Adaptive constraint handling and Success History Differential Evolution for CEC 2017 Constrained Real-Parameter Optimization

摘要

This paper presents Success-History Based Adaptive Differential Evolution Algorithm (SHADE) including Linear population size reduction (L-SHADE), enhanced with adaptive constraint violation handling, applied to the benchmark for CEC 2017 Competition on Constrained Real-Parameter Optimization. The constraint handling method prioritizes the feasible solutions before infeasible, while disregarding the constraint violation values below an adaptive threshold, i.e. adaptive ε-constraint handling. The 28 constrained test functions on 10, 30, 50, and 100 dimensions are assessed on the benchmark and the required resulting final fitnesses, constraints violations, and success rates are reported for 25 independent runs of the proposed algorithm under the budget of fixed maximum number of fitness evaluations for 10, 30, 50, and 100 dimensional test functions.