An introduction to differential evolution
New ideas in optimization
Advances in Engineering Software
Advances in Engineering Software
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Advances in Differential Evolution
Advances in Differential Evolution
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
A framework for memetic optimization using variable global and local surrogate models
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Emerging Trends in Soft Computing - Memetic Algorithms; Guest Editors: Yew-Soon Ong, Meng-Hiot Lim, Ferrante Neri, Hisao Ishibuchi
Self-adaptive differential evolution
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Generalised opposition-based differential evolution: an experimental study
International Journal of Computer Applications in Technology
Using Cartesian genetic programming to design wire antenna
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
International Journal of Computational Science and Engineering
Automated antenna design using paralleled differential evolution algorithm
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
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Differential evolution (DE) is a reliable and versatile function optimiser especially suited for continuous optimisation problems. Practical experience, however, shows that DE easily looses diversity and is susceptible to premature and/or slow convergence. This paper proposes a modified variant of DE algorithm called improved differential evolution (IDE). It works in three phases: decentralisation, evolution and centralisation of the population. Initially, the individuals of the population are partitioned into several groups of subpopulations (decentralisation phase) through a process of shuffling. Each subpopulation is allowed to evolve independently from each other with the help of DE (evolution phase). Periodically, the subpopulations are merged together (centralisation phase) and again new subpopulations are reassigned to different groups. These three phases helps in searching all the potential regions of the search domain effectively, thereby, maintaining the diversity. The promising nature of IDE is demonstrated on a testbed of 16 benchmark problems having box constraints. Comparison of numerical results shows that IDE is either better or at par with other contemporary algorithms.