107 results about "Harmony search" patented technology

The invention discloses a distributed power source contained power system multi-target reactive-power optimization method in the field of power system reactive-power optimization. The technical scheme includes: 1, deducing a model of a wind-driven generator in power flow calculation; 2, initializing power grid parameters and grid-connected parameters of a distributed power source; 3, constructing an individual vector formed by system reactive-power optimization control variables, and initializing species; 4, performing the power flow calculation according to the initialized species and grid parameters after grid-connection of the distributed power source, and calculating objective function values; 5, performing multi-target optimization by utilizing the harmony search hybrid algorithm based on artificial bee colony; and 6, finishing the optimization process and outputting optimized results. The distributed power source contained power system multi-target reactive-power optimization method is a hybrid optimization algorithm ABS-HS which integrates the advantages of global search of the artificial bee colony (ABC) algorithm with local search of the existing harmony search (HS) algorithm, so that efficiency and robustness of the algorithm are improved.

The invention discloses a central air-conditioning modeling and controlling strategy on the basis of demand response, which comprises the following steps of building a room model, namely the relation between the indoor temperature and outdoor temperature of the room and sensible heat cooling load; building a center air-conditioning model suitable for the system operation of the demand response, namely the relation between air conditioning load and a decision variable; determining a controlling strategy, namely making an objective function, a control variable, a constraint condition and the like clear; solving the objective function by adopting a harmony search algorithm combining with a state space truncation method; obtaining a result, and performing analysis and treatment on the result. According to the central air-conditioning modeling and controlling strategy on the basis of the demand response, through meticulously building the models, making the strategy of multiple control variables cooperative optimization clear, scientific theoretical support is provided for the central air-conditioning to participate in the system operation of the demand response.

The invention discloses a short-term power load forecast method which comprises the following steps of: 1, selecting 40 days of load data and weather data before the current moment as training sample data and forecast sample data; 2, preprocessing the sample data, and normalizing all the data to be between 0 and 1; 3, selecting parameters (gamma and sigma) as harmonic vectors, and calculating new harmonics (gamma' and sigma') by using a harmonic search algorithm; 4, calculating a target evaluation function value, and determining the harmonic vector corresponding to the maximum target evaluation function value and; 5, updating the iterative frequency k=k+1, and judging whether k belongs to NI; and 6, substituting the optimal harmonic (gamma0 and sigma0) into a least squares support vector machine model, training by the training sample, and further forecasting the load. According to the method, the load forecast precision is effectively improved.

The invention provides a multi-objective community detection method based on spectrum information of a modified common neighbours matrix, which mainly solves the problems of existing community detection methods that the resolution is low and time complexity of large scale networks is high. The detection method is achieved by the steps of constructing a common neighbours matrix and modifying the matrix according to a network; extracting spectrum information used for modifying the common neighbours matrix; initializing a parent memory bank by using the spectrum information, and calculating the fitness of the parent memory bank; generating a descendant memory bank from the parent memory bank through a harmony search arithmetic, and calculating the fitness of the descendant memory bank; combining the parent memory band and an descendant memory bank, and performing non-dominated sorting to obtain a temporary memory bank; performing partial learning about the temporary memory bank to obtain an updated temporary memory bank, and acquiring a parent memory bank in the next iteration from the updated temporary memory bank; and if maximum iterations are reached, extracting all non-dominated solutions from the parent memory bank as a final solution set, otherwise iterations continue. The multi-objective community detection method based on spectrum information of the modified common neighbours matrix has the advantages of being high in resolution of community detections and capable of reducing time complexity.

The invention provides a fault-tolerance and distributed power supply contained power distribution network fault locating method. According to the invention, based on the fault current information uploaded by an FTU (power distribution network feed line terminal) or a fault indicator, the switch fault current information and the line state information are encoded, the line state information is converted into corresponding switch fault current information by defining the switch functions, the advantage and disadvantage degrees of each solution are evaluated by means of target functions, and the optimal solution, namely, the fault position corresponding to the information of the FTU or the fault indicator, is searched by means of a harmony search algorithm. In order to improve the fault positioning speed, the power distribution network is divided into an active branch and a passive branch, and the passive branch having no fault current is not considered during the positioning, the dimension of the solution in the algorithm is shortened, and the positioning efficiency is improved. The method is suitable for the distribution automated power distribution network.

