Hydatid Disease Optimization Algorithm with Multistage Cross-Species Transmis-sion Characteristics

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.

Keywords

• population-based intelligent optimization algorithm
• epidemic dynamics
• hydatid disease optimization algorithm
• emission reduction scheme
• joint prevention and control