Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a da...Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy, automatic extinction, clonal propagation, diversity guarantee, and selection based on comprehensive fitness are also used in the process of IGA QSR. Theoretical analysis, numerical examples of three benchmark mathematical optimization problems and a trave ling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy （ SGA ES）. Besides, IGA QSR allows the designers to stop and restart the optimization process freely with out losing the best results that have already been obtained. These properties make IGA QSR be a fea sible, effective and robust search algorithm for complex engineering problems.展开更多
基金Supported by the National Science Foundation for Post-doctoral Scientists of China(20090460216)the National Defense Fundamental Research Foundation of China(B222006060)
文摘Combining the advantages of a genetic algorithm and an artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response （IGA QSR） is proposed. IGA QSR employs a database to simulate the standard secondary response and the quasi secondary response. Elitist strategy, automatic extinction, clonal propagation, diversity guarantee, and selection based on comprehensive fitness are also used in the process of IGA QSR. Theoretical analysis, numerical examples of three benchmark mathematical optimization problems and a trave ling salesman problem all demonstrate that IGA-QSR is more effective not only on convergence speed but also on convergence probability than a simple genetic algorithm with the elitist strategy （ SGA ES）. Besides, IGA QSR allows the designers to stop and restart the optimization process freely with out losing the best results that have already been obtained. These properties make IGA QSR be a fea sible, effective and robust search algorithm for complex engineering problems.
