نظام مستعمرة النمل المحسن والهجين للمساهمة في حل مسألة توجيه المركبة مع نوافذ زمنية
Abstract
ندرس في هذا البحث إمكانية المساهمة في حل مسألة توجيه المركبة مع نوافذ زمنية ، و هي واحدة من مشاكل الأمثلية من النوع NP-hard حيث أخذت كثير من اهتمام الباحثين في الوقت الحاضر بسبب تطبيقاتها ذات الطابع اليومي ، إذ لا توجد حتى الآن خوارزمية تقدم الحل الأمثل لهذه المشكلة بسبب تعقيد زمن كثيرة الحدود وهذا يعني أن زمن الحل لمسألة توجيه المركبة مع نوافذ زمنية ينمو باطراد مع زيادة عدد العقد ، و كل الخوارزميات المستخدمة تعطي حلولاً تقريبية . سنعرض في بحثنا خوارزمية نظام مستعمرة النمل المحسن القادرة على استكشاف مناطق بحث متنوعة في فضاء الحل ، وخوارزمية محاكاة التعدين ، و هي تقنية بحث محلي يتم تطبيقها بنجاح في العديد من مسائل NP-hard . نقدم أيضاً خوارزمية تدعى بالهجينة تعتمد على مبدأ الدمج بين خوارزمية نظام النمل المحسن وخوارزمية محاكاة التعدين ، و مقارنة الحل الناتج عن هذا النهج الهجين مع نتائج تجارب قياسية لاختبار فعالية النهج المقدم . In this research, we are studying the possibility of contribution in solving the Vehicle Routing Problem With Time Windows(VRPWTW), that is one of the optimization problems of the NP-hard type. This problem has attracted a lot of attention at the present time because of its real life applications. However, there is still no algorithm that provides us with the perfect solution to this problem because of the complexity of polynomial time. This means that the time of the solution to the Vehicle VRPWTW is growing steadily with the increase in the number of nodes .All the used algorithms have given solutions that are close to the optimal one . We'll introduce two algorithms , the first is Improved Ant Colony System algorithm (IACS) that is capable of searching multiple search areas simultaneously in the solution space is good in diversification ,and the second Simulated Annealing algorithm (SA) is a local search technique that has been successfully applied to many NP-hard problems. Moreover, we will present the In this research Hybrid algorithm (HA) Hybrid Algorithm provided (IACS-SA) that integrate between improved ant algorithm and Simulated Annealing algorithm . We will known standard tests are given to demonstrate the applicability and efficiency of the presented approach and comparisons with other available results are presented.Downloads
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