A Quantum-Inspired Evolutionary Algorithm with Elite Group Guided

Abstract

To improve the performance of quantum-inspired evolutionary algorithms (QIEAs), a new kind of QIEAs——elite group guided QIEA (EQIEA) are proposed through introducing an elite group guidance updating approach to solve knapsack problems. In EQIEA, the elite group at each iteration is composed of a certain number of individuals with better fitness values in the current population; all the individuals in the elite group cooperate together to affect quantum-inspired gates to produce off spring. Knapsack problems, a class of well-known NP-complete combinatorial optimization problems, are used to conduct experiments. The choices of parameters in EQIEA are discussed in an empirical way. Extensive experiments show that the EQIEA outperform six variants of QIEAs recently reported in the literature in terms of the quality of solutions. This paper also analyzes the convergence of EQIEA and the six variants of QIEAs. Experimental results show that EQIEA has better convergence than the six variants of QIEAs.