For the problems of multi-objective genetic algorithms(MGA), chaotic optimization multi-objective optimization
genetic algorithm(CMGA) is proposed. Adaptive mutative scale chaotic optimization algorithm based on improved chaotic
map is used for search space refinement and efficient optimization. Multi-objective optimization strategies such as non-
dominated sorting mechanisms and elitist preserve are used to maintain population diversity while ensuring the evolution
direction of Pareto global optimal solution set. Multi-objective test functions simulation and numerical example of reactive
power optimization show the effectiveness and feasibility of the proposed algorithm.