A fast evolutionary-deterministic algorithm to study multimodal current fields under safety level constraints
Purpose - This paper aims to design an algorithm able to locate all the possible dangerous areas generated by the leaking of a fault current in a grounding system (i.e. the areas where the limits of the technical standards are not respected) and thus locate, inside each area, the point which takes locally the maximum value of touch voltage. Design/methodology/approach - A fast evolutionary-deterministic algorithm to solve constrained multimodal optimization problems is proposed. The algorithm is composed by three algorithmic blocks: a Quasi Genetic Algorithm to find a population of feasible solutions, a Fitness Sharing Selection to choose a subpopulation of feasible and fitter solutions having high diversity, a Hooke-Jeeves Algorithm to find all the global and local feasible maxima. Findings - The proposed algorithm has been successfully applied to various current field (i.e. to many shapes of grounding grids) problems to find the dangerous values of touch voltages generated by various grounding systems having any shape and it has turned out to be fast and reliable. Originality/value - For this kind of problems, in fact, there is a lack, in literature, of multimodal optimization methods under safety constraints and the application of classical methods (e.g. genetic algorithms or deterministic methods) would be often inadequate since these methods are made so as to converge towards a single maximum point and so they unavoidably lose the information related to all the other possible maxima. On the contrary, a good application of the proposed allows the overcoming of these limits.
optimization techniques electric fields electric current programming and algorithm theory