Methodology and Case Study of Hybrid Quantum-Inspired Evolutionary Algorithm for Numerical Optimization

This paper proposes a hybrid quantum-inspired evolutionary algorithm which codes individuals with amplitudes. The evolutionary goals are evolved by classical crossover operator. Self-adaptive rotation operator and mutation operator with respect to mutation degree are introduced too. Extensive case studies show that the novel algorithm exceeds other quantum evolutionary algorithms and classical genetic algorithms on the single-objective numerical optimization problems. In addition, novel algorithm with random weighted-sum aggregation strategy performs very well on multi-objective numerical optimization problems.