Journal of Global Optimization
DE/EDA: a new evolutionary algorithm for global optimization
Information Sciences—Informatics and Computer Science: An International Journal
A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Advances in Differential Evolution
Advances in Differential Evolution
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
Modern continuous optimization algorithms for tuning real and integer algorithm parameters
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
A three-strategy based differential evolution algorithm for constrained optimization
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Multi-operator based evolutionary algorithms for solving constrained optimization problems
Computers and Operations Research
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
On an evolutionary approach for constrained optimization problem solving
Applied Soft Computing
Self-adaptive differential evolution incorporating a heuristic mixing of operators
Computational Optimization and Applications
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Differential evolution (DE) has shown its effectiveness in solving many problems. The difference vector (DV), which serves as a measure for the dispersion of candidate solutions, has a key role in the adaptive mutation of DE. Traditionally, DE adopts one DV. In this paper, we investigate the use of more than one DV and propose the Poisson differential evolution (PDE) with a varying number of DVs based on Poisson distribution. Experimental results on 24 numerical benchmark functions point out the ineffectiveness of increasing DVs in the original DE. On the other hand, the results show that the proposed PDE can achieve significant improvement on DE in terms of solution quality and convergence speed, which validates the benefit of varying number of DVs for DE.