Abstract: In this study attempt is taken to improve the performance of the fuzzy logic controller employed in finding solutions for the parallel job scheduling problems. The performance of the fuzzy logic controller depends on its knowledge base which consists of data base and the rule base. This paper proposes novel hybrid optimization techniques for performance improvement of the fuzzy logic controller by optimizing its knowledge base and a comparative analysis of the proposed optimization techniques are presented based on the computed simulation results. Scheduling of parallel jobs is one of the most challenging aspects with respect to analyzing the performance of the parallel system process. In a parallel system, if the application contains processes which are not co-scheduled together, then the performance of the parallel system starts degrading. Agile Scheduling algorithm classifies the grain sizes in a detailed manner for the real workloads and schedules them in an effective manner. Using the results obtained from the agile scheduling algorithm, a rule based system is generated which classifies all the scheduling states and assigns the appropriate scheduling class for the parallel jobs. The rule system is coded with the Mamdani Fuzzy model and to improve the modeled Fuzzy Logic Controller (FLC), the proposed optimization techniques are applied over the knowledge base of the fuzzy logic controller which involves optimization of both the database and rule base simultaneously. This paper employs optimization algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and River Formation Dynamics (RFD) for fuzzy logic controller design of parallel systems and as well hybrid technique along with the tabu search algorithm. Simulation results prove the effectiveness of the developed algorithms for fuzzy logic controller design of parallel job shop scheduling problems.