Heterogeneous Multiobjective Differential Evolution for Electric Vehicle Charging Scheduling

With the proliferation of electric vehicles, the Electric Vehicle Charging Scheduling (EVCS) becomes a critical issue in the modern transportation systems. The EVCS problem in practice usually contains several important but conflicting objectives, such as minimizing the time cost, minimizing the charging expense, and maximizing the final state of charge. To solve the multiobjective EVCS (MOEVCS) problem, the weighted-sum approaches require expertise to predefine the weights, which is inconvenient. Meanwhile, traditional Pareto-based approaches require users to frequently select the result from a large set of trade-off solutions, which is sometimes difficult to make decisions. To address these issues, this paper proposes a Heterogeneous Multiobjective Differential Evolution (HMODE) with four heterogeneous sub-populations. Specially, one is for the multiobjective optimization and the other three are single-objective sub-populations that separately optimize three objectives. These four sub-populations are evolved cooperatively to find better trade-off solutions of MOEVCS. Besides, HMODE introduces an attention mechanism to the knee and bound solutions among non-dominated solutions of the first rank to provide more representative trade-off solutions, which facilitates decision makers to select their preferred results. Experimental results show our proposed HMODE outperforms state-of-the-art methods in terms of selection flexibility and solution quality.