关键词: 动态环境, 多种群热力学遗传算法（MPTDGA）, 多样性, 概率向量, 分化, 动态背包问题

Abstract: Using multi-population instead of one population has proved to be a good approach for improving the performance 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 evolutionary 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 experimental 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.

Key words: dynamic environment, multi-population based thermodynamic genetic algorithm (MPTDGA), diversity, probability vector, forking, dynamic knapsack problems