The invention provides a photovoltaic power generation system maximum power tracking method and apparatus considering the factor of haze. The method includes the following steps: a. acquiring photovoltaic cell open-circuit voltage, short-circuit current, and cell temperature, and PM 2.5 concentration in air; b. inputting acquired data into a BP neural network prediction model improved based on a harmony search algorithm to obtain a MPP voltage predicted value; c. adjusting the PWM control signal duty factor of a DC-DC converter through a PI controller to enable the actual work voltage of the photovoltaic cell to reach the MPP voltage predicted value; and d. taking the MPP voltage predicted value as an initial value and tracking the maximum power of the photovoltaic cell at a specified disturbance step by means of a perturbation and observation method. The invention further provides a corresponding tracking device. Through the combination of the perturbation and observation method and the model forecasting method and the full consideration of the influence of haze weather to photovoltaic generation, the MPPT tracking precision can be effectively improved, the power loss of a static state is reduced, and the MPPT tracking speed can be improved.

The invention discloses a power system multi-target differentiation planning method based on an improved harmony search algorithm. The method is based on the differentiation planning basic principle and requirement, economical efficiency and reliability are quantized according to additional cost and lost benefits in the whole life cycle, and an optimized multi-target differentiation planning model is built; an optimizing criterion is formulated according to the Pareto dominance relation, and an optimal differentiation planning scheme is selected from a Pareto solution set with the maximum benefit-cost ratio as the goal. The newly-developed harmony search algorithm is applied to solution of the model, chaotic mapping, dynamic parameter setting, improved pitch adjustment strategies and a harmony optimizing information sharing mechanism are introduced to solve the 0-1 planning problem, and search performance is improved. According to the method, economical efficiency and reliability can be comprehensively given into consideration, multi-target differentiation planning of a power grid is achieved, and the method is of guidance significance in building a core backbone net rack and building a strong power grid.

The invention discloses a hyper-heuristic algorithm based ZDT (Zero Dead Time) flow shop job scheduling method. According to the invention, an objective function of a ZDT flow shop job scheduling problem is set firstly and a corresponding scheduling optimization model is established. On the above basis, a hyper-heuristic algorithm frame is combined, a harmony search algorithm applied widely is adopted as an HLH (High Level Heuristic) strategy of the hyper-heuristic algorithm, and a simple heuristic rule is designed aiming at the characteristics of the ZDT flow shop job scheduling problem for constructing an LLH (Low Level Heuristic) method set, so that the optimization solution of the ZDT flow shop job scheduling problem is realized. According to the invention, good global optimization performance of a meta-heuristic algorithm is remained and uncertainty caused by algorithm parameter adjustment depending on artificial experience in the meta-heuristic algorithm is avoided, so that the algorithm design efficiency is improved effectively and the method is significant to the improvement of flow shop job scheduling efficiency.

The invention discloses a wind and photovoltaic complementation generation system optimization configuration method. The method comprises steps that, correlation between wind generation and photovoltaic generation is calculated to acquire wind and photovoltaic generation output joint probability distribution; influence of a wind speed and illumination on generation and energy storage battery life factors are comprehensively considered, and an optimization function taking lowest system generation cost as a target and a load power loss rate as a constraint condition is established; twice optimization is carried out by employing an improved harmony search algorithm, a needed wind generation power value, a photovoltaic generation power value and a theoretical energy storage battery capacity value are acquired through first optimization; the wind generation power value is adjusted to be a rated wind generator power existing in a market, and the target function is re-optimized to acquire the photovoltaic generation power and a final energy storage battery capacity target value which satisfy the constraint condition. Through the wind and photovoltaic complementation generation system optimization configuration method, a convergence speed in a solution process can be accelerated, secondary optimization satisfies practical demands, the optimization result is that power supply reliability can be improved, and system establishment cost can be reduced.

The invention relates to a tracing method for river sudden water pollution events. The tracing river is abstracted into a two-dimensional river channel; assuming that the shape of the two-dimensionalriver channel is regular, according to the technical scheme, data such as the degradation coefficient of river pollutants, the average speed of transverse and longitudinal water flow and the transverse and longitudinal diffusion coefficients of the pollutants in a water body are fully considered; diffusion simulation of pollutant emission is carried out on an appointed river area; calculation isconducted according to an observation value obtained through fixed-point monitoring and a calculation value in actual diffusion, quantitative solution is conducted on the number, the discharge position and the discharge intensity of pollutant source points through the harmony search algorithm fused with the local search, and finally the tracing method for the river sudden water pollution event isdetermined. The method has the characteristics of openness, flexibility, low calculation complexity and the like.

