Abstract:

To solve a class of highly nonlinear constrained optimization problems, a hydatid disease optimization algorithm is proposed using the model of hydatid infectious disease with multistage cross-species propagation. This algorithm regards the search space of an optimization model as a grassland pasture, where such individuals as dogs, sheep and herdsmen live; hydatidosis can spread across species from dogs to herdsmen through sheep. Operators such as Su-Su, Su-Eu, Eu-Eu, Eu-Iu, Iu-Iu, Iu-Ru, Iu-Du, Ru-Ru, Ru-Su are constructed by the echinococcosis model, among which Su-Su, Eu-Eu, Iu-Iu, Ru-Ru operators can use the characteristics of strong individuals to improve the characteristics of weak individuals, thus improving the algorithm??s exploitation ability; Su-Eu, Eu-Iu, Iu-Ru, Ru-Su operators can improve individuals??fitness distribution characteristics, thus improving the exploration ability of the algorithm; the Iu-Du operator can effectively remove extremely weak individuals, thus reducing the probability of the algorithm falling into local traps. The algorithm processes only a small number of variables in each evolution, and has good convergence speed and global convergence. The application case shows that the algorithm can quickly solve the optimal emission reduction problem of VOCs joint prevention and control in an associated area.

Key words: population-based intelligent optimization algorithm, epidemic dynamics, hydatid disease optimization algorithm, emission reduction scheme, joint prevention and control

摘要：

为了求解高度非线性有约束优化问题，采用跨物种多级传播特征的包虫病模型提出了包虫病优化算法。该算法将优化模型的搜索空间看成一个草原牧区，其中生活有狗、羊和牧民等个体；包虫病能够从狗群跨物种经羊群传播到牧民。利用包虫传染病模型构造出了Su-Su、Su-Eu、Eu-Eu、Eu-Iu、Iu-Iu、Iu-Ru、Iu-Du、Ru-Ru、Ru-Su等算子，其中Su-Su、Eu-Eu、Iu-Iu、Ru-Ru算子可利用强壮个体的特征来改善虚弱个体的特征，从而提升算法的求精能力；Su-Eu、Eu-Iu、Iu-Ru、Ru-Su算子可改良个体的适应度分布特征，从而提升算法的探索能力；Iu-Du算子可使极虚弱个体得到有效清除，从而降低算法陷入局部陷阱的概率。该算法每次演化只处理极少部分变量，具有速度快和全局收敛性。应用案例表明：该算法可快速求解关联区域VOCs联防联控最优减排优化问题。

关键词: 群智能优化算法, 传染病动力学, 包虫传染病优化算法, 减排方案, 联防联控