32 results about "Mutation probability" patented technology

The invention provides a remanufacturing workshop scheduling method based on reinforcement learning and evolutionary algorithm fusion, relates to the technical field of production scheduling optimization, and solves the problem of remanufacturing system scheduling in three stages of disassembly, reprocessing and assembly. A four-dimensional decision model covering process sorting, factory distribution, machine selection and speed gear scheduling is constructed, and an initial population with diversity and high-quality characteristics is generated by adopting a multi-strategy hybrid initialization method. A reinforcement learning decision module based on Q-learning is introduced, an optimal combination is dynamically selected from various crossover operators, mutation operators and neighborhood search strategies, the crossover probability and the mutation probability are dynamically corrected according to the distribution characteristics of the Pareto leading edge, and a dual escape mechanism is integrated to enhance the capability of the algorithm to jump out of local optimum. According to the scheme, the group search advantage of the evolutionary algorithm is exerted, the dynamic optimization of the search process is realized, and an efficient and accurate scheduling solution is provided for a complex remanufacturing system.

The invention discloses a hybrid learning-based flexible job shop energy-saving batch scheduling method, which relates to the technical field of shop scheduling and comprises the following steps of: establishing a flexible job shop energy-saving scheduling model by taking minimization of maximum completion time and total energy consumption of a machine as optimization objectives; the method comprises the following steps of: constructing a mapping relationship between an operation process and machine selection by adopting a three-layer coding mode, initializing a population through multiple initialization strategies, decoding a coding part, and generating an initial solution; constructing a neighborhood structure, performing population evolution through an adaptive crossover and mutation operator, and adjusting the crossover and mutation probability according to the population evolution degree; searching an optimal solution through a plurality of search strategies; traversing the population to perform non-dominated sorting, and adjusting the crossover mutation rate according to a non-dominated sorting result; and judging whether an iteration termination condition is met or not, if not, continuing iteration, and if so, outputting the optimal Pareto frontier solution. According to the invention, the energy consumption is reduced while the processing time is minimized.

The application relates to a house interior design selection method, a terminal device and a storage medium, and the method comprises the following steps: setting a chromosome code composed of a plurality of room codes and a room position code, setting the value interval of each room code and room position code; setting the maximum value and minimum value of each gene on all individual chromosomes; randomly generating an initial population; according to the input mutation probability and the crossover probability, the population is iteratively updated through the mutation rule and the crossover rule; drawing the corresponding house plan of each individual in the kth generation population before and after the crossover; when the selection result of the better house plan is received, the individual chromosome corresponding to the selected better house plan is used as the individual in the k+1th generation population. The application can enable the user to participate in the design process of the indoor plan in a timely manner, and improve the design efficiency and the user satisfaction.

This application discloses a sliding mode control method, device, equipment, and storage medium for a permanent magnet synchronous motor, belonging to the field of control technology. It includes: determining the objective function corresponding to M sets of optimized parameters included in the first-generation optimized parameters; determining the target set of optimized parameters included in the first-generation optimized parameters; determining the fitness of the M sets of optimized parameters based on the objective function corresponding to the M sets of optimized parameters; determining the crossover probability and mutation probability corresponding to each of the M sets of optimized parameters based on the fitness of the M sets of optimized parameters; generating the next-generation optimized parameters including M sets of optimized parameters based on the crossover probability and mutation probability corresponding to each of the M sets of optimized parameters; and so on, until the target set of optimized parameters including G-generation optimized parameters is determined; and controlling the permanent magnet synchronous motor through the sliding mode controller based on the target set of optimized parameters including G-generation optimized parameters. This application can improve the control effect of permanent magnet synchronous motors.

The invention relates to the technical field of artificial intelligence, in particular to a cue word automatic optimization method based on an evolutionary algorithm, and the method comprises the steps: generating an initial cue word population through analyzing a business demand, and verifying the validity; performing mutation operation of dynamic mutation probability adjustment on the current population to generate a filial generation candidate set; a standardized failure case set is used as an evaluation sample, and the combined population is subjected to test sorting; executing an elitism and tournament selection strategy based on a sorting result to screen high-quality individuals to obtain a next-generation population; judging whether a termination condition is met or not, and if yes, outputting a candidate optimal cue word; and carrying out automatic business verification on the candidate prompt words, and if the verification is not passed, automatically adjusting parameters based on failure reasons and triggering secondary iteration until the verification is passed. According to the method, directional optimization can be carried out on the business failure case, the evolution pressure is dynamically adjusted, the efficiency and accuracy of cue word optimization are improved, and automatic closed-loop iterative optimization of cue words is realized.

