Evolutionary Asynchronous Optimization–A Novel Optimization Paradigm for Non-contact Voltage Measurement

Non-contact voltage measurement represents a novel but challenging area within modern smart grid systems, which mainly includes structure design for sensing the electric field and voltage estimation via the sensed information. Existing approaches regarded the two components as independent optimization tasks, where the former is low-dimensional but computationally expensive while the latter is high-dimensional but relatively cheap. In this study, we argue that the non-contact measurement task is an asynchronous optimization problem with heterogeneous features in both the decision and objective spaces. Specifically, the problem includes two subproblems to be optimized sequentially, and the result of the first subproblem affects the optimum of the second one. Consequently, we propose a new optimization paradigm called evolutionary asynchronous optimization to achieve the collaborative optimization of the two tasks, and accordingly develop a new asynchronous evolutionary algorithm to solve it. Experimental results on three laboratory cases validate that, the proposed algorithm exhibits superior performance over existing multi-stage, multi-objective, and bi-level evolutionary algorithms.