Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Hybrid Interior-Langrangian Penalty Based Evolutionary Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A Multi-objective Approach to Constrained Optimisation of Gas Supply Networks: the COMOGA Method
Selected Papers from AISB Workshop on Evolutionary Computing
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
A hybrid evolutionary multi-objective and SQP based procedure for constrained optimization
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
Society and civilization: An optimization algorithm based on the simulation of social behavior
IEEE Transactions on Evolutionary Computation
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Constrained engineering design optimization problems are usually computationally expensive due to non-linearity and non convexity of the constraint functions. Penalty function methods are found to be quite popular due to their simplicity and ease of implementation, but they require an appropriate value of the penalty parameter. Bi-objective approach is one of the methods to handle constraints, in which the minimization of the constraint violation is included as an additional objective. In this paper, constrained engineering design optimization problems are solved by combining the penalty function approach with a bi-objective evolutionary approach which play complementary roles to help each other. The penalty parameter is approximated using bi-objective approach and a classical method is used for the solution of unconstrained penalized function. In this methodology, we have also eliminated the local search parameter which was needed in our previous study.