A multi-objective teaching-learning-based optimizer for a cooperative task allocation problem of weeding robots and spraying drones

Abstract

In recent years, both intelligent robots and drones have been widely used in agriculture. This paper studies a cooperative task allocation problem of weeding robots and spraying drones (WRSDCTA) with two objectives of minimizing the maximum completion time and minimizing the total residual herbicide. Based on the characteristics of the problem, a mathematical model is established. An effective multi-objective teaching-learning-based optimizer (MOTLBO) is proposed to solve this problem. At first, two heuristic initialization methods are designed to generate high quality initial population. And then, a multi-teacher strategy is presented when selecting teachers and students. For teachers, two heuristic search operators are designed to improve personal ability. For students, an improved OBX crossover operator is designed to learn from selected teachers. In addition, a multi-neighborhood search strategy is presented to further improve the local search capability of the algorithm. Finally, the proposed MOTLBO algorithm is compared with several state-of-the-art algorithms from close-related literature, and the results demonstrate the effectiveness and high performance of MOTLBO for solving the WRSDCTA problem.