The application provides a high-efficiency fuzzy test method based on a score matrix, only normal basic blocks with a block score greater than 0 are subjected to a plug-in operation, and branches with a reaching probability of 0 are deleted through a pruning mode, which can prevent the fuzzy test of the application from generating new irrelevant test case inputs; therefore, it can be seen that the application avoids the fuzzy test tool of the application from exploring paths to irrelevant codes through the optimization mode; meanwhile, test cases in the seed pool of the application can trigger new paths reaching the target basic block or trigger vulnerabilities in the code to be tested, and the application selects a test case according to the final score and the mutation probability corresponding to each test case in the seed pool to perform mutation, and continues to test the vulnerabilities of the code to be tested according to the test case after mutation, so that the test case can reach the target basic block as much as possible through a high-score path, and the vulnerability mining efficiency of the software system to be tested is improved.

The application discloses an outlet SO2 mutation early prediction network and a feedforward probability fusion method, relates to the technical field of industrial process control and intelligent prediction, and comprises the following steps: obtaining outlet SO2 and related measuring point historical sequence data collected by a distributed control system, performing sliding window organization processing on the historical sequence data, forming a historical data matrix containing multivariate time sequences, and synchronously generating corresponding observation identification information. The application realizes early discrimination of mutation probability by introducing a feedforward variable and a working condition self-adaptive time lag alignment mechanism, drives prediction fusion of a smooth path and a mutation path, completes integrated output of prediction and early warning, improves adaptability by using physical time lag modeling and multi-scale alignment, enhances mutation response capability by rolling self-fusion and control plan injection, and realizes the transformation of prediction from history dependence to causal driving.

The invention discloses a mechanical arm trajectory planning method and system based on a hybrid particle swarm optimization, and the method comprises the steps: carrying out the interpolation of a multi-degree-of-freedom mechanical arm through a 3-5-3-degree polynomial, setting four key nodes, and obtaining the interpolation angle of each joint of the mechanical arm at the key nodes through inverse kinematics analysis; and taking the duration time of each section of track as particles, obtaining an optimal time distribution scheme by using a particle swarm algorithm, and generating a motion track of each joint on the mechanical arm based on the optimal time distribution scheme. In the speed updating process of the particle swarm algorithm, performing three-stage updating on the particle speed; in the first stage, an initial update speed is obtained according to the speed and position of particles; the second-stage speed obtains an intermediate update speed based on the adaptive crossover probability updated with the number of iterations; and in the third stage, obtaining the final updating speed based on the mutation probability.

The invention provides an airborne flight control system-oriented sparse coverage edge feedback-based efficient fuzzy test method, which comprises the following steps that: a fuzzy test system initializes a seed queue and constructs an initial probability distribution model for each seed, and the mutation probabilities of all byte positions are equal; the fuzzy test system tracks an execution path of a test case in real time in a variation process, and dynamically updates an enhanced control flow diagram to record edge hit times and path information; the fuzzy test system calculates a path fitness score based on the edge hit times and the path depth in the enhanced control flow diagram, adjusts a probability distribution model of seeds according to increments, and improves the mutation probability of high-contribution byte positions; and the fuzzy test system selects byte positions and mutation operators according to the adjusted probability distribution model to generate a new test case, and detects whether a new path is triggered to update the seed queue or not until a termination condition is met. According to the method, the abnormal condition of the detected program is efficiently, accurately and reliably detected, and it is guaranteed that security holes and defects of the airborne flight control system are found in time.

This invention discloses a mapping-guided fuzzing method for loop code, applied in the field of software testing. The method includes: continuously mutating test cases, inputting them into a test case queue, sending them to the program under test (CUPT) with instrumented loop code structures, and outputting a coverage statistics table when the fuzzing termination condition is met; training a deep learning model and back-calculating the weighted distance sum to construct and output a mapping relationship between the byte sequences of test cases and the coverage of the loop code structure; based on the mapping relationship, assigning mutation probabilities to the corresponding byte sequences in the test cases corresponding to the loop code structure coverage and performing mutations to generate child test cases, which are then used as input to the CUPT for fuzzing until the fuzzing termination condition is met, and outputting a fuzzing report. This method mutates the byte sequences of test cases based on mapping relationships, enabling the targeted generation of test cases that improve the coverage of loop code results.

