Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Performance comparison of self-adaptive and adaptive differential evolution algorithms
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Advances in Differential Evolution
Advances in Differential Evolution
Hybrid Evolutionary Algorithms
Hybrid Evolutionary Algorithms
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
Automatic Clustering Using an Improved Differential Evolution Algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Differential evolution (DE) is an efficient and versatile evolutionary algorithm for global numerical optimization over continuous domain. Although DE is good at exploring the search space, it is slow at the exploitation of the solutions. To alleviate this drawback, in this paper, we propose a generalized hybrid generation scheme, which attempts to enhance the exploitation and accelerate the convergence velocity of the original DE algorithm. In the hybrid generation scheme the operator with powerful exploitation is hybridized with the original DE operator. In addition, a self-adaptive exploitation factor is introduced to control the frequency of the exploitation operation. In order to evaluate the performance of our proposed generation scheme, the migration operator of biogeography-based optimization is employed as the exploitation operator. Moreover, 23 benchmark functions (including 10 test functions provided by CEC2005 special session) are chosen from the literature as the test suite. Experimental results confirm that the new hybrid generation scheme is able to enhance the exploitation of the original DE algorithm and speed up its convergence rate.