172 results about "Whale" patented technology

The invention discloses a method for solving low-carbon workshop scheduling based on an improved whale optimization algorithm. The method comprises the following steps: establishing a mathematical model of low-carbon workshop scheduling; setting algorithm parameters of the improved whale optimization algorithm, and generating an initial population; calculating the fitness value of the scheduling solution in the initial population, and keeping the current optimal scheduling solution; converting the current optimal scheduling solution into a whale individual position vector; carrying out whale individual position vector iteration updating by adopting an improved whale algorithm; performing whale individual position vector iteration updating on the updated whale individual position vector byadopting a self-adaptive adjustment search strategy; and when the number of iterations reaches the maximum number of iterations, converting the whale individual position vector into a scheduling solution, and outputting the scheduling solution. The whale algorithm is optimized, and a two-stage conversion mechanism is applied to initialize a machine part and a process part respectively, so that thenumber of iterations is reduced, and the quality and the operation efficiency of a final solution are improved; an improved whale algorithm is adopted, and the convergence speed and efficiency are improved.

The invention discloses a method for cloud manufacturing resource optimization configuration based on an improved whale algorithm, and the method comprises the steps: building a problem model, and defining a fitness function; setting improved whale algorithm parameters, and generating an initial population; Calculating fitness values of all individuals in the population, obtaining a current optimal resource allocation scheme and converting the current optimal resource allocation scheme into whale individual position vectors; Introducing a parameter p, and judging whether p is less than or equal to 0.5; If not, performing spiral motion iteration updating to complete population updating; If yes, whether the value A (1) of the coefficient vector of the improved whale algorithm is met or not is judged; If yes, performing shrinkage encircling iteration updating; If not, performing random search predation iteration updating; Obtaining a current optimal resource configuration scheme; Adding 1to the number of iterations, and judging whether the current number of iterations is smaller than the maximum number of iterations; If yes, repeating the operation; And if not, outputting the currentoptimal resource configuration scheme. The whale algorithm is improved, so that the algorithm convergence speed is higher, the optimal solution is easier to achieve, and a new method is provided forsolving the problem of resource allocation.

The invention discloses a recurrent neural network short-term power load prediction method for improving a whale algorithm, and relates to the technical field of short-term power load prediction. A recurrent neural network is used for short-term power load prediction, similar daily load data of a day to be predicted is used as input data of the recurrent neural network, and the number of input neurons, the number of output neurons, the number of hidden layers, the learning rate and the gradient descent algorithm of the recurrent neural network are determined. And a prediction model of the recurrent neural network is constructed. And the whale optimization algorithm is improved by using a differential evolution algorithm, so that the high-dimensional global optimization capability of a common whale algorithm is improved. An improved whale algorithm is adopted to pre-train the weight in the recurrent neural network, after pre-training is finished, the trained weight is put into a recurrent neural network model, then a gradient descent algorithm is adopted to train the recurrent neural network model, and after training is finished, a neural network model with the fixed weight is obtained, and then load prediction is carried out.

The invention discloses a method for solving flexible job shop scheduling based on an improved whale algorithm. The method comprises the following steps: 1) establishing a mathematical model of a flexible job shop scheduling problem; 2) setting algorithm parameters and generating an initial population; 3) obtaining a current optimal scheduling solution; 4) judging whether the current number of iterations is greater than the maximum number of iterations; if yes, outputting a scheduling solution; if not, judging whether the counter value of the current optimal individual is not smaller than a preset value or not; if yes, carrying out variable neighborhood search operation, and updating a scheduling solution; if not, converting the scheduling solution into a whale individual position vector,and retaining the whale individual corresponding to the scheduling solution; and 5) updating whale individual position information by adopting an improved whale algorithm, converting the whale individual position vector into a scheduling solution to complete population updating, adding 1 to the number of iterations, and returning to the step 3). According to the method disclosed by the invention,all optimal solutions of flexible job shop scheduling can be well solved, and the solving speed and precision are improved.

The invention relates to a biomimetic processing method for a leading edge of a blade at an end area, which belongs to the field of energy and power. In the method, bionics is combined with principles of controlling a horseshoe vortex, inhibiting the development of a boundary layer and reducing loss; and the characteristics that marine animals (such as dolphins, whales and the like) with streamlined bodies, particularly a streamlined head of a dolphin can effectively reduce resistance and the loss during advancement are applied to the leading edge processing of the blade of turbine machinery, namely, a dolphin head sharp area-shaped structure is formed at the leading edge of an end wall area so as to obtain a low-loss and high-performance blade; therefore, aerodynamic performance of a turbine is effectively improved, and the overall efficiency is improved. The biomimetic processing method not only can be widely used for turbines in the field of energy and power of aviation, aerospace, navigation and industries, and is also suitable for a confluence area of two flowing hard walls.