The invention relates to a rectification system multi-objective optimization method based on dynamic NSGA-II, and belongs to the technical field of chemical process system optimization. The method comprises: determining a decision variable and a target function according to a rectification system model; a diversity index is obtained by calculating an invalid solution proportion of each generation of population and diversity of a target and decision space; and the crossover probability, the mutation probability and the population size are dynamically adjusted, a dynamic NSGA-II algorithm is constructed, the rectification system is optimized, and an optimal solution is obtained. According to the method, dynamic parameter adjustment is achieved by introducing the invalid solution proportion and the population diversity index, the number of non-convergent solutions can be effectively reduced, the population diversity is enhanced, premature convergence is avoided, the calculation cost is reduced while the optimization efficiency is improved, and an efficient and practical solution is provided for multi-objective optimization of a complex chemical system.

The application relates to a distributed cache parameter optimization method and device, computer equipment and a storage medium. The method comprises the following steps: randomly generating multiple groups of distributed cache parameters as an initial population; inputting the initial population into multiple learners of a cache performance prediction model respectively, predicting the cache performance of the initial population, and determining the health value of the initial population according to the cache performance of the initial population; performing evolution processing on the initial population according to the health value of the initial population, a mutation probability threshold and a crossover probability threshold to generate a new population; updating the new population as the initial population, returning to the step of inputting the initial population into the multiple learners of the cache performance prediction model respectively, and iteratively performing evolution processing to obtain a group of distributed cache parameters with optimal cache performance. The method can improve the efficiency and accuracy of distributed cache parameter optimization.

The application discloses a kind of based on the bending process planning method of improved NSGA-Ⅱ, step one, input processing information: including workpiece information, die library information, machine tool processing parameters;Step two, workpiece manufacturability check: check whether workpiece can be completed processing under existing bending equipment, if not, jump to step six;Step three, NSGA-Ⅱ initialization: set algorithm parameters, generate initial population;Step four, genetic evolution: the iterative process of strategy optimization algorithm using adaptive adjustment crossover mutation probability;Step five, result processing: according to the priority of optimization target from the pareto optimal solution set output in step four Optimal bending process is selected;Step six, output process scheduling results: if workpiece is manufacturable, output bending sequence, positioning sequence;Otherwise, output workpiece is not manufacturable.The application can obtain high processing efficiency bending process under the condition of meeting interference constraint, precision constraint and processing constraint.

The application provides a self-adaptive optimization method for process forming scheduling workshop process parameters, comprising the following steps: feature extraction and feature alignment are performed on multi-modal data of the process forming scheduling workshop to obtain feature vectors of the modal data; the contribution degrees of the modal data to the target are used to calculate the modal weight, and the feature vectors are weighted and fused; the process parameters and the processes are respectively coded into chromosomes, an adaptability function is constructed according to the weighted and fused feature vectors, the influence weight of the process parameters is calculated based on the sensitivity of the process parameters and the modal contribution degree, and the cross probability and the mutation probability of each parameter gene are adaptively adjusted according to the influence weight, so that the optimal process parameter and the process scheduling scheme are solved; the optimal process parameter and the process scheduling scheme are input into a simulation system and compared with online measured results, if the error between the predicted defect rate and the actual defect rate exceeds a threshold value, the step length of genetic search and the contribution degrees of the modes are updated and then re-iterated, otherwise, the optimal process parameter and the process scheduling scheme are issued to the production line for execution.

The invention relates to the technical field of soil detection, in particular to a soil harmful substance residue detection method, device and system, and the method comprises the following steps: comparing the difference between the spectral intensity under any channel and the distribution condition of all fluorescence intensities in the channel section where the channel is located; determining a spectral noise confidence coefficient by combining the mutation probability of the fluorescence intensity under any channel in each channel interval so as to screen out a spectral peak; and determining an asymmetric factor based on the difference between each spectral peak in the baseline adjacent region of each channel interval and all fluorescence intensity distribution in the corresponding channel interval and the spectral noise confidence coefficient, and carrying out baseline correction and denoising on the spectrum detected each time by adopting a denoising algorithm for soil harmful substance residue detection. According to the method, the asymmetric proportion in the BEADS algorithm is dynamically optimized, spectral noise is effectively inhibited, trace spectral peaks are reserved, and the precision of soil harmful substance residue detection is improved.

