A New Fuzzy based Evolutionary Optimization for Job Scheduling with TLBO
Ch. Srinivasa Rao, B. Raveendra Babu · 2014
Grid computing is a frame work that shares data, storage, computing across heterogeneous and distributed locations to meet the current and growing computational demands. Thispaper proposes a novel evolutionary optimization approachusing fuzzy Teaching Learning Based Optimization (TLBO) for resource scheduling in computational grids. The fuzzy TLBOgeneratesan efficient schedule to complete the jobs within a minimum period of time. The performance of the proposed fuzzy based TLBOalgorithm evaluate with various other nature heuristic algorithms, GeneticAlgorithm (GA), Simulated Annealing (SA), Differential Evolution, and fuzzy PSO. Experimental results have shown the efficiency and prominence of new proposed algorithm in producing optimal solutions for the selected benchmark job scheduling problems compared to other algorithms.