکل شروع سرد2 را که معمولا برای کاربران جدید بروز می کند که در سیستم ثبت نام می کنند و سیستم هیچ اطلاعاتی از نظرات یا علایق کاربر ندارد، نیز برطرف می نماید. در چنین شرایطی، سامانه ها معمولا از یادگیری فعال3 یا استفاده از ویژگی های شخصیتی کاربر، برای حل مشکل استفاده می کنند.
کلید واژگان: سیستم پیشنهاددهنده، فیلترینگ ترکیبی، الگوریتم خفاش، فیلترینگ مشارکتی، فیلترینگ مبتنی بر محتویThe growing amount of information on the internet has made it difficult for many web users to make the decision-making and selection of information, data or goods. Recommended systems are designed to address this challenge and try to offer specific and useful information with respect to user tastes and past user experiences. So far, many offering systems have been developed in a variety of applications including movies, music, books, hotels etc. Choosing the right trip, the hotel proposal and so on, with regard to the individual's budget usually have a lot of difficulties and concerns for users and generally takes a lot of time and energy. In this paper, a travel and hotel recommendation system is developed which is constructed from combination of different filtering methods to maximize accuracy. The system is considering the current user's preferences, the quality of the service packages and past experiences of the same users with the current user in order to providing more accurate suggestions. It also eliminates the cold start problem.
Keywords: Recommended System, Bat Algorithm, Hybrid Filtering, Collaboration Filtering, Content-Based Filtering, Meta-Heuristic Algorithm -
This study concerns the development of a nonlinear programming model capable of solving an adapted version of a single-objective nonlinear problem. The original problem was adapted via the inclusion of an additional constraint and term in the objective function. The resultant aim is twofold: to optimize a three-level supply chain so as to decrease objective costs (such as shortage periods) while simultaneously increasing customer service levels. Demand is random and the inventory control system continuous. Lost sales due to urgent demand are assumed. After evaluating the formulated mathematical model, a metaheuristic algorithm is developed capable of determining the number of open distribution centers and allocating retailers to these centers. Experiments to evaluate the proposed method's performance are conducted on small to medium-sized problems. Results are compared against those of e-constraint and None Dominated Sorting Genetic Algoritms (NSGA2) (whose parameters are adjusted using the Taguchi method). Final results indicate the superiority of the proposed metaheuristic in comparison to other, competing approaches.
Keywords: Nonlinear Programming, dual-objective function, Taguchi Method, Meta-heuristic Algorithm, Supply Chain -
We present a bi-objective model for a green truck scheduling and routing problem at a cross-docking system. This model determines three key decisions at the cross dock: (1) defining a sequence and schedule of inbound trucks at the receiving door, (2) specifying a sequence and a schedule of outbound trucks at the shipping door, and (3) determining the routes of the outbound truck while serving customers. The first objective function is related to responsiveness of the network that minimizes time window violations and the second objective function minimizes total fuel consumption of trucks in order to consider the environmental factor of the network. Also, a learning effect is considered in loading and unloading process times. To solve the bi-objective model, an archived multi-objective simulated annealing (AMOSA) is used and modified. Finally, a number of test problems are solved and the efficiency of the proposed AMOSA is compared with the e-constraint method.
Keywords: Green truck routing, scheduling, Cross docking, Learning effect, Meta-heuristic algorithm
- نتایج بر اساس تاریخ انتشار مرتب شدهاند.
- کلیدواژه مورد نظر شما تنها در فیلد کلیدواژگان مقالات جستجو شدهاست. به منظور حذف نتایج غیر مرتبط، جستجو تنها در مقالات مجلاتی انجام شده که با مجله ماخذ هم موضوع هستند.
- در صورتی که میخواهید جستجو را در همه موضوعات و با شرایط دیگر تکرار کنید به صفحه جستجوی پیشرفته مجلات مراجعه کنید.