In this paper, a computing speed improvement for the clonal selection algorithm (CSA) is proposed based on a degeneration recognizing (DR) method. The degeneration recognizing clonal selection algorithm (DR-CSA) is designed for solving complex engineering multimodal optimization problems. On each iteration of CSA, there is a large amount of eliminated solutions which are usually neglected. But these solutions do contain the knowledge of the nonoptimal area. By storing and utilizing these data, the DR-CSA is aimed to identify part of the new population as degenerated and eliminate them before the evaluation operation, so that a number of evaluation times can be avoided. This pre-elimination operation is able to save computing time because the evaluation is the main reason for the time cost in the complex engineering optimization problem. Experiments on both test function and a real-world engineering optimization problem (wet spinning coagulating process) are conducted. The results show that the proposed DR-CSA is as accurate as regular CSA and is effective in reducing a considerable amount of computing time.

中文翻译：

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多模态优化的退化识别克隆选择算法

本文提出了一种基于变性识别（DR）方法的克隆选择算法（CSA）的计算速度提高方法。退化识别克隆选择算法（DR-CSA）是为解决复杂的工程多模式优化问题而设计的。在CSA的每次迭代中，通常都会忽略大量被淘汰的解决方案。但是这些解决方案确实包含非最佳区域的知识。通过存储和利用这些数据，DR-CSA的目的是在评估操作之前将一部分新种群识别为退化并消除它们，从而可以避免许多评估时间。由于评估是复杂工程优化问题中时间成本的主要原因，因此这种预消除操作能够节省计算时间。进行了有关测试功能和实际工程优化问题（湿法纺丝凝固过程）的实验。结果表明，提出的DR-CSA与常规CSA一样准确，并且在减少大量计算时间方面有效。