A Noise Resilient Differential Evolution With Improved Parameter And Strategy Control

A switched-parameter Differential Evolution (DE) enforced with equiprobable switching between two alternative mutation strategies, an optional blending crossover, and a threshold-based selection mechanism is proposed for optimization of complex functions corrupted with additive noise. In order to handle the noisy optimization problems, the DE framework is coupled with three new algorithmic components. Each individual is subjected to one of the two well known mutation strategies namely DE/best/1 and DE/rand/1 with equal chances. In the recombination stage, binomial and blending crossovers are opted in the same switchable strategy as done for mutation. A novel threshold-based selection mechanism is used to allow less fit offspring to survive occasionally, thus countering the noisy function behavior. Additive Gaussian noise is used to simulate the noisy behavior of functions defined over continuous search spaces. A benchmark suite comprising of 21 well-known numerical functions is considered to compare and contrast the proposed method with other state-of-the-art evolutionary algorithms specifically tailored for noisy optimization scenario. The proposed method shows very competitive performance indicating highly robust behavior against the noisy functional landscapes.