Multiobjective optimization problems widely exist in engineering application and science research. This paper#R##N##R##N#presents an archive bacterial foraging optimizer to deal with multiobjective optimization problems. Under#R##N##R##N#the concept of Pareto dominance, the proposed algorithm uses chemotaxis, conjugation, reproduction and#R##N##R##N#elimination-and-dispersal mechanisms to approximate to the true Pareto fronts in multiobjective optimization#R##N##R##N#problems. In the optimization process, the proposed algorithm incorporates an external archive to save the#R##N##R##N#nondominated solutions previously found and utilizes the crowding distance to maintain the diversity of the#R##N##R##N#obtained nondominated solutions. The proposed algorithm is compared with two state-of-the-art algorithms#R##N##R##N#on four standard test problems. The experimental results indicate that our approach is a promising algorithm#R##N##R##N#to deal with multiobjective optimization problems.
['Cuicui Yang', 'Junzhong Ji']
Multiobjective Bacterial Foraging Optimization using Archive Strategy