Incorporating Hybrid Operators on an Immune Based Framework for Multiobjective Optimization

This paper presents an Artificial Immune System framework for solving Multiobjective Optimization Problems that makes use of two immunologic operators with non-immunologic features, called Hybrid Operators, which were inspired from Artificial Neural Networks and Genetic Algorithms techniques. The first of these operators makes use of the concept of Momentum to improve the performance of mutation while the second incorporates the recombination operator. The new AIS framework is called MOHAIS, for Multiobjective Optimization Hybrid Artificial Immune System. The proposed framework was used to implement one traditional AIS algorithm and a new hybrid AIS, based on proposed hybrid operators. Experiments were made to evaluate hybrid operators performance, to compare the hybrid AIS with one traditional AIS and three Multiobjective Optimization Evolutionary Algorithms, in nine traditional benchmark problems. Results showed that hybrid operators improved the performance of the AIS with satisfactory results in all nine scenarios.