Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Immune optimization algorithm for constrained nonlinear multiobjective optimization problems
Applied Soft Computing
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Constraint handling in multiobjective evolutionary optimization
IEEE Transactions on Evolutionary Computation
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems
Applied Soft Computing
Gene silencing-A genetic operator for constrained optimization
Applied Soft Computing
Adaptive evolutionary planner/navigator for mobile robots
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Multiobjective GAs, quantitative indices, and pattern classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
For constrained multi-objective optimization problems (CMOPs), how to preserve infeasible individuals and make use of them is a problem to be solved. In this case, a modified objective function method with feasible-guiding strategy on the basis of NSGA-II is proposed to handle CMOPs in this paper. The main idea of proposed algorithm is to modify the objective function values of an individual with its constraint violation values and true objective function values, of which a feasibility ratio fed back from current population is used to keep the balance, and then the feasible-guiding strategy is adopted to make use of preserved infeasible individuals. In this way, non-dominated solutions, obtained from proposed algorithm, show superiority on convergence and diversity of distribution, which can be confirmed by the comparison experiment results with other two CMOEAs on commonly used constrained test problems.