Strategies for balancing exploration and exploitation in electromagnetic optimisation
Purpose - Electromagnetic design utilising finite element or similar numerical methods is computationally expensive, thus efficient algorithms reducing the number of objective function calls to locate the optimum are sought. The balance between exploration and exploitation may be achieved using a reinforcement learning approach, as demonstrated previously. However, in practical design problems, in addition to finding the global optimum efficiently, information about the robustness of the solution may also be important. In this paper, the aim is to discuss the suitability of different search algorithms and to present their fitness to solve the optimization problem in conjunction with providing enough information on the robustness of the solution. Design/methodology/approach - Two novel strategies enhanced by the surrogate model based weighted expected improvement approach are discussed. The algorithms are tested using a two-variable test function. The emphasis of these strategies is on accurate approximation of the shape of the objective function to accomplish a robust design. Findings - The two novel strategies aim to pursue the optimal value of weights for exploration and exploitation throughout the iterative process for better prediction of the shape of the objective function. Originality/value - It is argued that the proposed strategies based on adaptively tuning weights perform better in predicting the shape of the objective function. Good accuracy of predicting the shape of the objective function is crucial for achieving a robust design.
electromagnetics electromagnetics optimisation reinforcement learning surrogate modelling robust design kriging electromagnetic devices optimization techniques electromagnetism