A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody s...A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.展开更多
分析了传统的用于多峰函数优化问题的小生境遗传算法的特点和不足,基于免疫系统中的克隆选择原理,运用记忆算子、抑制算子和重组算子等技术对克隆选择算法进行了改造,并引入一种新的小生境技术,提出了一种解决多峰函数优化问题的小生境...分析了传统的用于多峰函数优化问题的小生境遗传算法的特点和不足,基于免疫系统中的克隆选择原理,运用记忆算子、抑制算子和重组算子等技术对克隆选择算法进行了改造,并引入一种新的小生境技术,提出了一种解决多峰函数优化问题的小生境克隆选择算法。最后,实现了该算法对单无人作战飞机(unmanned combat air vehicle,UCAV)多航路规划这类多峰函数优化问题的优化仿真,结果表明该算法简单有效。展开更多
将正交试验设计引入到克隆选择操作中,设计出基于正交试验的克隆选择操作(clonal selection operation based on orthogonal experiment design,简称CSO-OED),并将其加入到典型的克隆选择算法中,设计出并联式的CSO+CSO-OED(Ⅰ)算法和串...将正交试验设计引入到克隆选择操作中,设计出基于正交试验的克隆选择操作(clonal selection operation based on orthogonal experiment design,简称CSO-OED),并将其加入到典型的克隆选择算法中,设计出并联式的CSO+CSO-OED(Ⅰ)算法和串联式的CSO+CSO-OED(Ⅱ)算法.将新设计的算法用于9个经典的测试函数和6个复杂的测试函数进行对比测试,实验结果表明,CSO-OED能够有效地保持种群的多样性,避免算法不成熟收敛.CSO+CSO-OED(Ⅰ)和CSO+CSO-OED(Ⅱ)将全局搜索和局部搜索分开进行优化,对比实验表明,这种搜索策略不但能够保证算法的收敛性,还能有效地提高搜索解的精度,增强算法的鲁棒性.展开更多
基金Project(50275150) supported by the National Natural Science Foundation of ChinaProjects(20040533035, 20070533131) supported by the National Research Foundation for the Doctoral Program of Higher Education of China
文摘A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism.
文摘分析了传统的用于多峰函数优化问题的小生境遗传算法的特点和不足,基于免疫系统中的克隆选择原理,运用记忆算子、抑制算子和重组算子等技术对克隆选择算法进行了改造,并引入一种新的小生境技术,提出了一种解决多峰函数优化问题的小生境克隆选择算法。最后,实现了该算法对单无人作战飞机(unmanned combat air vehicle,UCAV)多航路规划这类多峰函数优化问题的优化仿真,结果表明该算法简单有效。
文摘将正交试验设计引入到克隆选择操作中,设计出基于正交试验的克隆选择操作(clonal selection operation based on orthogonal experiment design,简称CSO-OED),并将其加入到典型的克隆选择算法中,设计出并联式的CSO+CSO-OED(Ⅰ)算法和串联式的CSO+CSO-OED(Ⅱ)算法.将新设计的算法用于9个经典的测试函数和6个复杂的测试函数进行对比测试,实验结果表明,CSO-OED能够有效地保持种群的多样性,避免算法不成熟收敛.CSO+CSO-OED(Ⅰ)和CSO+CSO-OED(Ⅱ)将全局搜索和局部搜索分开进行优化,对比实验表明,这种搜索策略不但能够保证算法的收敛性,还能有效地提高搜索解的精度,增强算法的鲁棒性.
