Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution

• In the Success-History based adaptive DE (SHADE) algorithmic framework, we propose a very basic, yet successful, nearest spatial neighborhood-based adjustment to the adaptation process of the parameters. • Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. • The effectiveness of the proposed spatial neighborhood based parameter adaptation scheme is showcased on the IEEE Congress on Evolutionary Computation (CEC) 2013, 2014, 2015, and 2017 benchmark suites. • Furthermore, the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problem benchmark suite is considered. Differential Evolution (DE) has been widely appraised as a simple yet robust population-based, non-convex optimization algorithm primarily designed for continuous optimization. Two important control parameters of DE are the scale factor F , which controls the amplitude of a perturbation step on the current solutions and the crossover rate C r , which limits the mixing of components of the parent and the mutant individuals during recombination. We propose a very simple, yet effective, nearest spatial neighborhood-based modification to the adaptation process of the aforesaid parameters in the Success-History based adaptive DE (SHADE) algorithm. SHADE uses a historical archive of the successful F and C r values to update these parameters and stands out as a very competitive DE variant of current interest. Our proposed modifications can be extended to any SHADE-based DE algorithm like L-SHADE (SHADE with linear population size reduction), jSO (L-SHADE with modified mutation) etc. The enhanced performance of the modified SHADE algorithm is showcased on the IEEE CEC (Congress on Evolutionary Computation) 2013, 2014, 2015, and 2017 benchmark suites by comparing against the DE-based winners of the corresponding competitions. Furthermore, the effectiveness of the proposed neighborhood-based parameter adaptation strategy is demonstrated by using the real-life problems from the IEEE CEC 2011 competition on testing evolutionary algorithms on real-world numerical optimization problems.