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)
A comparative study of differential evolution variants for global optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
The Effects of Different Kinds of Move in Differential Evolution Searches
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Selection strategies for initial positions and initial velocities in multi-optima particle swarms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Improving differential evolution algorithm by synergizing different improvement mechanisms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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In the commonly used DE/rand/1 variant of differential evolution the primary mechanism of generating new solutions is the perturbation of a randomly selected point by a difference vector. The newly selected point may, if good enough, then replace a solution from the current generation. As the magnitude of difference vectors diminishes as the population converges, the size of moves made also diminishes, an oft-touted and obvious benefit of the approach. Additionally, when the population splits into separate clusters difference vectors exist for both small and large moves. Given that a replaced solution is not the one perturbed to create the new, candidate solution, are the large difference vectors responsible for movement of population members between clusters? This paper examines the mechanisms of small and large moves, finding that small moves within one cluster result in solutions from another being replaced and so appearing to move a large distance. As clusters tighten this is the only mechanism for movement between them.