Detection strategy optimization of large equipment group based on improved MOPSO algorithm
Aiming at the problem that the optimal detection strategy under multi-objective constraints such as tight testing schedules,high requirements for testing quality and personnel safety,and maximization of economic benefits in the inspection and testing of large crawler crane group(large equipment groups),an optimization method based on improved multi-objective particle swarm optimization(MOPSO)was proposed.Firstly,the traditional multi-objective particle swarm optimization algorithm was improved using mutation operators.Then,a multi-objective optimization model for testing duration,testing cost,testing quality,and safety was constructed based on the actual needs of large-scale equipment group testing projects,and the constraints of each sub objective were determined.Finally,the optimization algorithm and constructed model were applied to the detection project of a large crawler crane group to verify the effectiveness of the method.The research results show that the optimal detection strategy obtained using the method,comparing with traditional detection strategies,only requires 3/4 of the detection time while ensuring quality and safety.Meanwhile,the testing eunitcoste and the inspected eunitcoste can respectively save 14%and 32.8%.It greatly improved the detection efficiency,reduces the human,economic,and time costs of enterprises and inspection units,and the method has good practicality and promotion application value.
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维普
