Differential evolution with an adaptive penalty coefficient mechanism and a search history exploitation mechanism

A critical issue of constrained optimization evolutionary algorithms (COEAs) is how to balance the minimization of the objective function and the reduction of the constraint violation during the optimization process. This paper proposes a differential evolution (DE) algorithm with an adaptive penalty coefficient mechanism and a search history exploitation mechanism (called APCSH) to address this issue. Specifically, APCSH includes a learning stage and an evolving stage. In the learning stage, the correlation between the constraint violation and objective function values is calculated. Then, in the evolving stage, the correlation is used with a sigmoid function to determine the upper and lower bounds of the penalty coefficient, followed by a ranking strategy to assign the penalty coefficient to each solution. Furthermore, an exponentially weighted average method is used to update the search direction of solutions to promote convergence. Additionally, the scaling factor and crossover rate of DE are tuned automatically based on the successful search history. Many numerical experiments are conducted on 18 CEC2010 benchmark problems, 28 CEC2017 benchmark problems, and 4 heat pipe constraint optimization problems to demonstrate the advantages of the proposed algorithm over some state-of-the-art COEAs.