The invention proposes a uncertainty based reconstruction method for an active power distribution grid. The method comprises the following steps of: building a multi-target reconstruction mode with minimum grid loss and cost; processing a load parameter of the mode by using an interval analytic hierarchy process; and acquiring the optimal solution of the multi-target reconstruction mode by combining a binary particle swarm optimization with a harmony search algorithm containing a distribution grid (DG), wherein the optimal solution comprises a switch state, and the position and the output power of the DG.

The invention relates to an integrated search method for large-scale flexible job shop scheduling. The integrated search method comprises the following steps that: 1) a solution vector of FJSS (flexible job shop scheduling) to be solved is defined; 2) a solution vector in hybrid harmony search is converted into the codes of a feasible solution of the FJSS; 3) a hybrid harmony search algorithm is adopted to perform harmony search; 4) the solution vector of the harmony search is converted into the scheduling solution of an FJSP problems, and corresponding neighborhoods are designed based on the scheduling solution, and local search is performed through using the designed neighborhoods, and an improved scheduling solution which is obtained through the local search is converted into a solution vector, and the solution vector is utilized to replace the solution vector before the local search, and the search process of the harmony search is continued, so that an optimal solution can be obtained; and 5) the optimal solution which is found based on the hybrid harmony search algorithm is adopted as the initial solution of a large-scale search algorithm, and the large-scale search algorithm is adopted to continue search until a termination condition is satisfied, and scheduling of which the solution is completion time can be obtained.

The invention provides a routing method of a small-scale wireless sensor network, belongs to the field of routing of a wireless sensor network, and solves the problem that a Harmony search algorithm in the prior art cannot be directly used for solving the routing of the wireless sensor network. The routing method comprises a step of transmitting global information, a step of sending data packet, a step of transmitting the data packet and a step of updating rest energy information. Through improving the encoding manner and the candidate Harmony generation method of the existing Harmony search algorithm, with the consideration of both path energy consumption and path length, the energy consumption of the whole network can be balanced effectively, and the service life of the whole network is prolonged significantly.

The present invention relates to a medical image segmentation method and belongs to the image processing field. The method of the present invention is based on a harmony search algorithm and is realized by the steps of pre-processing an image, extracting the characteristics of the image, pre-segmenting the image, carrying out the global optimal harmony search, carrying out the optimal harmony search in partial areas of the image, etc. The theory and practice prove that the method of the present invention properly solves the problems of a single objective function and a single threshold value and the problem of not considering the clustering effect of the components, in a clustering algorithm. After adopting the method of the present invention, not only the medical images can be segmented efficiently, but also the important image characteristics do not lose, so that the high-quality medical segmentation images can be obtained, and the diagnosis reading demands of the medical care personnel are satisfied.

The invention relates to a production and transportation cooperative scheduling method and a production and transportation cooperative scheduling system based on improved harmony search. The production and transportation cooperative dispatching method comprises the steps of: 1, performing group batching on work pieces; 2, setting algorithm parameters; 3, generating an initial harmony library; 4, calculating an adaption degree value; 5, acquiring a negative ideal solution of the harmony library; 6, generating a new harmony; 7, performing harmony library searching operation on the new harmony, and updating the new harmony; 8, comparing the new harmony with the negative ideal solution, if the new harmony is superior to the negative ideal solution, substituting the negative ideal solution and updating the harmony library; 9, updating a global optimal solution; 10, and judging whether a termination condition is satisfied, if so, outputting the global optimal solution, otherwise, returning to the step 4. The production and transportation cooperative scheduling method and the production and transportation cooperative scheduling system can realize the optimization of overall efficiency of the supply chain, thereby reducing cost of the supply chain, and enhancing customer satisfaction level.

The invention discloses a wireless sensor network clustering routing method based on a harmony search algorithm. The method comprises the following steps: (1) global information transmission and convergence: acquiring the global information, and transmitting the global information to an aggregation node; (2) the aggregation node performs optimization calculation and configuration of network clustering routing of the entire sensor network through the global information; (3) a common sensor node monitors, collects and processes application information, processes the application information into a data packet, and forwards the data packet to a cluster head node, and the cluster head node performs data fusion and sends the data packet to a next hop cluster head node; and (4) the next hop cluster head node adds its own dump energy information to the data packet after receiving the data packet, and continues to forward the data packet to the next hop cluster head node according to the optical routing in the data packet. By adoption of the method disclosed by the invention, the problems of unreasonable member node allocation, uneven power consumption of the cluster head nodes of a wireless sensor network are solved, the energy efficiency of the network is improved, and the life cycle of the network is prolonged.

