10 results about "Local convergence" patented technology

The application relates to the technical field of path planning, in particular to a crowd-sensing cooperative optimization method and system based on evolutionary fusion two-stage mixing. The method comprises the following steps: constructing a multi-agent cooperative optimization model of a heterogeneous space based on a mobile crowd-sensing operation scene; adopting a stage-type evolutionary fusion strategy to deeply fuse MOPSO and NSGA-II, and constructing an EF-DH algorithm; using the EF-DH algorithm to perform unmanned aerial vehicle multi-target path planning based on the constructed multi-agent cooperative optimization model, including first-stage unmanned aerial vehicle cluster path optimization and second-stage ground operation personnel task optimization; and performing air-ground cooperative execution and dynamic re-optimization based on the path planning. The evolutionary fusion two-stage optimization algorithm fusing MOPSO and NSGA-II is constructed, and the global search capability and local convergence performance of the multi-target optimization problem are effectively improved.

This invention discloses a high-precision RTK positioning method based on an improved adaptive genetic algorithm-based satellite selection strategy, belonging to the field of satellite navigation and positioning technology. A subset of satellites with a specified number of satellites is selected to minimize the loss of the Position Accuracy Factor (PDOP) and satellite geometric volume. The satellite selection problem is transformed into a combinatorial optimization problem. A fitness function is designed with the reciprocal of PDOP and geometric volume as its core, and adaptive crossover and mutation probabilities are introduced. The genetic operation parameters are dynamically adjusted according to the population evolutionary state to balance global search and local convergence capabilities. Iterative evolution is performed under the condition of satisfying subset size constraints. When the convergence condition is met, the subset of satellites with locally optimal PDOP values ​​is output for subsequent RTK ambiguity resolution and positioning. This invention can quickly converge to a near-globally optimal satellite geometry configuration in polynomial time while also considering satellite quality, significantly reducing computational complexity and improving the real-time performance and reliability of RTK positioning.

This invention relates to an intelligent multi-objective optimization method for solving infeasible solutions in complex chemical process industrial applications. This method uses a classification model to screen infeasible solutions and performs targeted mutations to increase the likelihood of transforming infeasible solutions into feasible ones. The method uses classification as a data-driven model to accurately identify specific infeasible solutions and perform targeted mutations. Differentiated mutation operators are used for different types of solutions to ensure that the mutation direction matches the characteristics of the solution. For specific infeasible solutions, uniform mutation is used, constrained by the variable range of Pareto solutions, to correct the solution to the feasible region. This avoids the waste of traditional methods by generating high-quality Pareto solutions early on and avoids local convergence. Validated in dual-tower side-stream extractive distillation and four-tower extractive distillation systems, this method not only significantly improves computational efficiency, reducing optimization time by 35.3% and 20.8% respectively, but also outperforms widely used genetic methods.

This invention discloses an optimization method for mixed refrigerants in the cryogenic separation of coke oven gas, belonging to the field of chemical process engineering technology. A five-element mixed refrigerant refrigeration simulation model is established for the process of producing LNG and co-producing hydrogen from cryogenic separation of coke oven gas. The molar flow rates of each refrigerant component are used as optimization variables, and minimizing the cyclic compression work is the objective function. Constraints include the minimum temperature difference of the heat exchanger, the logarithmic mean temperature difference, and the refrigerant outlet temperature in the cold box. A genetic algorithm is used to perform a global search on the refrigerant flow composition to obtain an approximate optimal solution. Then, using this solution as the center, a composite method is used to achieve local fine-tuning optimization, ultimately outputting the optimal refrigerant ratio that satisfies the constraints. This invention overcomes the low efficiency problem of traditional empirical manual optimization methods, combining the advantages of global search and local convergence, effectively reducing process energy consumption, and has significant energy-saving and engineering application value.

