Cooperative Artificial Bee Colony Algorithm With Multiple Populations for Interval Multiobjective Optimization Problems

In practical engineering optimization problems (such as risk assessments), the parameters of the objective functions can be intervals because of noise and uncertainty; however, such problems cannot be solved by traditional multiobjective optimization methods. Yet, very little study has addressed int...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 27; no. 5; pp. 1052 - 1065
Main Authors: Zhang, Liming, Wang, Saisai, Zhang, Kai, Zhang, Xiuqing, Sun, Zhixue, Zhang, Hao, Chipecane, Miguel Tome, Yao, Jun
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Archive search
artificial bee colony
cooperative populations
Credibility
Evolutionary algorithms
interval multiobjective optimization
Intervals
Linear programming
Mathematical programming
Multiple objective analysis
Optimization
Optimization methods
Pareto optimum
Particle swarm optimization
Populations
Search algorithms
Sociology
Statistics
Swarm intelligence
uncertain multicriteria decision-making
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:In practical engineering optimization problems (such as risk assessments), the parameters of the objective functions can be intervals because of noise and uncertainty; however, such problems cannot be solved by traditional multiobjective optimization methods. Yet, very little study has addressed interval multiobjective optimization methods compared to traditional multiobjective optimization methods. Therefore, a novel interval multiobjective optimization method called the Interval Cooperative Multiobjective Artificial Bee Colony Algorithm (ICMOABC) based on multiple populations for multiple objectives and interval credibility is proposed. Interval credibility is selected as the interval dominant method. Interval credibility is easy to combine with multiobjective 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 artificial bee colony algorithm, meaning it uses evolutionary resources more efficiently. In order to bring in diversity, the elitist learning strategy 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.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2018.2872125