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在非线性约束优化中,处理好约束条件和增强局部搜索能力是解决这类问题的关键.本文在给出问题一般形式的基础上,设计了一个模拟退火和遗传算法结合的算法.它用模拟退火算法来增强局部搜索能力,用线性交叉来处理约束以外的解,将可行解与不可行解用适应值的正负来区分.仿真试验表明,该算法收敛速度快、搜索能力强、稳健性好,本方法是对应用遗传算法求解非线性约束优化问题的又一次深入探索.
In non-linear constrained optimization, dealing with constraints and enhancing the ability of local search is the key to solve this problem.This paper presents a general algorithm based on simulated annealing and genetic algorithm, Annealing algorithm to enhance the local search ability, linear cross to deal with the solution outside the constraint, the feasible solution and infeasible solution using the positive and negative to distinguish between.Furthermore, the simulation results show that this algorithm has the advantages of fast convergence, strong searching ability and robustness Well, this method is another deep exploration of the application of genetic algorithm to solve nonlinear constrained optimization problems.