bat algorithm
در نشریات گروه فناوری اطلاعات-
An optimal scheduling reduces the completion time (makespan) of jobs, and finally increase profits in today's competitive environment. Open shop scheduling problem (OSSP) involves a set of activities that should be performed on a limited set of machines. The aim of scheduling in an open shop is to provide a schedule for the execution of the entire operation so that the completion time of all operations is reduced. OSSP has a large solution space and belongs to NP-hard problems. So far, various algorithms are developed for OSSP. In this paper we propose a new optimization algorithm named Bat Algorithm (BA) for solving OSSP which has a relative advantage over other algorithms. Experimental results show that the proposed algorithm has high performance and increases the reliability and efficiency of the system.
Keywords: Open Shop, Bat Algorithm, Scheduling, Completion Time of Jobs -
Journal of Advances in Computer Engineering and Technology, Volume:6 Issue: 4, Autumn 2020, PP 227 -238
Clustering is a method of data analysis and one of the important methods in data mining that has been considered by researchers in many fields as well as in many disciplines. In this paper, we propose combining WOA with BA for data clustering. To assess the efficiency of the proposed method, it has been applied in data clustering. In the proposed method, first, by examining BA thoroughly, the weaknesses of this algorithm in exploitation and exploration are identified. The proposed method focuses on improving BA exploitation. Therefore, in the proposed method, instead of the random selection step, one solution is selected from the best solutions, and some of the dimensions of the position vector in BA are replaced We change some of the best solutions with the step of reducing the encircled mechanism and updating the WOA spiral, and finally, after selecting the best exploitation between the two stages of WOA exploitation and BA exploitation, the desired changes are applied on solutions. We evaluate the performance of the proposed method in comparison with other meta-heuristic algorithms in the data clustering discussion using six datasets. The results of these experiments show that the proposed method is statistically much better than the standard BA and also the proposed method is better than the WOA. Overall, the proposed method was more robust and better than the Harmony Search Algorithm (HAS), Artificial Bee Colony (ABC), WOA and BA.
Keywords: Bat algorithm, Whale Optimization Algorithm, Data clustering, Optimization -
Journal of Advances in Computer Engineering and Technology, Volume:6 Issue: 3, Summer 2020, PP 119 -130Metaheuristic algorithms are used to solve NP-hard optimization problems. These algorithms have two main components, i.e. exploration and exploitation, and try to strike a balance between exploration and exploitation to achieve the best possible near-optimal solution. The bat algorithm is one of the metaheuristic algorithms with poor exploration and exploitation. In this paper, exploration and exploitation processes of Gray Wolf Optimizer (GWO) algorithm are applied to some of the solutions produced by the bat algorithm. Therefore, part of the population of the bat algorithm is changed by two processes (i.e. exploration and exploitation) of GWO; the new population enters the bat algorithm population when its result is better than that of the exploitation and exploration operators of the bat algorithm. Thereby, better new solutions are introduced into the bat algorithm at each step. In this paper, 20 mathematic benchmark functions are used to evaluate and compare the proposed method. The simulation results show that the proposed method outperforms the bat algorithm and other metaheuristic algorithms in most implementations and has a high performance.Keywords: Bat algorithm, Gray wolf optimizer, Continuous Problems, Optimization
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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 -
Abstract The rapid growth in demand for computing power has led to a shift towards a cloud-based model relying on virtual data centers. In order to meet the demand of cloud computing clients, cloud service providers need to maintain service quality parameters at optimum levels. This paper presents a hybrid algorithm dubbed PSOBAT-Greedy, which is expected to reduce cost and time while enhancing the efficiency of resources. The main idea behind the newly proposed algorithm is to find an optimal weight for local and global search using Range and Tuning functions as an important solution overcoming various problems in task scheduling and provide the right response within an acceptable time. The new hybrid algorithm is less time-consuming and costly than the other two algorithms. As compared to particle swarm optimization (PSO) algorithm and combined particle swarm optimization and bat algorithm (PSOBat), resource efficiency improves by 15% and 5%, respectively. Keywords: Quality of Service, Cloud Computing, Particle Swarm Optimization, Bat Algorithm
Keywords: Quality of Service, cloud computing, particle swarm optimization, Bat algorithm -
The complex networks are widely used to demonstrate effective systems in the fields of biology and sociology. One of the most significant kinds of complex networks is social networks. With the growing use of such networks in our daily habits, the discovery of the hidden social structures in these networks is extremely valuable because of the perception and exploitation of their secret knowledge. The community structure is a great topological property of social networks, and the process to detect this structure is a challenging problem. In this paper, a new approach is proposed to detect non-overlapping community structure. The approach is based on multi-agents and the bat algorithm. The objective is to optimize the amount of modularity, which is one of the primary criteria for determining the quality of the detected communities. The results of the experiments show the proposed approach performs better than existing methods in terms of modularity.Keywords: Social networks, Multi-agent systems, Swarm intelligence, Bat algorithm, Community Detection, Modularity
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Because of the low accuracy of estimation and uncertainty of the techniques used in the past to Software Cost Estimation (SCE), software producers face a high risk in practice with regards to software projects and they often fail in such projects. Thus, SCE as a complex issue in software engineering requires new solutions, and researchers make an effort to make use of Meta-heuristic algorithms to solve this complicated and sensitive issue. In this paper, we propose a new method by improving Genetic Algorithm (GA) with Bat Algorithm (BA), considering the effect of qualitative factors and false variables in the relations concerning the total estimation of the cost. The proposed method was investigated and assessed on four various datasets based on seven criteria. The experimental results indicate that the proposed method mainly improves accuracy in the SCE and it reduced errors' value in comparison with other models. In the results obtained, Mean Magnitude of Relative Error (MMRE) on NASA60, NASA63, NASA93, and KEMERER is 17.91, 34.80, 41.97, and 95.86, respectively. In addition, the experimental results on datasets show that the proposed method significantly outperforms GA and BA and also many other recent SCE methods.Keywords: Software Cost Estimation, Bat algorithm, Genetic Algorithm, COCOMO model, Optimization
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International Journal of Academic Research in Computer Engineering, Volume:1 Issue: 1, Sep 2016, PP 45 -52Several software projects are developed and produced in software companies annually. Rapid environmental and technological changes, cost constraints, mismanagement, lack of skills of the project managers and inaccurate estimation cause many of these projects to fail in practice. In large software projects, software cost estimation has always been one of the main problems for the project manager and project-development team. The main SCE criterion is to determine the effort required to complete the project. The effort needed is usually determined according to people / month. There is a direct relationship between the amount of effort and the time required to complete a project. And the time can be used to estimate costs. Therefore, the main step in SCE is determining the amount of effort required to complete projects. COCOMO model is the algorithmic model for SCE. In COCOMO model, the emphasis is on the effort coefficients for better performance. In order to measure the relationship between the effort and time, i.e., the relation between the number of code lines and effort coefficients, COCOMO uses some linear formula that utilizes data from NASA software projects. In this paper, we have used bat algorithm to accurately estimate the effort coefficients and to reduce MMRE. Evaluation has been conducted on NASA60 dataset; the results show that BA has much less error compared with COCOMO model.Keywords: software cost estimation, COCOMO model, bat algorithm, NASA60 dataset
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