An effective WSN deployment algorithm via search economics

The development of a powerful search mechanism to find a good solution is the current research direction of studies on metaheuristic algorithms; however, most of the developed mechanisms will search and check the possible solutions without knowledge of the overall landscape of the solution space during the convergence process. To make each search during the convergence process as effective as possible, this paper presents a new metaheuristic algorithm called search economics (SE) to solve the deployment problem of wireless sensor networks. The main distinguishing features of the SE are twofold: the first is its capability to depict the solution space based on the solutions that have been checked by the search algorithm, and second is its capability to use the knowledge thus obtained, i.e., the “landscape of the solution space,” during the search process. On the basis of these concepts, the investment in a search process will be more meaningful and thus less easy to fall into a local optimum during early iterations. The experimental results show that the proposed algorithm can provide a result for the deployment problem that is significantly better than those provided by the state-of-the-art metaheuristic algorithms evaluated in this study in terms of the quality.