Theoretical and Practical Convergence of a Self-Adaptive Penalty Algorithm for Constrained Global Optimization
提出一种自适应罚函数算法，证明约束优化问题可转化为有界约束问题，并设计混合萤火虫算法进行求解，数值实验验证其有效性。
This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solving nonsmooth and nonconvex constrained optimization problems. We prove that the general constrained optimization problem is equivalent to a bound constrained problem in the sense that they have the same global solutions. The global minimizer of the penalty function subject to a set of bound constraints may be obtained by a population-based meta-heuristic. Further, a hybrid self-adaptive penalty firefly algorithm, with a local intensification search, is designed, and its convergence analysis is established. The numerical experiments and a comparison with other penalty-based approaches show the effectiveness of the new self-adaptive penalty algorithm in solving constrained global optimization problems.