Research Area: Metaheuristic Computing
In practical engineering optimization problems (like risk assessments), the parameters of the objective functions can be intervals because of noise and uncertainty; however, such problems cannot be solved by traditional multi-objective optimization methods. And yet, very little study has addressed interval multi-objective optimization methods compared to traditional multi- objective optimization methods. Therefore, a novel interval multi-objective optimization method called the Interval Cooperative Multi-Objective Artificial Bee Colony Algorithm (ICMOABC) based on multiple populations for multiple objectives (MPMO) and interval credibility is proposed. Interval credibility is selected as the interval dominant method. Interval credibility is easy to combine with multi-objective optimization methods because it can describe the mean and width of intervals without increasing the dimension of the objective functions. The proposed algorithm has M single-objective optimization subpopulations updated by ABC, which means that it uses evolutionary resources more efficiently. In order to bringing in diversity, the elitist learning strategy (ELS) is used in the archive. The results of ICMOABC on various benchmark problems sets with different characteristics demonstrate its superior performance compared to some state-of-the-art algorithms.
Keywords:
Uncertain multi-criteria decision-making
Interval multi-objective optimization
Cooperative populations
Archive search Artificial bee colony
Author(s) Name: Liming Zhang, Saisai Wang, Kai Zhang, Xiuqing Zhang, Zhixue Sun, Hao Zhang, Miguel Tome Chipecane, Jun Yao
Journal name: IEEE Transactions on Fuzzy Systems
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/TFUZZ.2018.2872125
Volume Information: Volume: 27, Issue: 5, May 2019
