Abstract: The Most Valuable Player Algorithm (MVPA) is a new intelligent optimization algorithm that simulates sports competitions. It has the problems of low precision and slow convergence. An adaptive most valuable player algorithm considering multiple training methods (ACMTM-MVPA) was proposed to solve these problems. MVPA has a single initialization method, which is random and blind, reducing the convergence speed and accuracy of the algorithm. In order to enhance the level of the initial player and improve the overall strength of the initial team, the training phase was added before the competition phase of MVPA, and the neighborhood search algorithm and chaotic sequence and reverse learning algorithms were used to train and screen players; in order to enhance the player’s ability to self-explore and learn from the best player to make the player have the qualification to compete for the most valuable player trophy, an adaptive player evolution factor was added during the team competition phase. Experimental results on 15 benchmark functions show that the proposed algorithm outperforms MVPA, Particle Swarm Optimization (PSO) algorithm and Genetic Algorithm (GA) in optimization accuracy and convergence speed. Finally, an application example of ACMTM-MVPA in parameter optimization of storm intensity formula was given. The results show that this proposed algorithm is superior to accelerated genetic algorithm, traditional regression method and preferred regression method.

Key words: Most Valuable Player Algorithm (MVPA), training phase, adaptive, player evolution factor, storm intensity formula

摘要： 最有价值球员算法（MVPA）是一种模拟体育比赛的新型智能优化算法,为解决其寻优精度低和收敛速度慢等问题，提出一种考虑多种训练方式的自适应最有价值球员算法（ACMTM-MVPA）。MVPA的初始化方式单一，随机性和盲目性强，降低了算法的收敛速度和寻求精度。为了增强初始化球员的水平，提高初始球队的整体实力，在MVPA的竞争阶段之前加入了训练阶段，并在训练阶段使用邻域搜索算法以及混沌序列和反向学习算法来训练和筛选球员；为了增强球员的自我探索能力以及向最佳球员学习的能力，使球员具有争夺最有价值球员奖杯的资格，在队伍竞争阶段加入了自适应的球员进化因子。对15个标准函数的测试结果表明，ACMTM-MVPA与MVPA、粒子群优化（PSO）算法和遗传算法（GA）相比，在寻优精度和收敛速度上更有优势。最后给出了ACMTM-MVPA在暴雨强度公式参数优化中的应用实例，结果显示，该算法法明显优于自适应光学优化算法、传统回归法与优选回归法。

关键词: 最有价值球员算法, 训练阶段, 自适应, 球员进化因子, 暴雨强度公式