ACM Computing Surveys (CSUR)
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Journal of Global Optimization
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Designing a hierarchical fuzzy logic controller using the differential evolution approach
Applied Soft Computing
A multipopulation cultural algorithm using fuzzy clustering
Applied Soft Computing
Crowding clustering genetic algorithm for multimodal function optimization
Applied Soft Computing
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Opposition versus randomness in soft computing techniques
Applied Soft Computing
Differential Evolution as a viable tool for satellite image registration
Applied Soft Computing
Differential evolution approach for optimal reactive power dispatch
Applied Soft Computing
Digital Signal Processing
Influence of crossover on the behavior of Differential Evolution Algorithms
Applied Soft Computing
Adaptation in differential evolution: A numerical comparison
Applied Soft Computing
Mathematics and Computers in Simulation
Model-free adaptive control design using evolutionary-neural compensator
Expert Systems with Applications: An International Journal
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
CSSIM '09 Proceedings of the 2009 International Conference on Computational Intelligence, Modelling and Simulation
A clustering particle swarm optimizer for dynamic optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CIDE: Chaotically Initialized Differential Evolution
Expert Systems with Applications: An International Journal
A 2-Opt based differential evolution for global optimization
Applied Soft Computing
A differential evolution algorithm with self-adapting strategy and control parameters
Computers and Operations Research
A clustering-based differential evolution for global optimization
Applied Soft Computing
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
An Evolutionary Algorithm for Global Optimization Based on Level-Set Evolution and Latin Squares
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
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Operations Research Letters
A novel differential evolution algorithm with adaptive of population topology
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators
Information Sciences: an International Journal
Multi-level image thresholding by synergetic differential evolution
Applied Soft Computing
An improved quantum-behaved particle swarm optimization algorithm
Applied Intelligence
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Differential evolution (DE) is a simple and efficient global optimization algorithm. However, DE has been shown to have certain weaknesses, especially if the global optimum should be located using a limited number of function evaluations (NFEs). Hence hybridization with other methods is a research direction for the improvement of differential evolution. In this paper, a hybrid DE based on the one-step k-means clustering and 2 multi-parent crossovers, called clustering-based differential evolution with 2 multi-parent crossovers (2-MPCs-CDE) is proposed for the unconstrained global optimization problems. In 2-MPCs-CDE, k cluster centers and several new individuals generate two search spaces. These spaces are then searched in turn. This method utilizes the information of the population effectively and improves search efficiency. Hence it can enhance the performance of DE. A comprehensive set of 35 benchmark functions is employed for experimental verification. Experimental results indicate that 2-MPCs-CDE is effective and efficient. Compared with other state-of-the-art evolutionary algorithms, 2-MPCs-CDE performs better, or at least comparably, in terms of the solution accuracy and the convergence rate.