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中文摘要：
针对传统遗传退火算法的缺陷,提出了小生境遗传退火算法,该算法引入小生境技术,避免了搜索初期有效基因的缺失,保证了解的多样性;引入了自适应双点交叉和互换变异策略,克服了算法交叉和变异概率固定不变导致的求解过程较长和易收敛于局部最小值的缺陷;引入精英保留策略,有效地避免了最优解的丢失,加快了进化速度;通过3个经典函数测试,并将其应用于Job Shop调度问题,仿真实验结果表明：新算法有效克服了停滞现象,增强了全局搜索能力,比遗传算法和传统遗传退火算法的寻优性能更佳。
英文摘要：
According to the defects of traditional genetic annealing algorithm,a niche genetic annealing algorithm was presented,which avoided the effective gene deletions at the early search stage and guaranteed the diversity of solution.Then adaptive double point crossover and swap mutation strategy were introduced to overcome the defects of long solving process and easily converging local minimum value due to the fixed crossover and mutation probability.The elite reserved strategy was imported,optimal solution missing was avoided effectively,evolution speed was accelerated.At last,the new algorithm was tested by three typical functions and the job shop scheduling problems,respectively.The simulation results show that the proposed algorithm can avoid the stagnation,improve the global convergence ability,and attain better optimization performance.
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Product collaborative design scheme evaluation based on gray statistical evaluation and analytic net
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