Multiple dynamic penalties based on decomposition for constrained optimization

Abstract

When solving constrained optimization problems by constraint-handling techniques, the core idea is to balance constraint violation and objective function during the search process. Although many constraint-handling techniques have proposed to deal with this problem, it is difficult to reduce the risk of falling into the local optimums and maintain good population diversity. To tackle these issues, this paper proposes an evolutionary algorithm using multiple dynamic penalties based on decomposition for constrained optimization. Specifically, a constrained optimization problem is decomposed into a number of unconstrained optimization subproblems, and each subproblem is assigned a penalty coefficient to balance constraint and objective function. Then, these subproblems are solved simultaneously, which can improve global search ability and population diversity. In order to generate proper penalty coefficients, a penalty coefficient adjusting strategy is designed, and the penalty coefficient value increases with the number of evolutionary generations to balance the exploration and exploitation. The experimental results on two benchmark test suites and six real-world application problems demonstrate the proposed algorithm is efficient and competitive.