GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A constraint handling approach for the differential evolution algorithm
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Constraint-Handling in Evolutionary Optimization
Constraint-Handling in Evolutionary Optimization
Differential evolution algorithm with strategy adaptation for global numerical 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
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
Search biases in constrained evolutionary optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
A Generic Framework for Constrained Optimization Using Genetic Algorithms
IEEE Transactions on Evolutionary Computation
A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization
IEEE Transactions on Evolutionary Computation
An Adaptive Tradeoff Model for Constrained Evolutionary Optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An improved (µ+λ)-constrained differential evolution for constrained optimization
Information Sciences: an International Journal
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This paper proposes a (μµ + λλ)-differential evolution and an improved adaptive trade-off model for solving constrained optimization problems. The proposed (μµ + λλ)-differential evolution adopts three mutation strategies (i.e., rand/1 strategy, current-to-best/1 strategy, and rand/2 strategy) and binomial crossover to generate the offspring population. Moreover, the current-to-best/1 strategy has been improved in this paper to further enhance the global exploration ability by exploiting the feasibility proportion of the last population. Additionally, the improved adaptive trade-off model includes three main situations: the infeasible situation, the semi-feasible situation, and the feasible situation. In each situation, a constraint-handling mechanism is designed based on the characteristics of the current population. By combining the (μµ + λλ)-differential evolution with the improved adaptive trade-off model, a generic method named (μµ + λλ)-constrained differential evolution ((μµ + λλ)-CDE) is developed. The (μµ + λλ)-CDE is utilized to solve 24 well-known benchmark test functions provided for the special session on constrained real-parameter optimization of the 2006 IEEE Congress on Evolutionary Computation (CEC2006). Experimental results suggest that the (μµ + λλ)-CDE is very promising for constrained optimization, since it can reach the best known solutions for 23 test functions and is able to successfully solve 21 test functions in all runs. Moreover, in this paper, a self-adaptive version of (μµ + λλ)-CDE is proposed which is the most competitive algorithm so far among the CEC2006 entries.