A quantum-inspired evolutionary algorithm for global optimizations of inverse problems

Purpose - The purpose of this paper is to report the investigations on the potential of a new evolutionary algorithm based on probabilistic models the quantum-inspired evolutionary algorithm (QEA) in solving inverse problems. Design/methodology/approach - An improved QEA. Findings - The proposed algorithm is an efficient and robust global optimizer for solving inverse Problems. Originality/value - To enhance the convergence speed without compromising the diversity performances of the populations, a new definition of global information sharing is introduced and implemented. To guarantee the balance between exploration and exploitation searches, a different migration strategy and formula, as well as a novel formulation for adaptively updating the rotation angle, are developed.