The invention discloses an image segmentation method based on improved whale optimal fuzzy clustering, so as to mainly solve the problems of serious loss and long segmentation time after image information segmentation in the prior art. The method comprises the realization steps: 1, an image is inputted and the gray levels of all pixel points are acquired; 2, c clustering centers are selected to segment the image into c classes; 3, n whales are generated, wherein each whale has a c-dimensional vector, which represents a possible solution of a set of clustering centers; 4, with the reciprocal ofa fast fuzzy C-means clustering objective function as a fitness value, the optimal clustering center is searched; and 5, according to the searched set of clustering centers corresponding to the maximum fitness value, image segmentation is realized, pixel points with the gray levels in the same membership grade interval are classified as one class, and an image after segmentation is outputted. Through combining the optimization result and the fuzzy clustering image segmentation, the image segmentation effects are improved, and the method can be used for target detection, video monitoring and medical imaging.

The invention belongs to the technical field of bearing vibration signal identification, and discloses a rolling bearing fault diagnosis method and system, a storage medium, equipment and application,and the method comprises the steps: collecting original signals of a bearing in four states, carrying out the signal decomposition through VMD, and obtaining all IMF components; extracting signal features by using multi-scale permutation entropy, constructing a feature vector set, and dividing the feature vector set into a training sample and a test sample; initializing a whale algorithm population scale, an iteration frequency and an adaptive weight value; establishing an LSSVM model by using the initialization parameters; calculating a fitness value corresponding to each whale, and sortingthe whale according to the fitness; carrying out neighborhood search by adopting a von Noemann topological structure, carrying out information exchange in a neighborhood, finding an optimal whale in the neighborhood, and carrying out position updating according to a formula; and outputting the whale position with the optimal fitness as the parameter of the LSSVM for training, and carrying out fault classification on the test set. The method is better in fault classification performance and higher in accuracy.

The invention discloses a wireless sensor network energy efficiency optimized clustering method based on a whale swarm algorithm. The method includes: a collection node respectively sends initial configuration information to all cluster head nodes and common nodes, and collects and acquires network information; optimal routing schemes and optimal clustering schemes from all the cluster head nodesto the collection node are obtained according to the current network information; clustering routing configuration is performed on a whole wireless sensor network according to the optimal clustering schemes and the optimal routing schemes; and the cluster head nodes perform data fusion on clusters which the cluster head nodes belong to and sends the data to the collection node, and information collection and acquisition can be completed. According to the technical scheme of the method, the improved whale swarm algorithm is introduced to the energy efficiency optimized clustering problem, multimodal optimization of the solution capability of the problem is realized by employing the improved whale swarm algorithm, and the energy efficiency optimized clustering problem of the wireless sensornetwork is solved; besides, a routing algorithm can effectively balance the energy consumption of the cluster head nodes during data forwarding so that the network life cycle is further prolonged.

The invention belongs to the technical field of underwater sound communication and particularly relates to a whale-sound-imitating covert underwater sound communication method based on self-adaptive interference cancellation. The method comprises steps as follows: a proper whale sound signal is selected, the time frequency characteristic of the whale sound signal is analyzed, and a proper time frequency interval is selected for spread spectrum signal concealment; digital information source information is modulated into a direct sequence spread spectrum signal, and the frequency range of the spread spectrum signal is required to be within the frequency band range of the whale sound signal; the spread frequency signal produced in step (2) and the whale sound signal selected in step (1) are summed up to form a bionic communication signal and the like. The whale-sound-imitating covert underwater sound communication method based on self-adaptive interference cancellation is simple, easy to implement, high in reliability and capable of guaranteeing the covert property and safety of communication information and well meeting the requirement of a covert underwater sound communication system.

