A Genetics Based Make span Optimizer with Knowledge Augmented Fuzzy Probabilities
Scheduling of various tasks in the multiprocessor environment has been a source of difficult problems for researchers in the field of computer engineering. The widespread issue of multiprocessor task scheduling can be explained as scheduling tasks onto processors and ordering their execution so that several performance standards can be improved. The nondeterministic polynomial-time (NP) completeness of the task scheduling problem has motivated researchers to suggest a variety of heuristic algorithms that can achieve suboptimal results in a reduced amount of polynomial time. Recently, genetic algorithms (GA) have received a lot of responsiveness as GAs are robust and give assurance of a better-quality solution. The outcomes of the experiments carried out with scheduling problems illustrate that genetic algorithms are superior to heuristic algorithms concerning the quality of their outcomes, but they are noticeably slower than heuristic algorithms concerning running time. To overcome the issues related to heuristic algorithms and genetic algorithms, a new approach is proposed in this research work, which combines a GA with various types of scheduling heuristics. In GA, the crossover and mutation rates are the control parameters that need to be set carefully to get optimal results. Generally, these key factors are left to the GA users, but in this research work, a GA that adopts a fuzzy knowledge-based system to control GA key factors has been proposed. To analyze the efficiency of the proposed method, various tests have been performed by varying the number of generations and by analyzing the variations in average schedule length. Based on the findings, the suggested hybrid algorithm is capable of competing with conventional scheduling algorithms in terms of increased quality of outcomes.