دانلود مقاله ISI انگلیسی شماره 52401
عنوان فارسی مقاله

نرم افزار بهینه سازی مورد نیاز با استفاده از الگوریتم تکاملی هوش ازدحامی چندهدفه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
52401 2015 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Software requirement optimization using a multiobjective swarm intelligence evolutionary algorithm
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Knowledge-Based Systems, Volume 83, July 2015, Pages 105–115

کلمات کلیدی
مشکل انتشار بعدی - الگوریتم تکاملی چند هدفه - نرم افزار انتخاب مورد نیاز؛ جستجو بر اساس مهندسی نرم افزار - هوش ازدحامی - کلنی زنبور عسل مصنوعی
پیش نمایش مقاله
پیش نمایش مقاله نرم افزار بهینه سازی مورد نیاز با استفاده از الگوریتم تکاملی هوش ازدحامی چندهدفه

چکیده انگلیسی

The selection of the new requirements which should be included in the development of the release of a software product is an important issue for software companies. This problem is known in the literature as the Next Release Problem (NRP). It is an NP-hard problem which simultaneously addresses two apparently contradictory objectives: the total cost of including the selected requirements in the next release of the software package, and the overall satisfaction of a set of customers who have different opinions about the priorities which should be given to the requirements, and also have different levels of importance within the company. Moreover, in the case of managing real instances of the problem, the proposed solutions have to satisfy certain interaction constraints which arise among some requirements. In this paper, the NRP is formulated as a multiobjective optimization problem with two objectives (cost and satisfaction) and three constraints (types of interactions). A multiobjective swarm intelligence metaheuristic is proposed to solve two real instances generated from data provided by experts. Analysis of the results showed that the proposed algorithm can efficiently generate high quality solutions. These were evaluated by comparing them with different proposals (in terms of multiobjective metrics). The results generated by the present approach surpass those generated in other relevant work in the literature (e.g. our technique can obtain a HV of over 60% for the most complex dataset managed, while the other approaches published cannot obtain an HV of more than 40% for the same dataset).

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