Integrated intelligent algorithms for global optimization

The systematical integrating of the intelligent algorithms for global optimization such as genetic algorithm, simulated annealing algorithm and so on is discussed. The properties and characteristics of these intelligent algorithms and local search algorithms for optimization are analyzed respectively. A unified structure for a class of integrated intelligent algorithms for global optimization, IGIOA, is given, and some key factors in designing a particular integrating intelligent algorithm are presented. Some indices for evaluation and comparison of the integrated intelligent algorithms are proposed involving the evaluations of algorithms for optimization, time cost, and robustness. And a weighted index to combine these three evaluations used in selecting and comparing integrating intelligent algorithms is presented.