The invention discloses an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm and a forest coverage type classification method. The algorithm comprises the steps of population initialization, non-dominated sorting, fitness evaluation, tournament selection, crossover and mutation operation, elitist retention and termination condition judgment. According to the invention, based on the improved NSGA-II algorithm, simplified congestion distance calculation is adopted, so that the required convergence speed is improved by 30-40%, the calculation time is reduced, and meanwhile, through cooperation of the adaptive crossover probability and the mutation probability, dynamic adjustment can be carried out according to the population evolution state, global exploration and local development are effectively balanced, premature convergence is avoided, and the convergence efficiency is improved. And the precision of outputting the optimal solution is improved.

The present application relates to the technical field of extrusion production line beat optimization, more particularly to a kind of MOEA-based extrusion production line beat on-demand control resource optimization configuration method and device, comprising the following steps: S1.gene coding;S2.population initialization;S3.construct objective function: the optimization target of objective function includes balance loss rate, station loss index, smoothing index and production waste amount;S4.get fitness function by objective function and solve, if the current fitness function value obtained by solving is optimal solution, then output current population, otherwise execute step S5;S5.gene crossover and mutation, generate new population, then return to step S4;Wherein, the crossover probability and mutation probability of generating new population are calculated according to population and fitness function.This application can solve the resource optimization configuration problem of extrusion production line, can optimize the resources of production line according to actual production order demand, improve the balance rate and production efficiency of production line.

The application discloses a star selection method and system based on an improved adaptive genetic algorithm, and relates to the technical field of image positioning and registration. The technical points of the application comprise: preprocessing obtained basic star table data; inputting the preprocessed basic star table data into a selection model based on an improved genetic algorithm for selection; using a binary mode for coding and carrying out population initialization; calculating individual fitness according to a fitness function, and calculating the crossover and mutation probabilities through an improved adaptive adjustment strategy according to the calculated fitness values, and then carrying out selection, crossover and mutation operations according to the calculated crossover and mutation probabilities; decoding the genes and calculating constraint conditions; and outputting a star selection result when the maximum number of iterations is reached and all the pointing field information has been optimally selected. The application can select uniformly-distributed stars in the premise that the number of stars in a given imager field of view is known, so as to provide the imager with observation and correct the pointing of the imager.

The invention provides a multi-unmanned aerial vehicle task allocation method and system based on a multi-target blue whale hunting mechanism, and belongs to the field of unmanned aerial vehicle task allocation. The problems that an existing unmanned aerial vehicle multi-target task allocation method is prone to falling into local optimum, and later convergence precision is insufficient are solved. Segmented initialization is applied during initialization, initialization is performed according to task allocation in the first stage, so that an initial blue whale group is more reasonable, and a chaotic mapping idea is adopted in the second stage, so that initial distribution of the blue whale group has higher randomness and ergodicity; an elitism strategy is applied, excellent individuals of each generation are reserved, and the convergence speed is increased; a simulated annealing thought is introduced in each iteration process, so that a relatively high mutation probability exists in the initial stage of iteration to accept a relatively poor solution so as to jump out of local optimum; the blue whale positions are searched in a continuous space through continuous value coding, threshold conversion and an optimization algorithm, a blue whale group is guided to evolve to an excellent solution through fitness function feedback, and finally an actually executable task allocation scheme is obtained through mapping.

The invention discloses an inhaul cable parameter identification method based on an improved hybrid optimization algorithm. The method comprises the following steps: inputting structural geometric parameters and multi-order modal frequency; constructing a correction frequency model fusing tension, bending rigidity and boundary rotation rigidity; designing a weighted relative error objective function; performing global search by adopting an improved genetic algorithm (GA), and introducing logarithm uniform sampling and dynamic crossover / mutation probability; taking the GA optimal solution as an initial population, adopting an improved particle swarm optimization (PSO) to carry out local refinement, and introducing an adaptive inertia weight and random disturbance; and finally verifying the rationality of an identification result through a limit physical model. The method realizes high-precision synchronous identification of the cable force, the bending rigidity and the boundary rotation rigidity, has the advantages of fast convergence, high precision, strong robustness, low calculation cost and the like, and is suitable for health monitoring of structures such as bridge cables, wind power blades, cableways and the like.

