No abstract

Firefly algorithm has been shown to yield good performance for solving various optimization problems. However, under some conditions, FA may converge prematurely and thus may be trapped in local optima due to loss of population diversity. To overcome this defect, inspired by the concept of opposition-based learning, a strategy to increase the performance of firefly algorithm is proposed. The idea is to replace the worst firefly with a new constructed firefly. This new constructed firefly is created by taken some elements from the opposition number of the worst firefly or the position of the brightest firefly. After this operation, the worst firefly is forced to escape from the normal path and can help it to escape from local optima. Experiments on 16 standard benchmark functions show that our method can improve accuracy of the basic firefly algorithm.

In this paper, the operation process of cleaning of intelligent rice–wheat combine harvester is divided into two key steps: initial setting of cleaning device operation parameters and dynamic control of cleaning device operation parameters. Combined with the operation experience of cleaning control of agricultural machinery operators, the dynamic control knowledge-based system of cleaning device operation parameters was built based on production rule reasoning. The cleaning device of the rice–wheat combine harvester is intelligently controlled based on the dynamic monitoring and control system of the cleaning device operation quality and operation parameters, so as to achieve the purpose of controlling the cleaning operation quality of the rice–wheat combine harvester in the normal range. Through the field experiment results and analysis, it is proved that the intelligent control system of the cleaning device operation parameters based on the dynamic control knowledge-based system of cleaning device operation parameters can effectively keep the cleaning impurity content and loss rate of intelligent rice–wheat combine harvester in the normal range, so as to verify the effectiveness of the intelligent control knowledge-based system of the cleaning device operation parameters.

The water quality of urban inland rivers is an important index of urban environmental health, which can reflect a city’s development level and its social and economic development. The water quality of these rivers strongly impacts the health and quality of life of the residents of urban and surrounding areas. Therefore, it is necessary to accurately assess the quality of water in urban inland rivers, which can also aid environmental protection departments in providing river governance. Generally, the water quality status of a city’s inland rivers is assessed and released by environmental monitoring stations in various regions that deploy the corresponding water quality detection equipment at certain major locations of the river. However, these detection devices can only detect water quality at fixed locations, and often, the water quality of an urban inland river changes owing to the impact of the surrounding environment and residents it serves. Therefore, the water quality around a detection point does not always reflect the water quality of the entire river section. To better express the water quality status of a city’s inland river, we propose a method based on a mobile crowd-sensing system that obtains the water quality data of the river during an entire period of time and then fuzes these sensing data to obtain the best truth-value estimate of the water quality of the river. We can use this water quality truth value to conduct an objective evaluation of the water quality of a city’s inland rivers. The water quality parameters obtained by the method can better represent the water quality status of the river, and the data are more accurate compared to the data collected and released by an environmental monitoring station. Through simulation and comparative analysis, we found that the water quality data obtained by the proposed method were more accurate, indicating that our method has more practical value than the detection device method.