Multi-objective Quantum-Inspired Cultural Algorithm

It had been proved that the knowledge may promote more efficient evolution. Considering the knowledge defined in different form, we present multi-objective quantum-inspired cultural algorithms so as to effectively utilize the implicit information embodied in the evolution to promote more efficient search. The dual structure derived from cultural algorithm was adopted. In population space, the rectangle´s height of each allele in quantum individuals was calculated in terms of non-dominated rank by sorting among individuals, instead of the relative fitness values. In belief space, the knowledge memorized the distribution and location about the non-dominated individuals´ objective values in the objective space and directed the mutation and selection operations so as to influence the update of quantum individuals further. The statistical simulation results for five benchmark functions indicated that the proposed algorithm keeps the diversity of population better and obtains more uniform pareto-optimal solutions near the true pareto front.