Immune Ant Colony Algorithm Research Articles

Optimal Scheduling of Pumped Storage Power Station Based on Immune Ant Colony Algorithm

At present, in the application of hydropower for multi-energy complementary, the utilization of large hydropower stations and pumped storage power stations is mainly considered. The utilization of small hydropower stations is relatively scarce. The use of small hydropower can implement complementary regulation of distributed energy nearby, which is very conducive to distributed power consumption. This paper proposes an improved genetic immune ant colony optimization algorithm to find the optimal solution. To solve the shortcomings of the ant colony algorithm in the early stage, such as the weak ability to traverse all paths, easy falling into a premature and local optimum, and slow convergence speed, a genetic algorithm is applied to initialize the genetic factor. It can dynamically adjust the step factor and introduce the genetic immune ant colony with a momentum coefficient when updating the position to search for the optimal solution. The penalty function is adopted to solve the model, and the specific steps are given. Through the comparative test of the algorithm, it is verified that the algorithm proposed has a better effect on the optimal solution, the shortest occupied time, good reliability, and strong optimization ability. It is of great practical significance to the scientific evaluation of the water resource’s eco-economic system.

Path Optimization of Medical Waste Transport Routes in the Emergent Public Health Event of COVID-19: A Hybrid Optimization Algorithm Based on the Immune-Ant Colony Algorithm.

In response to the emergent public health event of COVID-19, the efficiency of transport of medical waste from hospitals to disposal stations is a worthwhile issue to study. In this paper, based on the actual situation of COVID-19 and environmental impact assessment guidelines, an immune algorithm is used to establish a location model of urban medical waste storage sites. In view of the selection of temporary storage stations and realistic transportation demand, an efficiency-of-transport model of medical waste between hospitals and temporary storage stations is established by using an ant colony–tabu hybrid algorithm. In order to specify such status, Wuhan city in Hubei Province, China—considered the first city to suffer from COVID-19—was chosen as an example of verification; the two-level model and the immune algorithm–ant colony optimization–tabu search (IA–ACO–TS) algorithm were used for simulation and testing, which achieved good verification. To a certain extent, the model and the algorithm are proposed to solve the problem of medical waste disposal, based on transit temporary storage stations, which we are convinced will have far-reaching significance for China and other countries to dispatch medical waste in response to such public health emergencies.

Numerical simulation of hydrocarbon migration in tight reservoir based on Artificial Immune Ant Colony Algorithm: A case of the Chang 81 reservoir of the Triassic Yanchang Formation in the Huaqing area, Ordos Basin, China

The hydrocarbon migration in tight reservoirs is a complex process, the fluid flow patterns of which are notably different from those of conventional reservoirs. Therefore, specific mathematical models are needed to simulate the secondary hydrocarbon migrations. This study presents a numerical simulation method based on Artificial Immune Ant Colony Algorithm (AIACA) to simulate the secondary hydrocarbon migrations in tight reservoirs. It consists of three core parts: (1) the release modes of artificial ants based on the intensity of hydrocarbon generation; (2) the wandering patterns of artificial ants under the control of the dynamic field and the distribution of pheromones; (3) the updating modes of pheromones based on the changes in reservoir wettability. The simulation of secondary migration can be realized by the observing the dynamic movements and accumulations of the artificial ants. The method has been tested in the Chang 81 tight sandstone reservoir, which is part of the Triassic Yanchang Formation in the Huaqing Area, Ordos Basin in China, and proved to be successful in matching the current data in exploration and development.

Research on Ship Parts Nesting with Immune Ant Colony Algorithm

A kind of method with immune ant colony algorithm (IACA) was applied for ship parts nesting problems. Graphics coding and pretreatment method of ship parts were proposed. A method of combination and filling transformed ship parts nesting problem into orthogonal rectangular nesting problem. An automatic nesting system was realized by use of remaining rectangular matching method and orthogonal approach grafting algorithm. A specific example was given according to the proposed algorithm, the nesting results prove that it is feasible for ship parts optimization nesting problem with IACA.