Journal of Global Optimization
A Trigonometric Mutation Operation to Differential Evolution
Journal of Global Optimization
Advances in Engineering Software
Differential evolution with dynamic stochastic selection for constrained optimization
Information Sciences: an International Journal
A perturbed particle swarm algorithm for numerical optimization
Applied Soft Computing
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
Kernel-induced fuzzy clustering of image pixels with an improved differential evolution algorithm
Information Sciences: an International Journal
Information Sciences: an International Journal
Simulated annealing algorithm with adaptive neighborhood
Applied Soft Computing
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
An effective memetic differential evolution algorithm based on chaotic local search
Information Sciences: an International Journal
Adaptive strategy selection in differential evolution for numerical optimization: An empirical study
Information Sciences: an International Journal
Scalability of generalized adaptive differential evolution for large-scale continuous optimization
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
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Differential Evolution (DE) has become a very powerful tool for global continuous optimization. Many strategies have been proposed for the generation of new solutions and every strategy has its own pros and cons, so which one of them should be selected is critical for DE performance, besides being problem-dependent. In this paper, different new solution generation strategies are integrated together and the individual advantages of different generation strategies are utilized to enhance the exploring ability and/or to accelerate the convergence. Simulated annealing idea is introduced to escape from possible local optimum attraction. Clonal selection operation employs self-adaptive Gaussian hyper-mutation along each dimension to focus the exploitation on the promising areas and exerts different influences on different dimensions. Experiments show that the proposed ideas benefit the performance of the algorithm and the proposed algorithm performs comprehensively better than other DE variants in terms of convergence stability and solution accuracy.