The present application relates to the field of electric smelting magnesium electrode current tracking control, in particular to a kind of electric smelting magnesium electrode current variable domain fuzzy PID control method, for solving the problem of insufficient current control precision caused by nonlinear, time-varying characteristics in the process of magnesium electrolysis smelting, improve energy utilization efficiency and product quality stability.The method realizes the self-adaptive adjustment of fuzzy domain by constructing dynamic scaling factor function, improves the control precision under small deviation working condition, and combines with multi-population genetic algorithm (MPGA) to optimize the parameters of controller, overcome the local convergence defect of traditional single population genetic algorithm.The experimental results show that, compared with conventional control method, the present application significantly improves the key indicators such as regulation accuracy, response speed and anti-interference ability, can effectively deal with the complex working conditions such as raw material composition fluctuation and load disturbance of electric smelting magnesium furnace, reduce energy consumption and electrode loss rate.

The application provides a data processing method and device applied to agricultural machinery, electronic equipment and a storage medium, and the data processing method applied to agricultural machinery comprises the following steps: if it is detected that the agricultural machinery reaches a vibration optimization condition, an optimization algorithm is used to iteratively optimize filter parameters of the agricultural machinery to obtain target filter parameters; wherein the optimization algorithm fuses a Newton-Raphson algorithm and a t-distribution disturbance mechanism; and based on the target filter parameters, sensor signals of the agricultural machinery are processed to compensate for signal errors caused by vibration. Through fusion of the Newton-Raphson algorithm with fast local convergence capability and the t-distribution disturbance mechanism with global search capability, the optimal filter parameters suitable for the current working condition can be intelligently and efficiently found, the filter parameters can be dynamically adjusted, and then the signal errors caused by vibration can be compensated, so that the stability and accuracy of the sensor signals of the agricultural machinery in the vibration environment are effectively improved.

The application discloses a kind of engine model solving method combined Newton-Raphson and differential evolution method, in the initial stage of solving engine simulation model balance equation, using Newton-Raphson method to calculate, and whether it is judged to tend to diverge by divergence coefficient.If iterative process tends to diverge, then hybrid algorithm is switched to differential evolution algorithm to solve initial value again using Newton-Raphson method iteration, and divergence trend is judged.When iterative process tends to converge again, hybrid algorithm is switched to Newton-Raphson method.The above process is repeatedly carried out until the iterative error reaches the required accuracy to complete the calculation process.The method of the application combines the better local convergence and solving accuracy of Newton-Raphson method and the strong global search ability and robustness of differential evolution algorithm, and the convergence trend of iterative process is judged by introducing divergence coefficient reflecting the uniformity of independent variable error and error change trend to improve the convergence and convergence consistency of engine simulation model solving.

This invention belongs to the field of electronic interference technology, specifically relating to a method, program, device, and storage medium for allocating interference equipment. The invention designs a binary magnificent wren-warbler algorithm, which initializes the population's positional distribution using chaotic mapping. It judges the algorithm's local convergence trend by using the average Euclidean distance and fitness change rate. By introducing inertia weights and learning factors, the local search function is improved, enhancing the algorithm's local search capability. Combined with the algorithm's global search capability, a new fitness function is constructed to improve algorithm performance. Furthermore, activation functions and thresholds are used to convert continuous values ​​into discrete values, enabling the algorithm to solve discrete problems and enhancing its generalization and the rationality of interference equipment allocation. This invention solves the problems of existing interference equipment allocation methods' inability to respond quickly and to allocate interference resources rationally. Upon receiving a radiation source signal, this invention can immediately generate interference equipment allocation results, achieving rapid interference response.

A mobile edge computing offloading scheduling method based on predator swarm intelligence evolution. The method is a model strategy that optimizes time delay, energy consumption and cost together, which is closer to the cost consideration in the real scene. Chaos mapping is used to increase population diversity during population initialization, and then the marine predator algorithm combined with the differential evolution algorithm completes the scheduling of task offloading. The mutation advantage of the differential evolution algorithm is used to jump out of local convergence. Compared with other heuristic algorithms, the offloading scheduling method given here can effectively reduce the time delay and power consumption of edge computing offloading. The test results also show that MPA-DE can effectively reduce premature convergence and has good global search ability, and can efficiently handle more dimensional complex NP-complete problems.