Search Manager: A Framework For Hybridizing Different Search Strategies

In the last decade, many of the metaheuristic search methods have been proposed for solving tough optimization problems. Each of these algorithms uses its own learn-by-example mechanism in terms of "movement strategy" to evolve the candidate solutions. In this paper, a framework, called Search Manager, is proposed for hybridizing different learn-by-example methods in one algorithm, which is inspired by the organizational management system in which managers change their management method by viewing performance reduction in their managerial organization. The proposed framework is verified using standard benchmark functions and real-world optimization problems. Further, it is compared with some well-known heuristic search methods. The obtained results indicate not only the optimization capability of the proposed framework, but also its ability to obtain accurate solutions and to achieve higher convergence precision.