Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Self-adaptive simulated binary crossover for real-parameter optimization
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Statistical methods for convergence detection of multi-objective evolutionary algorithms
Evolutionary Computation
Online convergence detection for multiobjective aerodynamic applications
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
A bi-objective based hybrid evolutionary-classical algorithm for handling equality constraints
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
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In this paper, we propose a hybrid reference-point based evolutionary multi-objective optimization (EMO) algorithm coupled with the classical SQP procedure for solving constrained single-objective optimization problems. The reference point based EMO procedure allows the procedure to focus its search near the constraint boundaries, while the SQP methodology acts as a local search to improve the solutions. The hybrid procedure is shown to solve a number of state-of-the-art constrained test problems with success. In some of the difficult problems, the SQP procedure alone is unable to find the true optimum, while the combined procedure solves them repeatedly. The proposed procedure is now ready to be tested on real-world optimization problems.