Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Operations Research
Using Genetic Algorithms in Engineering Design Optimization with Non-Linear Constraints
Proceedings of the 5th International Conference on Genetic Algorithms
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Evolutionary programming techniques for constrained optimizationproblems
IEEE Transactions on Evolutionary Computation
Coevolutionary augmented Lagrangian methods for constrainedoptimization
IEEE Transactions on Evolutionary Computation
A mixed ant colony algorithm for function optimization
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Multi-operator based evolutionary algorithms for solving constrained optimization problems
Computers and Operations Research
Quad countries algorithm (QCA)
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Investigating the application of opposition concept to colonial competitive algorithm
International Journal of Bio-Inspired Computation
Sensor deployment for fault diagnosis using a new discrete optimization algorithm
Applied Soft Computing
Gases Brownian Motion Optimization: an Algorithm for Optimization (GBMO)
Applied Soft Computing
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In this work a complete framework is presented for solving nonlinear constrained optimization problems, based on the line-up differential evolution (LUDE) algorithm which is proposed for solving unconstrained problems. Linear and/or nonlinear constraints are handled by embodying them in an augmented Lagrangian function, where the penalty parameters and multipliers are adapted as the execution of the algorithm proceeds. The LUDE algorithm maintains a population of solutions, which is continuously improved as it thrives from generation to generation. In each generation the solutions are lined up according to the corresponding objective function values. The position's in the line are very important, since they determine to what extent the crossover and the mutation operators are applied to each particular solution. The efficiency of the proposed methodology is illustrated by solving numerous unconstrained and constrained optimization problems and comparing it with other optimization techniques that can be found in the literature.