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中文摘要：
基于克隆选择学说,采用浮点数编码,提出了一种新的克隆选择算法.定义了精英克隆变异和启发式交叉2种主要算子;对高亲和度抗体实施小幅变异策略以进行局部搜索,对中等亲和度抗体群实施与高亲和度抗体群进行启发式交叉的策略以加快全局搜索,低亲和度抗体则死亡再生以保持种群多样性;为防止进化停滞,自适应地调整亲和度尺度变换参数.对4个复杂函数的测试结果表明该算法有效地克服了早熟问题,收敛速度快,性能稳定,精度高.
英文摘要：
Based on clonal selection principle, a novel evolutionary algorithm is proposed using float point number code. The two main operations of elitism clone mutation and heuristic crossover are defined. The elitist antibodies with highest affinity are subject to a small mutation process to search local optima. Those antibodies with general affinity are suffered to a heuristic crossover with elitist antibodies to speed global optimization. The antibodies with lowest affinity are replaced by new individuals to maintain the diversity of the population. Also, to prevent evolutionary stagnation, the scaling factors of affinity are adaptively adjusted. The computer simulation results, which have adopted 4 comprehensive benchmark functions, demonstrate that the proposed algorithm has good performance.
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