通过采用群体化策略和竞赛奖励制度,提出一种集群竞赛优化算法,该算法的基本思想可以归纳为竞争择优、胜者奖励、向优集群和保持多样,指出该算法与其它集群智能方法之间的联系与区别。采用多个经典测试函数对该算法进行评价并与其它优化方法进行比较。比较结果表明,平均起来,该算法优于粒子群优化算法和一种进化优化方法。 By using group strategy and competition reward system, this paper proposes a cluster competition optimization algorithm. The basic idea of ​​this algorithm can be summarized as follows: competitive preference, winner reward, superior cluster and diversification. It is pointed out that this algorithm and other cluster intelligence methods The connection and difference. The algorithm is evaluated using several classical test functions and compared with other optimization methods. The comparison results show that, on average, this algorithm outperforms the particle swarm optimization algorithm and an evolutionary optimization method.