I. Introduction
Recently, solving multiobjective optimization problems (MOPs) has become important in engineering. For example, there are a lot of MOPs in robotics, such as footstep planning for humanoid robots, autonomous control for unmanned aerial vehicles (UAVs), and path planning for UAVs [1]–[3]. In order to solve MOPs, various multiobjective evolutionary algorithms (MOEAs) have been developed and have shown outstanding results through solving complex multiobjective benchmark functions. The Pareto archived evolutionary strategy (PAES) was developed by using the adaptive grid [4]. The strength Pareto evolutionary algorithm (SPEA) was created, which used a mixture of established and new techniques in order to approximate the Pareto-optimal set [5]. SPEA2, the improved version of SPEA, was developed by employing a refined fitness assignment and an enhanced archive truncation technique [6]. The nondominated sorting genetic algorithm (NSGA) was developed by the classification of nondominated fronts and the sharing operation [7]. The improved version of NSGA, NSGA-II, was created, which is a strong elitist method with a mechanism to maintain diversity efficiently by using a fast nondominated sort and crowding distance (CD) assignment [8]. The multiobjective quantum-inspired evolutionary algorithm (MQEA) was proposed to improve proximity to the Pareto-optimal set while preserving diversity [9], [10]. The multiobjective particle swarm optimization (MOPSO) was developed by extending the particle swarm optimization (PSO), which is a population-based stochastic algorithm inspired by the interaction among the individuals of a swarm, such as a flock of birds and insects [11]–[20].