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. [ABSTRACT FROM AUTHOR]