Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution

Hybridization of two or more variants algorithms is the recent trend research field. With help a hybridized algorithm we try to find out better optimal solution and solve various optimization applications. In this paper, new approach Advancement on Grey Wolf Optimization with Fitness Based Self Adaptive Differential Evolution (AGWO-FSADE) proposed. populations are calculated using self adaptive strategy FSADE updated by GWO algorithm. balance convergence diversity capability due fine tuning Crossover Probability CR Scale Factor F therefore, in large step size very less chance skip actual solutions. The performance AGWO-FSADE measured 19 Benchmark functions compared classic GWO, ABC PSOGWO results accurate these functions. Keywords - nature inspired algorithm, technique, grey wolf optimizer, fitness based differential evolution, technique