多策略改进的乌燕鸥算法及应用

Multi-Strategy Improved Sooty Tern Optimization Algorithm and its Engineering Application

乌燕鸥算法(STOA)存在收敛缓慢、稳定性差、收敛精度低等问题,鉴于此,提出一种多策略改进的乌燕鸥算法(MISTOA)。首先,为增强初始种群的多样性,采用Cat混沌映射对STOA算法种群进行初始化。其次,将自适应权重因子和高斯函数改进了算法的迁徙位置更新方式,增强了算法的全局搜索能力。同时,结合自适应权重因子和邻代交叉学习策略改进了算法的攻击位置的更新方式,增强了算法跳出局部最优的能力。最后,采用高斯变异策略对乌燕鸥最优个体进行扰动,提高算法的全局搜索与局部搜索之间的平衡能力。利用7个测试函数和主梁轻量化设计对MISTOA算法收敛性能和工程实际应用能力进行了验证。结果表明:与其他5种先进的算法,MISTOA算法收敛性能更优,稳定性较好和鲁棒性较强。MISTOA算法可实现桥式起重机主梁质量减重率约为20.76%,优化结果优于已有的方法,因此,MISTOA算法可以高效地处理复杂的非线性约束的现实问题。

In view of the problems of slow convergence,poor stability and low convergence accuracy ofSTOA,a multi-strategy improved STOA algorithm(MISTOA)was proposed.Firstly,in order to enhance the diversity of the initial population,Cat chaotic map was used to initialize the STOA algorithm population.Secondly,the adaptive weighting factor and Gaussian function were used to improve the migration position update method of the algorithm,which enhanced the global search ability of the algorithm.At the same time,the attack position update method of the algorithm was improved by combining the adaptive weighting factor and the neighbor generation cross learning strategy,which enhanced the ability of the algorithm to jump out of the local optimal.Finally,Gaussian mutation strategy was used to disturb the optimal individual to improve the balance between global search and local search.The convergence performance and practical application ability of MISTOA algorithm were verified by using 7 test functions and lightweight design of main girder.The results show that compared with other five advanced algorithms,MISTOA algorithm has better convergence performance,better stability and stronger robustness.MISTOA algorithm can reduce the girder quality of bridge crane by about 20.76%,and the optimization result is better than other methods.Therefore,MISTOA algorithm can efficiently deal with the practical problems with complex nonlinear constraints.