خوارزمية نظام مستعمرة النمل المحسنة لحل مسألة توجيه المركبة
Abstract
ندرس في هذا البحث إمكانية المساهمة في حلّ مسألة توجيه المركبة Vehicle Routing Problem (VRP) باستخدام خوارزمية نظام مستعمرة النمل المحسنة Improved Ant Colony System (IACS) ، وهي واحدة من مشاكل الأمثلية , التي أخذت الكثير من الاهتمام في الوقت الحاضر بسبب تطبيقاتها ذات الطابع اليومي ، و هي مشكلة تعقيدها الخوارزمي من النوع NP-hard , ولا توجد حتى الآن خوارزمية تقدم لنا الحل الأمثل لهذه المشكلة بسبب تعقيد الزمن متعدد الحدود ، فكل الخوارزميات المستخدمة تعطي حلولاً قريبة من الحل الأمثل . إن خوارزمية نظام مستعمرة النمل المحسنة المقترحة تعتمد على خوارزمية نظام مستعمرة النمل التي تمتلك قاعدة انتقال جديدة ، وقاعدة تحديث فورمون جديدة ، ونهج بحث محلي متنوع . تمت مقارنة النتائج التطبيقية للخوارزمية المقترحة مع نتائج اختبارات قياسية معروفة وموثقة , إذ تظهر النتائج بأنّ الخوارزمية المحسنة المقترحة تنتج حلولاً أفضل من خوارزميات مستعمرات النمل الأخرى و خوارزميات ما وراء الإرشادية الأخرى , من حيث الجودة ( زمن التنفيذ وعدد الحلول الجيدة ) . الكلمات المفتاحية: مسألة توجيه المركبة- خوارزمية البحث المحلي 2-opt - خوارزمية مستعمرة النمل - خوارزمية نظام مستعمرة النمل المحسنة . مجلة جامعة تشرين للبحوث والدراسات العلمية - سلسلة العلوم الأساسية المجلد (36) العدد (5) 2014 Tishreen University Journal for Research and Scientific Studies - Basic Sciences Series Vol. (36) No. (5) 2014 Improved Ant Colony System Algorithm To Solve The Vehicle Routing Problem Waseem Habib Bilal * (Received 31 / 8 / 2014. Accepted 14 / 10 /2014) ABSTRACT We study in this paper the possibility of contribution in solving the vehicle routing problem (VRP) by using the improved ant colony system ( IACS) , which is one of the optimization problems that, because of its Real Life applications, has attracted a lot of attention at the present time. It is a problem of the NP-hard type. However, because of the complication of polynomial time there is still no algorithm providing us with the optimal solution of this problem. All the used algorithms give solutions that are close to the optimal one . We present the improved ant colony system algorithm that, based on ant colony system algorithm, possesses a new state transition rule, a new pheromone updating rule and diverse local search approaches . The experimental results of the proposed ( IACS) algorithm compared with the results of well-known standard tests show that our IACS yields better solutions than the other ant algorithms in the literature and is competitive with other meta-heuristic approaches in terms of quality(run time and number of good solutions ).Downloads
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