Improved strategies of multi-objective differential evolution (MODE) for multi-objective optimization

Ashish M. Gujarathi*, B. V. Babu

*المؤلف المقابل لهذا العمل

نتاج البحث: Conference contribution

11 اقتباسات (Scopus)

ملخص

Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE is used to compare the performance metrics (such as convergence and divergence) with other evolutionary algorithms from the literature. The Pareto optimal solutions are obtained for benchmark test functions and are compared using several strategies of MODE. Improved strategies of MODE show a competitive performance when compared with other evolutionary multi-objective optimization algorithms (EMOAs).

اللغة الأصليةEnglish
عنوان منشور المضيفProceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009
الصفحات933-948
عدد الصفحات16
حالة النشرPublished - 2009
منشور خارجيًانعم
الحدث4th Indian International Conference on Artificial Intelligence, IICAI 2009 - Tumkur, India
المدة: ديسمبر ١٦ ٢٠٠٩ديسمبر ١٨ ٢٠٠٩

سلسلة المنشورات

الاسمProceedings of the 4th Indian International Conference on Artificial Intelligence, IICAI 2009

Other

Other4th Indian International Conference on Artificial Intelligence, IICAI 2009
الدولة/الإقليمIndia
المدينةTumkur
المدة١٢/١٦/٠٩١٢/١٨/٠٩

ASJC Scopus subject areas

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