Evolutionary Many-Objective Optimization of Hybrid Electric Vehicle Control: From General Optimization to Preference Articulation

Abstract

Abstract-Many real-world optimization problems have more than three objectives, which has triggered increasing research interest in developing efficient and effective evolutionary algorithms for solving many-objective optimization problems. However, most many-objective evolutionary algorithms have only been evaluated on benchmark test functions and few applied to real-world optimization problems. To move a step forward, this paper presents a case study of solving a many-objective hybrid electric vehicle controller design problem using three state-of-the-art algorithms, namely, a decomposition based evolutionary algorithm (MOEA/D), a non-dominated sorting based genetic algorithm (NSGA-III), and a reference vector guided evolutionary algorithm (RVEA). We start with a typical setting aiming at approximating the Pareto front without introducing any user preferences. Based on the analyses of the approximated Pareto front, we introduce a preference articulation method and embed it in the three evolutionary algorithms for identifying solutions that the decision-maker prefers. Our experimental results demonstrate that by incorporating user preferences into many-objective evolutionary algorithms, we are not only able to gain deep insight into the trade-off relationships between the objectives, but also to achieve high-quality solutions reflecting the decision-maker's preferences. In addition, our experimental results indicate that each of the three algorithms examined in this work has its unique advantages that can be exploited when applied to the optimization of real-world problems.

A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

“…So it is desirable to further improve the performance of NSGA-II/SDR on MaOPs by developing a new MOEA which can effectively distinguish the non-dominated solutions identified by SDR. In addition, it is also interesting to assess the performance of SDR on real-world applications with many objectives [56], [57] in the future.…”

A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges

“…Preferences in those algorithms are presented in different models, such as weights in MOEA/D [19] [20,21,76] model preferences by reference vectors or points. Most recently, preference articulation methods based on reference points, reference vectors, and weights have been examined and compared on a hybrid electric vehicle control problem [121].…”

A mutation operator guided by preferred regions for set-based many-objective evolutionary optimization

“…There are now numerous methods of representing a preference, such as reference points [34], reference directions [35], reference vectors [18,36], relative importance between objectives [37], and preferred regions [38]. An achievement scalarizing function is a special one based on reference points and can mirror preferences to some degree.…”