IMPULSIVE OPTIMAL PEST CONTROL WITH LOCAL SEARCH BY SIMULATED BINARY CROSSOVER ALGORITHM

The impulsive optimal pest control problem involves applying control measures at discrete time points, while the pest population dynamics evolve continuously between applications. Recently, this problem has been addressed using the Differential Evolution (DE) algorithm, an evolutionary technique capable of producing high-quality, though not necessarily optimal, solutions. In this work, the Simulated Binary Crossover (SBX) algorithm was employed as a local search strategy applied in the neighborhood of the solution previously obtained by DE, with the aim of refining the results. The improvements achieved highlight the effectiveness of the method, resulting in more accurate solutions, surpassing those reported in the literature.