Journal of Global Optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Two improved differential evolution schemes for faster global search
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A New Differential Evolution for Constrained Optimization Problems
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
Optimization of a Kraft Pulping System: Using Particle Swarm Optimization and Differential Evolution
AMS '08 Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS)
Advances in Differential Evolution
Advances in Differential Evolution
Self-adaptive differential evolution
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Opposition-Based Differential Evolution
IEEE Transactions on Evolutionary Computation
Optimal coordination of over-current relays using modified differential evolution algorithms
Engineering Applications of Artificial Intelligence
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. Mutation operation plays the most significant role in the performance of a DE algorithm. This paper proposes a simple modified version of classical DE called MDE. MDE makes use of a new mutant vector in which the scaling factor F is a random variable following Laplace distribution. The proposed algorithm is examined on a set of ten standard, nonlinear, benchmark, global optimization problems having different dimensions, taken from literature. The preliminary numerical results show that the incorporation of the proposed mutant vector helps in improving the performance of DE in terms of final convergence rate without compromising with the fitness function value.