多种群纵横双向学习和信息互换的鲸鱼优化算法

鲸鱼优化算法(WOA)相较于传统的群体智能优化算法，具有较好的寻优能力和鲁棒性，但仍存在全局寻优能力有限、局部极值难以跳出等问题。针对上述不平衡问题，该文提出一种多种群纵横双向学习的种群划分思路，子群相互独立，子群内个体受到来自横向和纵向两个方向的最优值影响，从而规避局部最优，在探索和开发之间取得均衡。对纵向种群的所有个体，该文提出一种线性下降概率的个体置换策略，促进不同子群的信息流动，加快算法收敛。基于不同个体的历史进化信息，来进行策略算子选择，从而区别于现有基于随机数的策略算子选择方法。利用基准函数进行跨文献对比，数值结果表明该文算法具有很好的优越性和稳定性，在大多数问题上都获得了全局极值，具有较好的问题适用性。

Whale Optimization Algorithm for Multi-group with Information Exchange and Vertical and Horizontal Bidirectional Learning

Compared with traditional swarm intelligence optimization algorithms, the Whale Optimization Algorithm(WOA) has better optimization capabilities and robustness, but there are still problems such as limited global optimization capabilities and difficulty in jumping out of local extremes. Considering the above-mentioned imbalance problem, a multi-group population division idea with vertical and horizontal bidirectional learning is proposed. The subgroups are independent of each other, and the individuals in the subgroups are affected by the optimal values from both the horizontal and vertical directions, thereby avoiding the local optimal and getting the balance between exploration and development.For all individuals in the vertical population, an individual replacement strategy with linearly decreasing probability is proposed to promote the information flow of different subgroups and accelerate the algorithm convergence.The selection of strategy operators is based on the historical evolution information of different individuals, which is different from the existing strategy operator selection methods based on random numbers.The benchmark function is used for cross-document comparison. The numerical results show that the algorithm in this thesis has good superiority and stability. It obtains global extreme on most problems and has good problem applicability.