The invention discloses a wind power prediction method and system for optimizing a deep Transformer network based on a whale group algorithm. The method comprises the steps of taking the sequence data of wind power and related influence factors as sample data, and dividing the sample data into a training set and a test set, establishing a Transformer network model according to values of the initialized hyper-parameters, respectively training and predicting data in the training set and the test set, and taking an average absolute error of wind power prediction as a fitness value of each whale group, determining a local optimal position according to the initial fitness value of the individual whale group, updating the current optimal position by utilizing whale group optimization, and obtaining the best prediction effect by comparing the local optimal solution with the global optimal solution, and obtaining an optimal hyper-parameter combination in the Transformer network after a plurality of iterations of the whale group algorithm, and predicting the wind power. According to the method, the optimization algorithm and the deep learning prediction algorithm are combined, so that the wind power prediction accuracy is greatly improved.

The invention discloses a big data intelligent search method and system based on a whale optimization algorithm. The whale optimization algorithm is utilized for optimizing and solving big data intelligent search problems, so that search results closest to user needs are obtained rapidly, and the accuracy and efficiency of intelligent search engines are further improved. Accordingly, a high-quality feasible solution of the big data intelligent search problems can be found within the acceptable time cost; according to search conditions of a user, big data related to keywords input by the user is obtained from an engine database, the user marks the search results continuously, and the search effect meeting the personalized requirements of the user is achieved gradually. The whale optimization algorithm is utilized for conducting big data intelligent search, one intelligent search engine is built on the basis of the body characteristics, the search results most conform to the requirementsof the user are found rapidly and efficiently, the problem that the current search engine cannot well provide interested information for the user is solved, and the search efficiency is high.

The invention discloses a hybrid whale algorithm optimized by a bionic strategy. The algorithm uses Chebyshev chaotic mapping to realize the population initialization of the whale group, and uses a new mathematical model to update the individual position during the search process, so that the convergence performance of the algorithm is optimized. . The hybrid whale algorithm optimized by a bionic strategy of the present invention is beneficial to avoid the pre-mature convergence problem of general optimization algorithms, and is conducive to jumping out of the local convergence trap.

The invention provides a cascade power generation and ecological balance optimization scheduling method and device, equipment and a medium. The method comprises the following steps: obtaining the annual runoff change of a river of the cascade hydropower station, calculating the ecological flow of the cascade hydropower station by adopting a ten-day-by-ten-day frequency method, and determining theupper and lower limits of the ecological suitable flow and the minimum and maximum ecological flows of the cascade hydropower station; setting an objective function and constraint conditions to establish a power generation and ecological balance optimization scheduling model; and optimizing a scheduling result according to the power generation and ecological balance optimization scheduling model based on an improved whale algorithm. The method can achieve organic unification of economic benefits and ecological benefits of the cascade power station, find out the optimal balance point of the power generation benefits and the ecological benefits, and effectively improve the ecological flow environment of the riverway under the stress of water conservancy projects. By changing the hydropower station scheduling mode, the water requirement of the river ecosystem is met to the maximum extent, the natural runoff mode of the river channel is well maintained, and the influence of human power generation requirements on river ecology is reduced.

The invention discloses a novel sliding mode prediction fault tolerant control method for actuator failure of a variable time delay control system. A sliding mode prediction fault tolerant control method based on whale optimization algorithm is designed for the problem of fault tolerant control of four-rotor systems with discrete time delay uncertainties. The whole-process sliding mode surface isused as a prediction model to ensure the global robustness, and a power function reference trajectory with failure compensation is designed to suppress the uncertainty and failure while reducing the influence of buffeting. In the process of rolling optimization, considering the requirement of high-precision and fast response of the optimization process, the whale optimization algorithm is adoptedto have strong optimization performance, less parameter setting, fast convergence and high precision. The novel fault tolerant control method provided by the invention is applied to robust fault tolerant control of a class of uncertain discrete systems with time-varying delays.

The invention relates to technologies for manufacturing large marine mammal skinned specimens and discloses a novel method for degreasing hides of large marine mammals, such as whales. The method comprises the steps of soaking, degreasing, liming, deliming, softening, pickling, tanning and retanning. According to the method, a degreasing operation runs through a whole hide tanning process; and adipose tissues can be removed from the hides greatly, a tanning agent is used for replacing soluble protein in skin and undertakes a medium of a connection structure, the stability of a collagen structure is improved, and the shrinkage temperature and hydrothermal resisting stability are improved, so that the aims of no oil spill corruption, no contraction distortion, long-term storage and healthy exhibition are achieved.

