Performance Evaluation of Vector Evaluated Gravitational Search Algorithms Based on ZDT Test Functions
Badaruddin Muhammad, Zuwairie Ibrahim, Kamarul Hawari Ghazali, Mohd Riduwan Ghazali, Nor Azlina Ab. Aziz, Kian Sheng Lim, Sophan Wahyudi Nawawi, Marizan Binti Mubin, Norrima Binti Mokhtar · International Journal of Simulation Systems Science & Technology · 2020
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 multi-objective 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.