A Modified Genetic Learning PSO for Task Matching in Grid Environment
Eid Mohammad Albalawi, Parimala Thulasiraman, Ruppa K. Thulasiram · 2018
This paper introduces a modified genetic learning PSO (MGLPSO) algorithm for task matching problem in grid systems. MGLPSO incorporates genetic operators to create candidate solutions (exemplars) to guide the particles in the search space. Results show that MGLPSO is more efficient and effective in handling large-scale problem instances. Compared with PSO and Genetic Learning Particle Swarm Optimization (GLPSO), MGLPSO minimizes the makespan by 52% and 43%, respectively. Further, MGLPSO requires few iterations to obtain high quality solutions. Results also reveal that MGLPSO can achieve a good diversity while maintaining exploration and exploitation search.