The invention discloses a throughput type multi-hanging-ball rising floating platform for collecting suspended solids in water bodies and belongs to the technical field of water body pollution treatment. With the platform adopted, whale preys on fishes and shrimps can be simulated. The relative movement between dynamic adsorption balls and the water bodies is used as a driving force, so that sunken deformation of a local area is formed, and therefore, nearby water bodies and suspended matters are absorbed to actively contact with the platform; the collection range of the suspended matters is expanded; the suspended matters are collected by utilizing adsorption and capture effects, meanwhile, a middle transmission rod is driven to move by utilizing the local deformation; the decomposition of hydrogen peroxide is catalyzed; the release of a large amount of oxygen serves as reverse boosting force, the local deformation area is forced to spit out the absorbed water bodies through reshapingmode; a water body swallowing and spiting process is repeated; and with the dynamic collection mode adopted, on one hand, the collection range of the suspended matters can be greatly enlarged, and onthe other hand, the collection effect and efficiency of the suspended matters can be improved.

The invention discloses a WOA-LSTM-MC-based hydrological time series prediction optimization method. The method comprises the steps of performing parameter optimization on part of parameters of a prediction model by using an optimized whale optimization algorithm; selecting flow data of the hydrological station to be predicted as experimental data; dividing the data set into a training set and a test set, and performing training and prediction; using a Markov chain MC for correction, so that a final hydrological prediction result, namely a more accurate prediction value, is obtained; and establishing a hybrid WOA-LSTM-MC (White Object Model Management Controller) model. According to the method, the optimal parameters required by the prediction model can be found more quickly and accurately, the global search capability and the local exploration capability of the algorithm can be ensured, and the convergence rate is high; and the prediction result is accurate.

The invention relates to a whale group PID independent variable-pitch control method for a large wind turbine unit. The whale group PID independent variable-pitch control method comprises the following steps of calculating the bending moment of all blades in the direction perpendicular to an impeller surface by means of a blade load model; conducting MBC coordinate transformation on the bending moment, perpendicular to the impeller surface, of all blades, and obtaining the pitching moment and the yaw moment in a coleman coordinate system; obtaining influence angles used for eliminating the pitching moment and the yaw moment through a whale group PID controller algorithm; and obtaining the pitch angle increment of all the blades, superimposing the pitch angle increment with the uniform variable-pitch angle, obtaining individual variable-pitch pitch angles of all the blades, and completing independent variable-pitch operation through a variable pitch driver. By optimizing the whale groupalgorithm, the good dynamic performance on the aspect of PID parameter setting precision and stability is achieved, the unbalanced load of the unit is effectively reduced, the vibration of the unit is reduced, more stable and smooth output power is ensured, and the life of the unit is prolonged.

A control parameter optimization method for a hybrid direct current transmission system based on a whale algorithm. For the fact that PI parameter tuning can greatly affect the performance of the system in a hybrid high-voltage direct current transmission system, the invention provides a novel parameter optimization method for a hybrid direct current transmission system. The method based on a whale optimization algorithm uses the integral ITAE of time multiplying the absolute error as an objective function to realize the PI parameter tuning with the restraint of an inequation. The method of the invention utilizes the interface technology between the PSCAD and the MATLAB to realize joint call to the PSCAD and the MATLAB, and utilizes the whale algorithm to search for the optimal control parameter, and uses the multiple running functions in the PSCAD to realize the multiple calls between softwares, thereby achieving the optimized search for the control parameter. Through modeling and simulation, the method is validated to improve the dynamic response performance of the hybrid direct current transmission system.

The invention discloses a network node selection method and system based on a whale optimization algorithm, and a storage medium. The method comprises the following steps: setting a binary coded population matrix, setting the maximum number of iterations and the initial number of iterations, and randomly initializing the population matrix; calculating the target function according to the node selection scheme corresponding to each whale individual in the population to obtain the optimal individual position and the optimal fitness function value in the population; calculating a current dynamicconvergence factor and a current dynamic weight according to the current number of iterations; determining a scheme for calculating the whale individual position according to the current dynamic convergence factor and the generated random number, and updating the current whale individual position and the number of iterations; if the number of iterations reaches the maximum number of iterations, returning to the optimal whale individual position, and determining sensor nodes participating in tracking according to the optimal whale individual position; otherwise return computation. According tothe invention, the tracking precision and the real-time performance in the target tracking process in the wireless sensor network can be improved.

