Metaheuristic Start for Gradient based Optimization Algorithms

摘要: Due to the complexity of many real-world optimization problems, better algorithms are always needed. Complex problems that cannot be solved using classical approaches require efficient search metaheuristics find optimal solutions. Recently, metaheuristic global becomes a popular choice and more practical for solving complex loosely defined which otherwise difficult solve by traditional methods. This is due their nature implies discontinuities space, non differentiability objective functions initial feasible But less susceptible discontinuity also bad proposals solution do not affect end solution. Thus, an gauss gradient based can generated with well known population Genetic Algorithm. The continuous genetic algorithm will easily couple optimization, since optimizers use variables. Therefore, Instead starting guess, random finds region optimum value, then optimizer takes over optimum. In this paper hybrid search, followed methods shows great improvements on than separately.