The present disclosure provides an interface test method, device, computer device and storage medium, the method comprising: obtaining an initial test case corresponding to a to-be-tested interface from a database based on an interface identifier of the to-be-tested interface; determining a mutation probability and a mutation method of a target data node to be mutated in the initial test case; mutating the initial test case based on the mutation probability and the mutation method of the target data node to obtain a mutated test case; and testing the to-be-tested interface based on the mutated test case.

The present application is directed to the workflow scheduling problem of multi-user and multi-virtual server in mobile edge environment, and proposes a multi-user workflow task offloading decision and scheduling method based on genetic algorithm. Firstly, the offloading scheduling problem of multi-user and multi-virtual server workflow task is modeled, and the calculation expression of the total time delay and total energy consumption of the system is obtained on this basis. Then, by using genetic algorithm, the optimal execution order and offloading position of the workflow task are determined through coding, individual correction, elite selection, adaptive crossover and mutation probability operations. The method considers the workflow task offloading scene of multi-user and multi-virtual server, and can make optimal decisions on the execution order and offloading position of the workflow task through genetic algorithm, so that the total energy consumption of the system is minimized under the condition of meeting the time delay constraint. The simulation results show that compared with other several comparison methods, the system energy consumption can be effectively reduced.

The invention specifically relates to a semiconductor wafer manufacturing system scheduling method based on an adaptive genetic algorithm, and the method comprises the steps: building a time Petri net model according to the technological process and technological time characteristics of a semiconductor wafer manufacturing system, and determining an initial identifier, a target identifier and a transition trigger logic of the time Petri net model; initializing parameters of the genetic algorithm and an initial population; and the genetic algorithm is subjected to iteration of strategies such as crossover, variation and selection to finally obtain a scheduling result of the semiconductor wafer manufacturing system. In the solving process, in order to enhance exploration and avoid premature convergence, three adaptive strategies of adaptively adjusting crossover probability according to fitness bodies of parent individuals, adaptively adjusting mutation probability according to population diversity and selecting operators are provided, and global search and local development capabilities of the algorithm are balanced.

The application discloses a construction method of a radial six-pole hybrid magnetic bearing without displacement sensor control system, determines the structure of a BP neural network, initializes genetic parameters and coding length of the BP neural network, each individual in a population contains all weights and thresholds of the BP neural network, and determines a fitness function; the individual with the highest fitness value in each generation population is reserved as an elite individual and directly genetically passed to the next generation; the roulette method is adopted to select and operate the remaining individuals; adaptive crossover probability and adaptive mutation probability are calculated; optimal weights and thresholds are output after iteration; the BP neural network is trained; and a displacement prediction model is obtained; genetic algorithm is adopted to optimize initial weights and thresholds of the BP neural network, improve prediction accuracy and convergence speed, the selection operator is redesigned to avoid loss and damage of the best genes in the current population, and the convergence and global optimization ability are improved through adaptive adjustment of the crossover probability and the mutation probability.

The embodiment of the invention discloses a protein denoising method and device, a storage medium and a program product, and at least relates to technologies such as artificial intelligence. Through the method, the prediction precision of protein properties can be improved, and the generalization ability of the model in scenes of new protein and the like is enhanced. The method comprises the following steps: acquiring a to-be-processed protein sequence; the to-be-processed protein sequence is processed on the basis of a protein language model, a first evolution sequence including N evolution probabilities is obtained, and each evolution probability is used for representing the mutation probability of multiple amino acids; performing noise adding processing on a target attribute probability in the N evolution probabilities and the T time steps in a sorting space to obtain a second evolution sequence, and performing denoising processing on the second evolution sequence and the to-be-processed protein sequence in a likelihood space to obtain a target attribute sequence corresponding to the target attribute probability, the method is used for predicting the property of the to-be-processed protein sequence.