The invention discloses a power system economic load distribution method based on an improved whale algorithm. The power system economic load distribution method comprises the steps of establishing a corresponding constraint condition expression according to the requirement of a power system economic load distribution problem in practice; establishing an objective function of the power system economic load distribution problem according to the constraint condition, and converting an actual application problem into a solution of a nonlinear programming problem; proposing an improved whale optimization algorithm, and performing optimization solution on the power system economic load distribution problem by utilizing the algorithm; and updating the position by adopting a self-adaptive weight strategy through the improved whale optimization algorithm, updating the position again by an individual through a random differential variation strategy, obtaining the final position before and after the change, and further obtaining the optimal result of economic load distribution of the power system. In order to improve the search capability of the whale optimization algorithm, an adaptive weight and differential variation strategy is introduced, so that the algorithm can perform global search in the early stage and perform accurate local search in the later stage.

The invention discloses an intelligent identification method for a hydroelectric generating set model. The intelligent identification method comprises the steps of: creating a corresponding identification system model according to a water turbine speed regulation system, acquiring an actual response signal outputted by the water turbine speed regulation system under the excitation of a given inputsignal, and acquiring an analog response signal outputted by the identification system under the excitation of the given input signal; defining a difference value between the actual response signal and the analog response signal as a target function, and performing iterative optimization on to-be-identified parameters by adopting a whale optimization algorithm to minimize the target function to obtain optimal identification parameters of the hydroelectric generating set; and increasing the global search probability by balancing random search and optimal search in the iteration process. According to the intelligent identification method, the global search probability is increased in the traditional whale optimization algorithm, immune operators are fused, the search space is adjusted by adopting a self-adaptive correction method, the optimization efficiency is improved, the intelligent identification method has the advantages of high convergence speed, short calculation time and high efficiency, and the identification precision is effectively improved.

According to the unmanned aerial vehicle three-dimensional path planning method based on the improved whale algorithm in the urban environment, threat area judgment is set, whether a path passes through the threat area or not is determined more accurately, a reasonable cost function is constructed, and the path with low energy consumption, high coverage and no threat becomes a better choice. By setting threat area judgment, whether the path passes through the threat area or not is determined more accurately, a reasonable cost function is constructed, and the path with low energy consumption, high coverage and no threat becomes a better choice. A convergence factor a is set to change with cosine of iteration times in the algorithm, levy flight disturbance is added in the iteration process,and an information exchange surrounding mechanism updates the rest individuals together through an individual historical optimal solution xmbest, a neighborhood optimal solution xlbest and a current iteration optimal solution xbest in algorithm iteration, so that the convergence speed and convergence precision of the algorithm are improved, and falling into a local optimal solution is better avoided.

The invention relates to a structure damage identification method based on IWOA, and the method comprises the steps: firstly, building an identification model of structure physical parameters, carrying out the coding of the identified structure physical parameters (mass, rigidity and damping ratio), and reasonably setting the searching range of the parameters; establishing a fitness function according to the actually measured power time history response data of the structure; and finally, optimizing the fitness function by utilizing an improved whale optimization algorithm, and searching an optimal solution. The improved whale algorithm effectively overcomes the defects that a traditional whale optimization algorithm is low in convergence precision and prone to falling into local optimization in the later iteration period. The structural physical parameter identification method provided by the invention is a time domain optimization method, namely, parameter optimization is carried outby directly utilizing structural dynamic time history response data, a modal analysis step in a frequency domain method is avoided, and the structural physical parameter identification method has good identification capability on structural quality, rigidity and damping and can be applied to structural model correction.

The invention provides a near field source direction finding method based on a quantum whale optimization mechanism. A near field source direction finding model is established; whales in a whale groupare initialized, a fitness function is constructed, the fitness of each whale quantum position is calculated, and a global optimal quantum position is determined; each whale selects one from quantumevolution rules including spiral update, a contracted bounding mechanism and random food searching according to the probability to update the quantum position of itself; the updated whale quantum position is mapped to the whale position, the fitness of each whale quantum position is calculated, a greedy selection strategy is used to select the quantum position, and the globally optimal quantum position is updated; and the globally optimal quantum position is output, and maximum likelihood estimated values of the corresponding azimuth and distance are obtained by mapping transformation. The method can be used to carry out high-precision range finding on coherent near field sources, and the quantum whale optimization mechanism is used to realize the high-precision range finding method.

