Global best Local Neighbourhood in Particle Swarm Optimization for Big Data Environment

• Main Authors: Zalili, Musa, M. N. M., Kahar, Mohd Hafiz, Mohd Hassin, Rohani, Abu Bakar
• Format: Conference or Workshop Item
• Language: English
• Published: Faculty of Computer System & Software Engineering 2017
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/19973/1/Global%20best%20Local%20Neighbourhood%20in%20Particle%20Swarm.pdf; http://umpir.ump.edu.my/id/eprint/19973/; http://icsecs.ump.edu.my/index.php/en/program/program-book
• Institution: Universiti Malaysia Pahang

The conventional Particle Swarm Optimization (PSO) still has weaknesses in finding optimal solutions especially in a dynamic environment. Therefore, in this paper we proposed a Global best Local Neighborhood in particle swarm optimization in order to solve the optimum solution in dynamic environment. Based on the experimental results of 50 datasets, show that GbLN-PSO has the ability to find the quality solution in dynamic environment.