Population Dynamics Optimization Algorithm Under Microbial Control in Contaminated Environment

doi:10.3778/j.issn.1673-9418.2002046

Abstract

Abstract:

To solve some function optimization problems, the PDO-MCCE algorithm is proposed by using the population dynamics model with microbial control in contaminated environment. In this algorithm, individuals are automatically divided into two categories, normal population and mutation population. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, which solves the problem of determining the number of individuals artificially. The algorithm has 7 operators, among which the competition and mutation operators realize the information exchange within and between populations respectively; the influence and poison operators realize the information diffusion of strong individuals and the transfer of environmental information to individuals respectively; the new and death operators increase and reduce the number of individuals respectively; the growth operator ensures the algorithm has global convergence; the number of individuals in the mutation population increases periodically and the probability of jumping out from the trap of local optimal solutions can be greatly increased; in the iterative progress, the algorithm only deals with 3/500~1/10 of the number of individual features at a time, thus greatly reducing the time complexity. Test cases show that the PDO-MCCE algorithm has good performance and is suitable for solving some optimization problems with high dimensions.

Key words: swarm intelligence optimization algorithm, population dynamics, environmental pollution, microbial control

摘要：

为了解决一些函数优化问题，采用污染环境中具微生物治理的种群动力学模型，提出了PDO-MCCE算法。在该算法中，个体被自动划分成正常种群和突变种群2类，每类个体数依据种群动力学模型自动进行计算和调整，解决了人为确定个体数的难题。该算法拥有7个算子，其中竞争和突变算子分别实现种群内和种群间个体之间的信息交换；影响和毒害算子分别实现强壮个体的信息扩散和环境信息向个体传递；新生和死亡算子分别增加和减少个体数；生长算子可确保该算法具有全局收敛性；突变种群个体数的周期性增加，可大幅增加搜索跳出局部最优解陷阱的概率；在进行迭代计算时，算法每次只处理每个个体特征数的3/500~1/10，从而使时间复杂度大幅降低。测试案例表明，PDO-MCCE算法性能较好，适于求解一些维数较高的优化问题。

关键词: 群智能优化算法, 种群动力学, 环境污染, 微生物治理

HUANG Guangqiu, LU Qiuqin. Population Dynamics Optimization Algorithm Under Microbial Control in Contaminated Environment[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(11): 1956-1966.

黄光球，陆秋琴. 污染环境中微生物治理种群动力学优化算法[J]. 计算机科学与探索, 2020, 14(11): 1956-1966.

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