The application discloses a motor rotating speed loop ADRC controller parameter optimization method and relates to the field of swarm intelligence optimization algorithm. The adaptive genetic algorithm is applied to the rotating speed loop of a motor for simulation experiment. The current loop of the motor adopts PI control. The bandwidth of the current loop is adjusted by adjusting parameters to meet the control requirement of the rotating speed. The ADRC control is adopted for the control of the rotating speed loop. The ADRC control simulation is realized by building and writing S-function in Matlab / Simulink. The adaptive genetic algorithm includes crossover and mutation operations. The fitness of individuals in the population is taken as the reference index of the crossover probability and the mutation probability, so as to screen out excellent individuals. The application can adaptively adjust the parameters in the genetic algorithm. The selection of the crossover probability and the mutation probability has a great effect on the accurate global optimal search of the genetic algorithm.

This invention discloses a genetic programming mutation probability optimization method based on the maximum mutual information coefficient. In traditional genetic programming, feature selection tends to shift from initial completely random selection to selective selection with bias. This invention constrains the search direction of genetic programming, thereby improving search efficiency. The method includes: Step S1, using the maximum mutual information coefficient to measure the correlation between each feature and the target in the dataset, and merging the correlations of each feature and the target into a correlation vector; Step S2, determining the probability distribution of genetic programming when selecting new features through mutation based on the correlation vector; Step S3, performing genetic programming evolution, during which the probability distribution remains fixed and is unaffected by the number of features selected within the population. This invention reduces the impact of random initialization on the overall performance of genetic programming, enhances the robustness of genetic programming on fundamental problems, improves training efficiency and accuracy, and enhances model generalization performance.

A five-axis cutter feeding and retracting method based on a genetic algorithm belongs to the technical field of numerical control machining and comprises the following steps: step 1, discretizing a workpiece space; defining a cutter as a cylinder, and segmenting a cutter path by using a linear sampling method; 2, gene coding processing, wherein the chromosome of each individual is coded into a path point sequence; 3, performing population initialization processing, randomly generating Group Size individuals, and calculating and selecting the Group Size individuals with relatively high fitness to form an initial population; 4, performing gene recombination treatment based on individual fitness; 5, performing gene mutation, namely performing mutation on individual genes according to a mutation probability; 6, natural selection is conducted, population individual fitness is ranked, and a population is reformed; and carrying out iterative solution on the step 4, the step 5 and the step 6. According to the scheme, tool retracting can be achieved at any position, the optimal tool retracting track is automatically selected, collision is prevented in the tool retracting process, and the tool retracting efficiency can be improved.

The application discloses a kind of distributed wind power planning method, belong to wind power generation technical field.The method includes: generating wind speed random vector from the probability distribution of wind speed, generating load random vector from the probability distribution of load;Acquire power system network and element parameter, generate the sensitivity matrix of node power to line power;Obtain the number of chromosomes, hybridization probability and mutation probability;Initialization, produce predetermined number of chromosomes, and the maximum installed capacity of wind turbine is calculated according to wind speed random vector according to wind turbine power calculation rule;Genetic and mutation operation are carried out to chromosome, and the feasibility of offspring is tested according to each constraint condition;Calculate the target value of all chromosomes;According to target value, the fitness of each chromosome is calculated using sequence-based evaluation function;Spin wheel bet, select chromosome;Get satisfactory solution;Repeat the foregoing multiple times, obtain optimal solution;According to objective function, the maximum installed capacity of system acceptable wind farm is predicted.

The present application belongs to the technical field of smart grid, and particularly relates to a multi-target energy optimization management system based on a light storage hydrogen micro-grid, comprising a multi-target data acquisition module, a multi-target fluctuation quantification module, a fluctuation degree iterative correction module, a self-adaptive parameter adjustment module and an optimal solution set optimization module; multi-target time sequence data of the micro-grid is acquired; the multi-target fluctuation degree of each time node is calculated according to the time sequence data; the multi-target fluctuation degree is corrected in combination with the differences in the number of leading points and the crowding distance of algorithm iteration; the crossover probability parameter and the mutation probability parameter are self-adaptively adjusted based on the multi-target fluctuation degree of the next iteration after correction; and the multi-target genetic algorithm is run by using the adjusted parameters to obtain an optimal solution set and execute it. The present application solves the problems of poor optimization stability and easy falling into local optimization caused by fixed probability parameters, and significantly improves the accuracy, optimization efficiency and operation stability of micro-grid energy scheduling.