【摘要】 为了全面提升布谷鸟搜索算法(CS)的性能,提出了一种改进的布谷鸟搜索算法(MCS)。MCS算法采用了能大幅提高局部搜索能力的局部搜索策略、能使步长控制因子随算法进程由大到小自适应变化的自适应策略和能加强布谷鸟个体间信息交流的学习策略。2个标准测试函数被用于检验算法的性能,性能测试结果及对比试验表明,MCS算法在继承了CS算法强大的全局寻优能力的同时,具有更快的收敛速度、更高的收敛精度和更好的鲁棒性。最后,将MCS算法应用于求解多效蒸发系统的优化设计问题,优化效果显著。更多还原

【Abstract】 Cuckoo search algorithm is a novel stochastic global optimization algorithm based on swarm intelligence,with advantages of few control parameters,optimal search path and good global search capability,but it also has shortcomings of weak local search ability,slow convergence velocity and low convergence accuracy.In order to comprehensively improve the performance of Cuckoo Search(CS) algorithm,a Modified Cuckoo Search(MCS) algorithm is presented in this paper.A local search strategy which can significantly improve the local search ability,an adaptive strategy which can make the step length control factor along with the process of algorithm adaptive changes from the largest to the smallest and a learning strategy which can strengthen the communication between the cuckoo individuals are used in MCS algorithm.Two benchmark test functions are used to test the performance of MCS algorithm.The performance test and comparison experiment results indicate that CS algorithm,ICS algorithm and MCS algorithm all can be converged to global optimal solution successfully.However,MCS algorithm has the highest convergence accuracy.At the same time,the convergence speed of MCS algorithm is notably superior to CS algorithm and ICS algorithm.From the mentioned above,MCS algorithm has faster convergence speed,higher convergence accuracy and more robust while inheriting powerful global search ability from CS algorithm.Finally,MCS algorithm is applied to solve the optimum design problem of multiple-effect evaporation system and remarkable optimization effect is obtained.The optimization results indicate that MCS algorithm is better than other algorithms.更多还原