Abstract
This paper proposes a simple, yet effective, modification to scaling factor and crossover rate adaptation in Success-History based Adaptive Differential Evolution (SHADE), which can be used as a framework to all SHADE-based algorithms. The performance impact of the proposed method is shown on the CEC2015 benchmark set in 10 and 30 dimensions for both SHADE and L-SHADE (SHADE with linear decrease of population size) algorithms.
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