Multi-objective optimization design of induction magnetometer based on improved chemical reaction algorithm

Abstract

The optimal design of Induction Magnetometer (IM) is a prevalent and practical issue. A major combinatorial optimization problem is to design an IM so that it operates optimally in the sense of producing minimal equivalent input magnetic noise level and having the minimal total weight. In this paper, we constructed a desirability function that combines the above two conflicting criteria and proposed a novel Adaptive Chemical Reaction Optimization based on Stimulating Strategy (SE-ACRO) to address this multi-objective optimization problem. CRO is a newly developed evolutionary algorithm inspired by the interactions between molecules in chemical reactions. In the proposed SE-ACRO, on the basis of the original CRO, we further introduced probability selection mechanism and stimulating strategy to improve the performance of the algorithm. In addition, the adaptive mechanism was used for the adjustment of some parameters in CRO. Simulation results demonstrate that the proposed SE-ACRO algorithm is highly competitive and outperforms many other state-of-the-art evolutionary algorithms in the aspects of searching ability, robustness, and convergence rate. At the same time, the optimal trade-offs between the equivalent input magnetic noise level and the total weight of IM is achieved.