A collection of test problems for constrained global optimization algorithms
A collection of test problems for constrained global optimization algorithms
Some guidelines for genetic algorithms with penalty functions
Proceedings of the third international conference on Genetic algorithms
EVOLVE: a genetic search based optimization code with multiple strategies
Computer aided optimum design of structures III
A parallel evolution strategy for solving discrete structural optimization
Advances in Engineering Software
Test Examples for Nonlinear Programming Codes
Test Examples for Nonlinear Programming Codes
Genetic Optimization Using A Penalty Function
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic AlgorithmsNumerical Optimizationand Constraints
Proceedings of the 6th International Conference on Genetic Algorithms
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
An adaptive penalty formulation for constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Inequality constraint handling in genetic algorithms using a boundary simulation method
Computers and Operations Research
Manufacturer-retailer supply chain coordination: A bi-level programming approach
Advances in Engineering Software
A novel selection evolutionary strategy for constrained optimization
Information Sciences: an International Journal
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Penalty function approaches have been extensively applied to genetic algorithms for tackling constrained optimization problems. The effectiveness of the genetic searches to locate the global optimum on constrained optimization problems often relies on the proper selections of many parameters involved in the penalty function strategies. A successful genetic search is often completed after a number of genetic searches with varied combinations of penalty function related parameters. In order to provide a robust and effective penalty function strategy with which the design engineers use genetic algorithms to seek the optimum without the time-consuming tuning process, the self-organizing adaptive penalty strategy (SOAPS) for constrained genetic searches was proposed. This paper proposes the second generation of the self-organizing adaptive penalty strategy (SOAPS-II) to further improve the effectiveness and efficiency of the genetic searches on constrained optimization problems, especially when equality constraints are involved. The results of a number of illustrative testing problems show that the SOAPS-II consistently outperforms other penalty function approaches.