Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Trigonometric Mutation Operation to Differential Evolution
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)
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Differential Evolution with Parent Centric Crossover
EMS '08 Proceedings of the 2008 Second UKSIM European Symposium on Computer Modeling and Simulation
A Memetic Differential Evolution Algorithm for Continuous Optimization
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
A Modified Differential Evolution Algorithm and Its Application to Engineering Problems
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Accelerating Differential Evolution Using an Adaptive Local Search
IEEE Transactions on Evolutionary Computation
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Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms. DE has drawn the attention of many researchers resulting in a lot of variants of the classical algorithm with improved performance. This paper presents a new modified differential evolution algorithm for minimizing continuous space. New differential evolution operators for realizing the approach are described, and its performance is compared with several variants of differential evolution algorithms. The proposed algorithm is basedon the idea of performing biased initial population. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed differential evolution algorithms. The results indicate that the proposed algorithm is able to arrive at high quality solutions in a relatively short time limit: for the largest publicly known problem instance, a new best solution could be found.