Abstract: A self-adaptive hybrid differential evolution with simulated annealing algorithm using a constraint-handling approach based on feasibility rules，termed SahDESAfr，is proposed to solve real-parameter constrained optimization problems.In the SahDESAfr algorithm，the choice of learning strategy and several critical control parameters are not required to be pre-specified.During evolution，the suitable learning strategy and parameters setting are gradually self-adapted according to the learning experience.A simple constraint-handling approach based on feasibility rules is employed to deal with inequation constraints.The performance of the SahDESAfr algorithm is evaluated on a set of well-know constrained optimization problems commonly adopted in the specialized literature.The performance of the SahDESAfr is evaluated on the set of 13 benchmark functions.The proposed approach is compared with respect to two techniques that are representative of the state-of-the-art in the area.Comparative study exposes the SahDESAfr as a competitive algorithm for constrained optimization.

Key words: differential evolution algorithm, simulated annealing algorithm, self-adaptation, constrained optimization, constraint-handling approach

摘要： 结合基于可行性规则的约束处理技术，构造了一个求解约束优化问题的自适应杂交差分演化模拟退火算法。该算法以差分演化算法为基础，用模拟退火策略来增强种群的多样性，用一个基于可行性规则的约束处理技术来处理不等式约束，且自适应化关键控制参数，避开人为控制参数的困难。在标准测试集上的实验结果表明该算法的有效性，与同类算法的比较表明了该算法的优越性。

关键词: 差分演化算法, 模拟退火算法, 自适应技术, 约束优化, 约束处理技术