Publications

Abstract : Evolutionary algorithms are developed systematically to solve optimization problems with one, several, and many objectives. NSGA-II is one of the universally used algorithm for superior performance and adaptability to many purposes, especially the most constant and combinatorial difficulties. NSGA-III following the configuration of classical genetic algorithms: reproductive and selection cycles. This ensures optimal placement and ensures complete coverage and connectivity with a minimum number of sensors. The NSGA-III method is very powerful in solving nonlinear feature problems and achieving many goals and this method should solve multi-target problems and solve all three problem classes. In this article we use and applies one of the most attractive ways to optimize the use of wireless sensor networks based on a meta-heuristic search called non-dominant genetic classification algorithm III. Limit connectivity while maximizing network life by achieving maximum range and minimizing power consumption. The comparison shows that the NSGA-III algorithm surpasses the Pareto-based multipurpose evolutionary approach used as the basis for this study. These results recommend applying NSGA-III to real deployment issues, and the importance of this method can be solved for a variety of purposes.