A novel immune genetic algorithm based on quasi-secondary response

Combining with advantages of genetic algorithm and artificial immune system, a novel genetic algorithm named immune genetic algorithm based on quasi secondary response (IGA-QSR) is proposed. IGA-QSR employs database to simulate the standard secondary response and quasi secondary response. Elitist strategy, automatic extinction, clonal propagation, diversity guarantee, and selection based on comprehensive fitness are also integrated in its process. Theoretical analysis, numerical examples of three benchmark function optimization problems and traveling salesman problem all demonstrate that IGA-QSR is more effective on convergence speed and convergence probability than simple genetic algorithm with elitist strategy (SGA-ES). Besides, IGA-QSR allows designers to stop and restart optimization process freely without being worry about loosing best result which has been got. In general, IGA-QSR improves search performance and robustness of SGA-ES and broadens its applicable fields. IGA-QSR is a feasible, effective and robust search algorithm for complex engineering problems, especially for time-consuming problems.