Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
An introduction to differential evolution
New ideas in optimization
Mechanical engineering design optimization by differential evolution
New ideas in optimization
Journal of Global Optimization
Stochastic Global Optimization: Problem Classes and Solution Techniques
Journal of Global Optimization
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
On the Convergence of a Population-Based Global Optimization Algorithm
Journal of Global Optimization
Journal of Global Optimization
System design by constraint adaptation and differential evolution
IEEE Transactions on Evolutionary Computation
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
Modified movement force vector in an electromagnetism-like mechanism for global optimization
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART II
Cooperative multi-robot path planning using differential evolution
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Theoretical advances of intelligent paradigms
Recent advances in differential evolution: a survey and experimental analysis
Artificial Intelligence Review
A clustering-based differential evolution for global optimization
Applied Soft Computing
Engineering Applications of Artificial Intelligence
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Differential evolution (DE) has gained a lot of attention from the global optimization research community. It has proved to be a very robust algorithm for solving non-differentiable and non-convex global optimization problems. In this paper, we propose some modifications to the original algorithm. Specifically, we use the attraction-repulsion concept of electromagnetism-like (EM) algorithm to boost the mutation operation of the original differential evolution. We carried out a numerical study using a set of 50 test problems, many of which are inspired by practical applications. Results presented show the potential of this new approach.