Authors:
Zikri Bayraktar
1
and
Muge Komurcu
2
Affiliations:
1
Schlumberger-Doll Research Center, United States
;
2
Massachusetts Institute of Technology, United States
Keyword(s):
Multiobjective Adaptive Wind Driven Optimization, Covariance Matrix Adaptation Evolutionary Strategy, Numerical Optimization, Wind Driven Optimization, Wdo, Awdo, Mo-Awdo, Pareto, Nondominated Sorting.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Soft Computing
Abstract:
In this work, we introduce a new nature-inspired multiobjective numerical optimization algorithm where Pareto dominance is incorporated into Adaptive Wind Driven Optimization for handling multiobjective optimization problems and named as Multiobjective Adaptive Wind Driven Optimization (MO-AWDO) method. This new approach utilizes an external repository of air parcels to record the non-dominated Pareto-fronts found at each iteration via the fast non-dominated sorting algorithm, which are then utilized in the velocity update equation of the AWDO for the next iteration. The performance of the MO-AWDO is tested on five different numerical test functions with two objectives and results indicate that the MO-AWDO offers a very competitive approach compared to well-known methods in the published literature even performing better than NSGA-II for ZDT4 test function.