多策略融合的改进天鹰优化算法

Improved Aquila Optimization Based on Multi-Strategy Integration

为了解决天鹰优化算法(Aquila Optimization algorithm,AO)易陷入局部最优及收敛速度慢的问题,本文提出一种多策略融合的改进天鹰优化算法(Multi-Strategy Integration Aquila Optimization algorithm,MSIAO).该算法采用结合Tent混沌映射的折射反向学习初始化种群以提高算法前期的搜索效率,根据种内互助及优化策略解决算法寻优停滞的缺陷,并通过基于Bernoulli混沌序列的自适应权重策略提高算法的收敛速度,引入了柯西-高斯变异算子增强算法迭代后期逃逸局部极值的能力.本文对10个基准函数、部分CEC2014测试函数集进行实验,并将MSIAO用于2个工程设计优化问题.结果表明,对于高维单峰、高维多峰以及固定维复杂多模态函数,MSIAO比AO具有更高的收敛精度和更快的收敛速度;MSIAO对压力容器与焊接梁优化设计的经济成本较AO分别节约4.62%、0.77%,验证了MSIAO对于处理机械工程问题的实用性和优越性.

In order to solve the problem that aquila optimization algorithm(AO)is easy to fall into local optimum and slow convergence,this paper proposes an improved aquila optimization algorithm with multi-strategy integration(MSIAO).In this algorithm,the refracted opposition-based learning combined with Tent chaotic map is used to initialize the population to improve the early search efficiency of the algorithm,and intraspecific and mutual assistance and optimization strategy are used to solve the problem of optimization stagnation of the algorithm.The convergence speed of the algorithm is improved by an adaptive weighting strategy based on Bernoulli chaotic sequences.Cauchy-Gaussian mutation operator is introduced to enhance the ability of the algorithm to escape local extremum in the later iteration.This paper conducts experi-ments on 10 benchmark functions and some CEC2014 test function sets,and the proposed MSIAO is applied to 2 engineer-ing design optimization problems.The results show that MSIAO has higher convergence accuracy and faster convergence speed than AO for high-dimensional single-peak,high-dimensional multi-peak and fixed-dimensional complex multimode functions.Compared with AO,MSIAO saves 4.62%and 0.77%in economic cost of optimal design of pressure vessel and welding beam,which verifies the practicability and superiority of MSIAO in dealing with mechanical engineering problems.