基于多目标文化鲸鱼算法的水库防洪调度

The reservoir flood control operation problem (RFCO) is a complex multi-objective problem (MOPs), with numerous complex constraints, interdependent decision variables, and conflicting optimization objectives. Traditional research mostly stays at converting multi-objective problems into single-objective problems for solution, and there are certain limitations in practical applications. In view of this, a multi-objective optimization method for reservoir flood control operation - the multi-objective cultural whale optimization algorithm (MOCWOA) is proposed. MOCWOA takes the cultural algorithm (CA) as a framework, adopts the whale optimization algorithm (WOA) in the population space, and defines three knowledge structures in the belief space to improve the diversity and convergence accuracy of the algorithm results. MOCWOA is first applied to the optimization of typical test functions, and then further applied to the actual reservoir flood control operation problem, and is compared with several excellent multi-objective optimization algorithms. The results show that MOCWOA has certain advantages both on typical test functions and on the actual RFCO problem.

水库防洪调度问题（ＲＦＣＯ）是复杂的多目标问题（ＭＯＰｓ），具有众多复杂的约束条件，相互依存的决策.变量，以及相互冲突的优化目标，传统研究多停留在将多目标问题转换为单目标问题解决，在实际应用中存在一定.限制。鉴于此，提出一种针对水库防洪调度的多目标优化方法———文化鲸鱼算法（ＭＯＣＷＯＡ）。ＭＯＣＷＯＡ 以文.化算法（ＣＡ）为框架，在种群空间采用鲸鱼优化算法（ＷＯＡ），在信度空间定义了３种知识结构以提高算法所得结果.的多样性和收敛精度。ＭＯＣＷＯＡ 先应用于典型测试函数的优化，之后进一步应用于实际的水库防洪调度问题，..并与几种优秀的多目标优化算法进行对比，结果表明，无论是在典型测试函 数 上，还 是 在 实 际 ＲＦＣＯ 问 题 上，..ＭＯＣＷＯＡ 都具有一定的优势。.