Performance Evaluation of Vector Evaluated Gravitational Search Algorithm II
Muhammad Badaruddin, Ibrahim Zuwairie, Kamarul Hawari Ghazali, Ghazali Mohd Riduwan, Muhammad Salihin Saealal, Lim Kian Sheng, Nawawi Sophan Wahyudi, Ab. Aziz Nor Azlina, Marizan Binti Mubin, Norrima Binti Mokhtar · Frontiers in artificial intelligence and applications · 2014
This paper presents a performance evaluation of a novel Vector Evaluated Gravitational Search Algorithm II (VEGSAII) for multi-objective optimization problems. The VEGSAII algorithm uses 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. The results shows that the VEGSA is outperformed by other multi-objective optimization algorithms and further enhancements are needed before it can be employed in any application.