9results about How to "Improve local search capabilities" patented technology

The invention discloses a spectrum map construction-oriented sensing time resource allocation method, which belongs to the field of electromagnetic spectrum, and comprises the following steps of: establishing a time resource allocation model of a sensor; based on the timeliness index, setting an information age, and establishing a relationship between the information age and a service rate, a data arrival rate and a packet loss rate in a data transmission stage as a constraint condition of a time resource allocation model; converting a resource optimization problem in the time resource allocation model after setting the information age constraint into a convex optimization problem; solving a convex optimization problem by using a multi-strategy improved dung beetle optimization algorithm MSDBO to obtain an optimal time resource allocation scheme of three time periods of data acquisition Tc, channel detection Ts and data transmission time Tt; wherein the MSDBO improves the dung beetle optimization algorithm DBO by setting chaos pseudo-random, tangential flight search and crisscross strategies. Aiming at incoordination of communication and perception in the prior art, efficient allocation of communication time resources in a spectrum map construction process is realized.

The invention relates to an adaptive learning hyper-heuristic optimization method for a joint replenishment optimization problem, and the method comprises the steps: obtaining a to-be-optimized decision variable in a joint replenishment problem JRP model, determining a hyper-parameter and a sub-algorithm of an adaptive learning hyper-heuristic optimization model, and generating an initial sub-population individual according to the to-be-optimized decision variable; calculating the fitness value of each individual in the sub-populations and the updating times of the sub-populations, and updating the sub-populations; according to the historical information of the sub-populations, whether optimal individual communication among the sub-populations is carried out or not is judged; and generating a random number, judging whether individual migration is carried out among the updated sub-populations according to the random number, calculating the latest fitness value of the sub-populations after individual migration, and obtaining the current optimal individual, thereby generating the current optimal decision variable. The method comprises an adaptive learning strategy and a sub-population communication and migration strategy, has excellent global optimization ability and solving efficiency, and can significantly reduce the total cost when solving the JRP problem.

The invention relates to the technical field of scheduling management, and discloses a logistics scheduling management system based on big data, which comprises a data acquisition module, a data classification module, a scheme preliminary screening module, a scheme optimization module and an intelligent management and control module. Through the arrangement of the data classification module, the scheme preliminary screening module and the scheme optimization module, marked data categories can be obtained by classifying data in a real-time logistics data set, matching of scheduling content can be performed on the marked data categories, matching of unmarked data categories is not performed, the data calculation amount is reduced, the efficiency is improved, and the real-time logistics data scheduling method is suitable for being used in a logistics system. Meanwhile, the accuracy is improved, it is ensured that all scheduling contents conform to the latest regulation and policy contents, an alternative scheduling scheme set is formed based on the matched scheduling contents, a foundation is laid for subsequently obtaining an optimal logistics scheduling scheme, and a scheme optimization module combines a genetic algorithm and a bat algorithm, so that the scheduling efficiency is improved. Therefore, the optimal logistics scheduling scheme can be obtained from complex and changeable scheduling schemes.

The invention provides an active power distribution network low-carbon economic dispatching optimization method based on carbon-energy coupling, and belongs to the technical field of power distribution network dispatching. Comprising the following steps: constructing a double-chromosome model consisting of a carbon chromosome and an energetic chromosome; binding carbon-energy coupling constraint for the carbon chromosome and the energy chromosome, and initializing the double chromosome model; constructing a total cost function including an energy cost item and a carbon constraint penalty item; constructing a selection operator based on the total cost function, a crossover operator based on carbon violation feedback and a mutation operator of the genetic algorithm; constructing a local optimization function of the genetic algorithm; and performing iterative optimization on the double-chromosome model by using the optimized genetic algorithm, and obtaining an optimal scheduling scheme after iteration is finished. According to the method, through the double-chromosome model, the unified cost function, the adaptive genetic operator and the enhanced local optimization function, collaborative optimization of economical efficiency and low-carbon targets of the power distribution network can be realized, and a high-quality optimization result is provided for actual dispatching operation.

