基于协同变异与莱维飞行策略的教与学优化算法及其应用

Teaching-learning-based optimization algorithm based on cooperative mutation and Lévy flight strategy and its application

针对教与学优化(TLBO)算法在处理优化问题时存在搜索不均衡、易陷入局部最优、综合求解性能弱等缺陷,提出一种基于均衡优化与莱维飞行策略的改进教与学优化算法ELMTLBO。首先设计精英均衡引导策略,通过种群中多个精英个体的均衡引导提高算法的全局寻优能力;其次在TLBO算法的学习者阶段后,利用自适应权重策略对莱维飞行产生的步长进行自适应缩量,以提高种群局部寻优能力,增强个体对复杂环境的自适应性;最后设计了变异算子池逃逸策略,通过多个变异算子的协同引导,提升算法的种群多样性。为验证算法改进的有效性,将EMLTLBO算法与侏儒猫鼬优化算法(DMOA)等先进的智能优化算法以及平衡教与学优化(BTLBO)算法、标准TLBO等同类型算法在15个国际测试函数上进行综合收敛性能比较。统计实验结果表明,与先进的智能优化算法和TLBO算法变体相比,ELMTLBO算法能够有效平衡其搜索能力,不但有效求解单峰和多峰问题,而且在复杂多峰问题上仍有显著的寻优能力。在不同策略的共同作用下,ELMTLBO算法的综合优化性能突出,全局收敛性能较为稳定。此外,ELMTLBO算法成功应用于基于隐马尔可夫模型(HMM)的多序列比对(MSA)问题中,优化后得到的高质量对齐序列可用于疾病诊断、基因溯源等,可为生物信息学提供算法支撑。

Concerning the shortcomings of unbalanced search,easy to fall into local optimum and weak comprehensive solution performance of Teaching-Learning-Based Optimization(TLBO)algorithm in dealing with optimization problems,an improved TLBO based on equilibrium optimization and Lévy flight strategy,namely ELMTLBO(Equilibrium-Lévy-Mutation TLBO),was proposed.Firstly,an elite equilibrium guidance strategy was designed to improve the global optimization ability of the algorithm through the equilibrium guidance of multiple elite individuals in the population.Secondly,a strategy combining Lévy flight with adaptive weight was added after the learner phase of TLBO algorithm,and adaptive scaling was performed by the weight to the step size generated by Lévy flight,which improved the population􀆳s local optimization ability and enhanced the self-adaptability of individuals to complex environments.Finally,a mutation operator pool escape strategy was designed to improve the population diversity of the algorithm by the cooperative guidance of multiple mutation operators.To verify the effectiveness of the algorithm improvement,the comprehensive convergence performance of the ELMTLBO algorithm was compared with 7 state-of-the-art intelligent optimization algorithms such as the Dwarf Mongoose Optimization Algorithm(DMOA),as well as the same type of algorithms such as Balanced TLBO(BTLBO)and standard TLBO on 15 international test functions.The statistical experiment results show that compared with advanced intelligent optimization algorithms and TLBO algorithm variants,ELMTLBO algorithm can effectively balance its search ability,not only solving both unimodal and multimodal problems,but also having significant optimization ability in complex multimodal problems.It can be seen that with the combined effect of different strategies,ELMTLBO algorithm has outstanding comprehensive optimization performance and stable global convergence performance.In addition,ELMTLBO algorithm was successfully applied to the Multiple Sequence Alignment(MSA)problem based on Hidden Markov Model(HMM),and the high-quality aligned sequences obtained by this algorithm can be used in disease diagnosis,gene tracing and some other fields,which can provide good algorithmic support for the development of bioinformatics.