Performance Evaluation of Vector Evaluated Gravitational Search Algorithms Based on ZDT Test Functions

• Main Authors: Badaruddin, Muhammad, Zuwairie, Ibrahim, Kamarul Hawari, Ghazali, Mohd Riduwan, Ghazali, Kian, Sheng Lim, Sophan Wahyudi, Nawawi, Nor Azlina, Ab. Aziz, Marizan, Mubin, Norrima, Mokhtar
• Format: Article
• Language: English
• Published: United Kingdom Simulation Society 2014
• Subjects: TA Engineering (General). Civil engineering (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/6625/1/fkee-2014-badarudin-Performance_Evaluation.pdf; http://umpir.ump.edu.my/id/eprint/6625/; http://ijssst.info/Vol-15/No-1/paper1.pdf

This paper presents a performance evaluation of Vector Evaluated Gravitational Search Algorithm (VEGSA), namely VEGSA-I and VEGSA-II algorithms, for multi-objective optimization problems. The VEGSA algorithms use a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximization of every objective function is realized by exchanging a variable between populations. Performance evaluation is done based on ZDT test functions, which is a common benchmark problem for multiobjective optimization. The results shows that both VEGSA algorithms are outperformed by other multi-objective optimization algorithms and further enhancements are needed before it can be employed in any application.