The present invention provides a method, system and device for scheduling offline maintenance tasks of IoT system equipment. In view of the problem of scheduling offline maintenance tasks of IoT system equipment, an improved symbiotic biological search algorithm with local random micro-mutation is proposed to solve it. Improved mutually beneficial symbiotic operations and improved partial beneficial symbiotic operations are proposed to improve the global search ability of the algorithm, and a local random micro-mutation strategy is proposed to enhance the local search ability of the algorithm. The present invention can effectively solve the problem of scheduling offline maintenance tasks of IoT system equipment, and has the characteristics of high efficiency, strong convergence ability, high precision, etc.

The present invention provides a method and system for predicting the state of charge of a power battery based on a combination of multiple intelligent algorithms. A first set of SOC data is obtained by using a lithium battery OCV-SOC test; a moth-to-flame algorithm is integrated with an artificial rabbit optimization algorithm, and an adaptive weight factor is introduced to obtain an improved artificial rabbit optimization algorithm; based on internationally public power battery charge and discharge data, the improved artificial rabbit optimization algorithm is used to optimize the support vector machine algorithm to obtain an improved support vector machine prediction model; the improved prediction model is used to predict the lithium battery OCV-SOC test data to obtain a second set of battery SOC data; the two parts of data are integrated, and the integrated data is trained using the improved support vector machine prediction model to obtain a new battery SOC prediction model; using the prediction model, the battery current, voltage and battery temperature data are acquired in real time, and real-time battery SOC data is predicted. The present invention can improve the accuracy and precision of the power battery state of charge prediction results.

A pulmonary nodule benign and malignant identification method based on an improved seagull optimization algorithm belongs to the field of lung cancer early screening in disease diagnosis, and is characterized in that the identification method comprises the following steps: S1, global search of collision avoidance weight factors based on index change; s2, local search based on dynamic reverse learning strategy improvement; s3, improving secondary local search of an Litalian mechanism based on mutual information so as to improve the local search capability of a sea gull optimization algorithm; and S4, population updating based on a multi-target fitness function. Compared with the prior art, the method has the advantages that the global search process and the local search process are better balanced, the local search capability of the sea gull optimization algorithm is enhanced, and radiomics features with high richness are selected by improving a traditional alteration mechanism.

The invention belongs to the related technical field of layout optimization, and discloses a layout optimization method and system for triangularly-arranged three-head laser cutting blanking. According to the method, the characteristics of three-head laser cutting are combined, after a part sequence is initialized, pre-partitioning of longitudinal trisection of a plate is firstly carried out, and then a heuristic positioning algorithm which is based on the leftmost and lowermost preferentially leftmost and searches for an optimal positioning point according to the variance of the contour perimeter of the part in the region is designed for part positioning. And finally, an improved genetic-taboo mixed sequencing algorithm is used for optimizing the layout sequence of the parts, and layout optimization of three-head laser partition cutting blanking is achieved through algorithm iteration. Through the three-head laser cutting blanking line, the number of times that parts are broken can be reduced while the calculation complexity is not increased, the cutting quality of the three-head laser cutting blanking line is improved, and the laser cutting efficiency is improved.

This invention discloses a distributed heterogeneous flexible assembly workshop scheduling method with transfer functionality. It includes the following steps: setting assumptions and basic parameters; proposing corresponding constraints based on the problem under study; constructing a bi-objective optimization model with the objectives of minimizing maximum completion time and minimizing total processing energy consumption. This bi-objective optimization model is used to optimize the flexible assembly workshop scheduling considering factory processing capacity, transportation stages, and workpiece transfer. Based on the process sequence, factory selection, and machine selection, a three-segment encoding and decoding mode corresponding to the bi-objective optimization model is designed. Based on the three-segment encoding and decoding modes, an improved multi-objective genetic algorithm based on Q-Learning is used to solve for the optimal scheduling scheme. This invention considers factors such as workshop processing capacity, collaborative processing, and transportation stage time, arranging the process sequence, factory, machine, and assembly workshop selection from a global optimization perspective.