Constraint handling in multiobjective evolutionary optimization
IEEE Transactions on Evolutionary Computation
An adaptive penalty formulation for constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Parameter control in differential evolution for constrained optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Differential evolution with level comparison for constrained optimization
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A novel modified differential evolution algorithm for constrained optimization problems
Computers & Mathematics with Applications
Directed searching optimization algorithm for constrained optimization problems
Expert Systems with Applications: An International Journal
Geometric nelder-mead algorithm on the space of genetic programs
Proceedings of the 13th annual conference on Genetic and evolutionary computation
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Expert Systems with Applications: An International Journal
Geometric generalization of the nelder-mead algorithm
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
Constrained optimization based on modified differential evolution algorithm
Information Sciences: an International Journal
Information Sciences: an International Journal
A novel selection evolutionary strategy for constrained optimization
Information Sciences: an International Journal
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Constrained optimization problems are very important and frequently appear in the real world. The α constrained method is a new transformation method for constrained optimization. In this method, a satisfaction level for the constraints is introduced, which indicates how well a search point satisfies the constraints. The α level comparison, which compares search points based on their level of satisfaction of the constraints, is also introduced. The α constrained method can convert an algorithm for unconstrained problems into an algorithm for constrained problems by replacing ordinary comparisons with the α level comparisons. In this paper, we introduce some improvements including mutations to the nonlinear simplex method to search around the boundary of the feasible region and to control the convergence speed of the method, we apply the α constrained method and we propose the improved α constrained simplex method for constrained optimization problems. The effectiveness of the α constrained simplex method is shown by comparing its performance with that of the stochastic ranking method on various constrained problems.