基于混沌“微变异”自适应遗传算法

Adaptive genetic algorithm based on chaos“micro variation”

遗传算法可以较好地解决复杂的组合优化问题,但也存在两方面不足:一是搜索效率比其他优化算法低;二是容易过早收敛,陷入局部最优.对此,提出一种混沌"微变异"遗传算法.利用混沌优化算法具有随机性和遍历性的特点,解决遗传算法容易陷入局部最优解的早熟问题,使得新算法同时具有较强的局部搜索能力和完成全局寻找最优解的能力.同时,对遗传算法的选择算子增加了混沌扰动,对交叉算子和变异算子进行自适应调整,对适应度函数进行改进,使遗传算法整体性能得到提高.最后,通过经典函数验证表明,混沌"微变异"遗传算法比一般的混沌遗传算法和经典遗传算法的进化速度更快,搜索精度更高.

Complex combinatorial optimization problems can be solved using the genetic algorithm,but there are also two shortcomings,one is lower search efficiency than other optimization algorithms,the other is easy to premature convergence and fall into local optimum.Therefore,this paper proposes the chaos"micro variation"genetic algorithm.Due to the characteristics of randomness and ergodicity,the chaos optimization algorithm solves the premature problem that genetic algorithm is easy to fall into the local optimal solution,which makes the proposed algorithm have strong local search ability and the ability to complete the global search for the optimal solution.At the same time,chaos disturbance is added into the selection operator of the genetic algorithm,the crossover operator and mutation operator are adjusted adaptively,and the fitness function is improved,such that the overall performance of the genetic algorithm is improved.Finally,the chaotic"micro mutation"genetic algorithm has faster evolution speed and higher search accuracy than the general chaotic genetic algorithm and classical genetic algorithm through the classical function verification.