关键词: 智能优化算法, 乌鸦搜索算法, 变因子加权学习机制, 邻代维度交叉策略, 基准测试函数

Abstract: Considering that crow search algorithm (CSA) has low optimization accuracy and weak local-optimum escape ability in optimizing high-dimensional problems,an improved crow search algorithm(ICSA) is proposed by coupling the variable-factors' weighted learning mechanism of multiple individuals(Mi-VWL) and the adjacent-generations dimension crossover strategy of the best individual(Bi-ADC).In the proposed algorithm,the model parameters,i.e.awareness probability and flight length,are firstly modified dynamically with increasing number of iterations.Meanwhile,the Mi-VWL is introduced to guarantee that offspring individuals of crow population can inherit position information from the followed crow and the best individual of the last generation simultaneously,which is advantageous to avoid the over-rapid population intensification of single-individual learning and reduce the algorithm's risk on dropping into local optimum.Furthermore,Bi-ADC is constructed and the priority replacement principle of larger absolute value difference of dimensions between two-generations is adopted to update position of the best individual,which is beneficial to retain the optimal dimension information of historical best crows and enhance the local extreme escape ability of algorithms.Experimental results verify the influence of modal parameters on CSA's performance,the effectiveness and differences of different-type weighted learning factor on improving ICSA's capability and the superior optimization ability of the proposed algorithm,respectively.

Key words: intelligent optimization algorithm, crow search algorithm, variable-factor weighted learning mechanism, adjacent-generations dimension crossover strategy, benchmark function