Search Economics: A Solution Space and Computing Resource Aware Search Method

Although most metaheuristic algorithms claimed that they have a chance to find the optimal solution if given sufficient computation time. In fact, a metaheuristic algorithm may search the same region or particular solutions for a long time when the search process is approaching the convergence state. The question that arises now is, how to invest the limited computing resource to search for the "solutions on the right region" instead of wasting time to search for the irrelevant solutions. This paper introduces a new metaheuristic algorithm, called search economics (SE), for solving optimization problems. The basic idea of the SE is to depict the solution space based on the solutions that have been checked by the search algorithm and use the "information of solution space" to search for the solution on the convergence process. Based on these concepts, the investment of a search process will be more meaningful and thus not easy to fall into local optimum at the early iterations. The experimental results show that the proposed algorithm can provide a result that is significantly better than those provided by state-of-the-art metaheuristic algorithms in terms of the quality.