Novel Metaheuristic: Spy Algorithm

We proposed a population-based metaheuristic called the spy algorithm for solving optimization problems and evaluated its performance. The design of our ensures benefit exploration exploitation as well cooperative non-cooperative searches in each iteration. compared with genetic algorithm, improved harmony search, particle swarm on set non-convex functions that focus accuracy, ability detecting many global optimum points, computation time. From statistical analysis results, outperformed other algorithms. had best accuracy detected more points within less time, indicating is robust faster then these