CAJ | 학술논문

多种群方法已被证明是提高演化算法动态优化性能的重要方法之一。提出了多种群热力学遗传算法（multi-population based thermodynamic genetic algorithm，MPTDGA）。该算法使用一个概率向量在热力学遗传算法迭代过程中不断演化优化与竞争学习，环境变化时分化成三个概率向量，并分别抽样产生原对偶和随机迁入三个子种群，依据这三个种群和记忆种群最好解的情况，选择新的工作概率向量进入新环境进行学习。在动态背包问题上的实验结果表明，MPTDGA比原对偶遗传算法跟踪最优解的能力更强，有很好的多样性，非常适合求解0-1动态优化问题。
다충군방법이피증명시제고연화산법동태우화성능적중요방법지일。제출료다충군열역학유전산법（multi-population based thermodynamic genetic algorithm，MPTDGA）。해산법사용일개개솔향량재열역학유전산법질대과정중불단연화우화여경쟁학습，배경변화시분화성삼개개솔향량，병분별추양산생원대우화수궤천입삼개자충군，의거저삼개충군화기억충군최호해적정황，선택신적공작개솔향량진입신배경진행학습。재동태배포문제상적실험결과표명，MPTDGA비원대우유전산법근종최우해적능력경강，유흔호적다양성，비상괄합구해0-1동태우화문제。
Using multi-population instead of one population has proved to be a good approach for improving the per-formance of evolutionary algorithms (EAs) for dynamic optimization problems. This paper proposes a multi-population based thermodynamic genetic algorithm (MPTDGA). This algorithm forks the working probability vector while the environment is detected to be changed. At each iteration, the working probability vector is a combination of evolu-tionary optimization and competitive learning until the environment changes, at which the forked probability vectors are sampled to generate three sub-populations independently, and new working probability vector is selected according to the best fitness of three sub-populations (primal-dual, random immigrants) and memory population. The experi-mental results on dynamic knapsack problems show that MPTDGA can comprehensively explore the search space and rapidly find changing optimal solution. Compared with primal-dual genetic algorithm (PDGA), this algorithm can maintain better diversity and be more suitable to solve 0-1 dynamic problems.