张盛意, 蔡之华, 占志刚. 基于改进模拟退火的遗传算法求解0-1背包问题[J]. 微电子学与计算机, 2011, 28(2): 61-64.
引用本文: 张盛意, 蔡之华, 占志刚. 基于改进模拟退火的遗传算法求解0-1背包问题[J]. 微电子学与计算机, 2011, 28(2): 61-64.
shu
ZHANG Sheng-yi, CAI Zhi-hua, ZHAN Zhi-gang. Solving 0-1 Knapsack Problem Based on Genetic Algorithm with Improved Simulated Annealing[J]. Microelectronics & Computer, 2011, 28(2): 61-64.
Citation: ZHANG Sheng-yi, CAI Zhi-hua, ZHAN Zhi-gang. Solving 0-1 Knapsack Problem Based on Genetic Algorithm with Improved Simulated Annealing[J]. Microelectronics & Computer, 2011, 28(2): 61-64.
shu

基于改进模拟退火的遗传算法求解0-1背包问题

Solving 0-1 Knapsack Problem Based on Genetic Algorithm with Improved Simulated Annealing

    摘要
  • 摘要: 引入改进的模拟退火思想来改进遗传算法.本算法结合了遗传算法和模拟退火算法的优点,并有效地克服了各自的弱点,使其在优化性能、优化效率和可靠性方面具有明显的优越性. 运用本算法求解不同种群规模的0-1背包问题,数值试验结果表明,算法既具有较快的收敛速度,又能够收敛到最优解,优于遗传算法和模拟退火算法.

     

    Abstract: The paper brings in simulated annealing to improve the performance of genetic algorithms. The algorithm combines the advantages and avoids the disadvantages of genetic algorithm and simulated annealing algorithm. It has superiority in performance, efficiency and reliability. This algorithm is used to solve 0-1 knapsack problem on different scaled datasets, and the results is better than either genetic algorithm or simulated annealing algorithm.

     

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