A float (1) suitable for use as a buoy or as a component for a wave-powered vehicle. The float (1) includes an upper member (12) whose height can be changed and / or which remained substantially vertical even when the float is in wave-bearing water. A low drag cable (2) suitable for use as a tether in a wave-powered vehicle has a streamlined cross-section and includes a tensile member (21) near the front of the cross-section, at least one non-load-bearing member (22) behind the tensile member, and a polymeric jacket (23). Wave-powered vehicles having a float (1), a submerged swimmer (3) and a tether (2) connecting the float and the swimmer, include a means for determining whether the tether is twisted; or a means (91) for untwisting the tether; or a pressure-sensitive connection (71, 72, 73) which can disconnect the tether when the vehicle is dragged downwards by entanglement with a whale; or a 2-axis universal joint securing the tether to the float or to the swimmer; or elastic elements which absorb snap loads created by the tether; or two or more of these.

The invention belongs to the field of array antennas, and particularly relates to an array element failure correction method based on an improved whale optimization algorithm, which comprises the following steps: step 1, establishing a mathematical model for a linear array antenna, and constructing a fitness function according to the maximum sidelobe level and the beam width of the antenna; step 2to step 8; and step 9, outputting the optimal individual position. The method has the advantages that the whale optimization algorithm is improved, on the basis of the whale optimization algorithm, convergence of the algorithm is accelerated through the self-adaptive weight, the algorithm is combined with the differential evolution algorithm, population individual information is enriched, and theglobal convergence of the optimization algorithm is enhanced. Compared with intelligent optimization algorithms such as heredity and particle swarm, the convergence speed and convergence precision ofthe algorithm are improved. The improved whale optimization algorithm is used for optimizing the remaining normal array elements of the array antenna, failure correction of an array antenna directional diagram is rapidly achieved in a short time, and the performance of the array antenna is guaranteed to the maximum extent.

The invention provides a numerical control machine tool rolling bearing fault diagnosis method based on a whale optimization algorithm to optimize a variational mode decomposition algorithm, and the method comprises the steps: collecting original vibration signals of a bearing in a machine tool spindle box in four states, optimizing the variation modal decomposition algorithm by using whale optimization algorithm and using envelope entropy as a fitness function of the whale optimization algorithm to obtain parameter combinations (alpha and K) which are originally required to be set according to human experience, decomposing the original signals by using the optimized variation modal decomposition algorithm to obtain a plurality of intrinsic modal components IMF, selecting the IMF component with the most fault characteristics from the IMF components to carry out Teager energy demodulation, and finally comparing a Teager energy spectrogram with a fault characteristic theoretical value to judge whether the bearing has faults and the type of the faults. The problem that the number of the variation modal decomposition algorithm is required to be set manually in advance is solved, so that the result has more theoretical basis, the reliability is higher; and the intrinsic characteristic component is selected by using the envelope entropy, so that the accuracy and effectiveness of the result are improved.

The invention discloses a short-term wind speed prediction method and system based on an improved whale algorithm optimized ELM. The method comprises the steps: (1) obtaining the time series of various historical meteorological data of a wind power plant within a preset time range, and carrying out the preprocessing of the data; (2) analyzing the influence of each collected meteorological factor on the wind speed, calculating the weight of the characteristic quantity through the correlation degree obtained by grey correlation analysis, and taking the characteristic quantity with high correlation degree as input; (3) determining a network structure of the extreme learning machine and selecting an activation function; (4) adding chaos initialization and hill-climbing local search into the basic whale optimization algorithm, and adding inertia weight for improvement; and (5) establishing an extreme learning machine algorithm model based on improved whale algorithm optimization. According to the method, the technical problem that the wind driven generator cannot generate power according to the ideal wind power curve due to the uncertainty of the wind speed is solved, so that the technical effect of improving the short-term wind speed accurate prediction precision is achieved, and the utilization of wind energy resources by a wind power plant is improved.

The invention discloses a method for solving a low-carbon workshop scheduling problem on basis of a discrete whale algorithm. Firstly, a flexible job shop low-carbon scheduling problem model is established; a target function of flexible job shop low-carbon scheduling problems is established, the target function is directly taken as a fitness function of the discrete whale algorithm; an initial workshop scheduling solution set is generated with a mixed method and serves as an initial population of the discrete whale algorithm; fitness functions of all individuals in the initial population are calculated, the current optimal individual is found and stored, and parameters of the discrete whale algorithm are initialized; all population individuals are subjected to iteration updating; and fieldsearching operation is executed for the current optimal individual, the optimal individual and a fitness function value corresponding to the optimal individual are finally output, and the best scheduling scheme of the low-carbon workshop scheduling problem is further obtained. The problem about complicated optimization of multiple targets including energy consumption in the new sustainable manufacturing mode of low-carbon scheduling in the prior art is solved.