6 results about "Adaptive mutation" patented technology

The embodiment of the invention discloses a self-adaptive rapid non-dominated sorting genetic method and system, a storage medium and equipment. The method comprises the following steps: setting a population scale, a maximum iteration generation number and a generation channel parameter; selecting a preset number of individuals from the initial population as a parent population according to the individual fitness on the basis of the population scale and the generational channel parameters; determining a self-adaptive crossover probability and a self-adaptive mutation probability according to the non-dominated level and the congestion degree of the parent population; performing crossover variation according to the adaptive crossover probability and the adaptive variation probability in the progressive stage and the mutation stage; performing non-dominated solution sorting on individuals in the parent population after crossover variation to form a new offspring population; and determining a final filial generation population according to the non-dominated level and the congestion degree of the combined parent population and the new filial generation population until the maximum iteration generation number is reached, and outputting a non-dominated optimal solution set. And multiple crossover and mutation are carried out through the adaptive crossover probability and the adaptive mutation probability, so that the convergence speed is accelerated.

The invention discloses a micro spring washer production method based on genetic algorithm optimization, and the method comprises the following steps: S1, initializing a technological parameter set, and generating a preliminary optimization scheme through a morphological optimization control model; s2, mapping high-dimensional parameters to a low-dimensional morphological feature space through self-organizing mapping, and implementing adaptive variation adjustment; s3, integrating the processed morphological characteristics and technological parameters into chromosome codes, and performing multi-generation iterative optimization; s4, combining an evolutionary game model and fuzzy logic control, and dynamically adjusting a process parameter combination; s5, cooperatively regulating and controlling technological parameters through crossover and mutation operations; s6, performing fitness evaluation, and adjusting genetic algorithm parameters; and S7, after a convergence condition is reached, outputting an optimal process parameter, and applying the optimal process parameter to actual production. The method can significantly improve the production efficiency and the product quality, and is suitable for the precision manufacturing field of complex process optimization.

The application discloses a high-sensitivity parameter determination and optimization method of an enzyme-constrained metabolic network model. First, a sensitivity analysis method is used to sort parameters in the model according to contributions to comparative growth rate prediction results from high to low, and parameters with higher contributions are selected for optimization. Then, a differential evolution algorithm with an adaptive mutation strategy is used for parameter optimization. The algorithm can adaptively select a mutation strategy to improve optimization efficiency. Comparative growth rate data under different growth conditions are obtained from a database, and part of the training set is selected from the data. Finally, a performance evaluation result of the optimized model is obtained through a flux variability and phase plane analysis method. The high-sensitivity parameter is a turnover number kcat parameter of an enzyme.

The invention provides a Modbus TCP (Transmission Control Protocol) test case generation method based on genetic algorithm optimization. According to the method, the test effectiveness and efficiency are improved through a finely designed fitness evaluation formula and a self-adaptive variation mechanism. The fitness evaluation formula evaluates the fitness value of the individual from the three aspects of the distance between the individual and the group center test case, the abnormal code of the individual and the edge value coverage rate of the data bit of the individual. A self-adaptive variation rate mechanism is introduced, so that each offspring individual can be varied according to a certain probability on the basis of inheriting excellent characteristics of a parent. The variation mechanism not only retains excellent individuals, but also encourages generation of new excellent individuals, so that diversity of test case populations is effectively increased, and the capacity of discovering unknown errors and abnormities is improved. And the generation process of the fuzzy test case is adjusted by combining the feedback of the tested PLC, so that the test case which is more likely to trigger collapse is screened out.

The invention belongs to the technical field of intelligent control, and particularly relates to a multi-agricultural machinery task allocation method based on a directional adaptive variation strategy. The method is used for allocating m work tasks to n agricultural machines, taking a task allocation strategy as an individual, searching an optimal task allocation strategy based on a genetic algorithm, searching the optimal task allocation strategy by using the genetic algorithm, determining an individual needing to be mutated, and adaptively updating the mutation probability of each gene segment in the individual, and selecting the gene segments for performing mutation operation through the mutation probability, wherein the mutation probability of one gene segment is the sum of the set basic mutation probability and the accumulated contribution of the gene segments. According to the method, the optimal solution can be effectively searched on the premise of ensuring the event detection reliability, and the convergence speed of the algorithm is accelerated.

The application provides a test case adaptive mutation method based on double population crossover learning, comprising: compiling and inserting a program source code of a measured object; obtaining a data packet flow of a measured protocol and loading into a seed queue as a starting point of a fuzzy test process; adopting a test case adaptive mutation algorithm based on double population crossover learning, dividing seeds of each iteration into high-quality seeds and low-quality seeds according to quality degrees, respectively executing different mutation strategies, and expanding a global possible solution search surface; monitoring a system, a memory and a CPU survival state of a program, recording seeds causing abnormality of a target program or system crash, seeds generating new branch paths or seeds generating new state conversions; evaluating seed mutation strategies according to monitoring results, measuring and calculating quality degrees of seeds causing program abnormality, system crash, new branch paths or state conversions, increasing quality degree reward values of the seeds in an iteration process, and obtaining a test global optimal solution.