Journal of Global Optimization
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
A clustering-based differential evolution for global optimization
Applied Soft Computing
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In searching for optimal solutions, Differential Evolution (DE), a type of genetic algorithms can find an optimal solution satisfying all the constraints. However, DE has been shown to have certain weaknesses, such as slow convergence,the accuracy of solutions are not high. In this paper, we propose an improved differential evolution based on orthogonal design, and we call it ODE (Orthogonal Differential Evolution). ODE makes DE faster and more robust. It uses a novel and robust crossover based on orthogonal design and generates an optimal offspring by a statistical optimal method. A new selection strategy is applied to decrease the number of generations and make the algorithm converge faster. We evaluate ODE to solve twelve benchmark function optimization problems with a large number of local minimal. Simulations results show that ODE is able to find the near-optimal solutions in all cases. Compared to other state-of-the-art evolutionary algorithms, ODE performs significantly better in terms of the quality, speed, and stability of the final solutions.