Multi-objective plant root growth optimization algorithm for engineering design problems and UAV path planning

In this study, a new multi-objective version of Plant Root Growth Optimization Algorithm (PRGO ) called Multi-Objective Plant Root Growth Optimization Algorithm ( MOPRGO ) is proposed. MOPRGO is a combination of the traditional PRGO and elite non-dominated sorting technique to define Pareto optimal solutions by means of taproot rhizome growth and fibrous rhizome growth. Pareto archives with selection mechanisms are used to preserve and enhance the convergence and diversity of solutions. In order to validate the performance and effectiveness of MOPRGO, it is validated in 50 real engineering design problems, including 21 mechanical design problems, 3 chemical engineering problems, 5 process, design and synthesis problems, 6 power electronics problems and 15 power system optimization problems, and the statistical results are compared with those of other recognized algorithms using the same performance metrics. The comparison results show that MOPRGO is robust and superior in dealing with various multi-objective problems. To further validate the performance of the proposed algorithm, a multi-objective UAV path planning problem is also designed, and the effectiveness of MOPRGO is demonstrated by designing two complex terrain sets and comparing them with various classical and state-of-the-art multi-objective evolutionary algorithms.