The invention discloses a strawberry image segmentation method base on an integration strategy harmony searching algorithm. The tone disturbance operation of the harmony searching algorithm is integrated with three kinds of tone disturbance strategies; and thus advantage complementation of the three kinds of tone disturbance strategies are utilized fully. During the searching process, a tone disturbance strategy suitable for a current evolution state is selected adaptively according to feedback information of an adaptive value. In addition, the local searching operation is executed by using mean value information of a harmony base, so that the strawberry image segmentation is accelerated. Therefore, the segmentation speed of the strawberry image is increased and the segmentation precision is improved.

The invention discloses a prediction method for power generating system output power of a grid-connected type photovoltaic power station in the technical field of photovoltaic power generation. The prediction method comprises the following steps of: firstly constructing a sample set: denoising the sample set to obtain the sample set after denoising; then establishing a kernel matching pursuit training model by utilizing the sample set after denoising; afterwards optimizing a coefficient of the kernel matching pursuit training model by utilizing a harmony search algorithm; and finally, predicting the generating power of the photovoltaic power station by utilizing the kernel matching pursuit training model with the optimized coefficient. The prediction method avoids the local optimum phenomenon to enable a prediction model to be accurate, and has very good nonlinear processing capability and high-dimensional data processing capability.

The invention relates to an NSST domain flotation froth image enhancement and denoising method based on a quantum harmony search fuzzy set. The NSST domain flotation froth image enhancement and denoising method comprises the steps: carrying out NSST decomposition on a flotation froth image, and obtaining a low-frequency sub-band image and multi-scale high-frequency sub-bands; performing quantum harmony search fuzzy set enhancement on the low-frequency sub-band image; secondly, for the multi-scale high-frequency sub-bands, removing a noise coefficient by combining an improved BayesShrink thresholding and scale correlation, and enhancing an edge coefficient and a texture coefficient through a nonlinear gain function; and finally, performing NSST reconstruction on coefficients of the processed low-frequency sub-band and each high-frequency sub-band to obtain an enhanced de-noised image. According to the NSST domain flotation froth image enhancement and denoising method, the brightness, the contrast and the definition of the froth image can be improved, and the froth edge is obviously enhanced while noise is effectively inhibited, and more texture details are reserved, and subsequent processing such as froth segmentation and edge detection is facilitated.

The invention discloses a method for predicting the concentration of chlorophyll a in a water body based on harmony searching of a cloud model. The concentration of chlorophyll a in the water body is predicted by utilizing a support vector machine (SVM); a penalty factor C and a radial basis kernel parameter g of the SVM and a parameter epsilon in a non-sensitive loss function are coded into individuals of a harmony searching algorithm; a tone adjustment strategy based on cloud model sampling is designed; a new individual is generated by utilizing a forward and normal cloud generator; in addition, local searching is carried out by utilizing a chaos searching strategy; the local searching capability of the algorithm is enhanced; and, compared with the similar methods, the predication precision of the concentration of the chlorophyll a in the water body can be increased through the method disclosed by the invention.

The invention provides a power maximum load small-sample prediction method. The method comprises the steps of a, acquiring the historical data of the annual maximum load of a power grid, and acquiring a training sample and a test sample; b, defining an optimization problem, and initializing the parameters of the quantum search algorithm and the harmony search algorithm; c, initializing a quantum library and a harmony library; d, generating a new solution in the harmony library at the HMCR and disturbing the new solution; e, evaluating the new solution and updating the harmony vector; f, determining the optimal solutions of model parameters; g, taking the optimal value of sigma and the optimal value of C into a least squares support vector machine (LSSVM) model, and training the model by utilizing the training sample and the test sample; h, predicting the annual maximum load of the power grid by utilizing the trained LSSVM model. According to the technical scheme of the invention, the small-sample prediction on the annual maximum load of the power grid is realized by means of the LSSVM model. At the same time, the optimal value of sigma and the optimal value of C for the LSSVM model are found out based on the quantum search algorithm and the harmony search algorithm. Therefore, the blindness of parameter selection is avoided effectively, and the prediction precision is greatly improved.

The invention discloses an active power distribution network reactive voltage optimization method coordinated with multiple active management means. According to the invention, a global optimization-local adjustment two-stage reactive voltage optimization strategy is adopted. Specifically, according to the operation scheme of the invention, the optimized comprehensive satisfaction degree of the network loss and the voltage deviation is adopted as a target, and elements longer in regulation time scale, such as an on-load tap-changer, a capacitor bank, a contact and sectional switch and the like, are optimized. After that, a minimum voltage volatility index is adopted as a target. Moreover, a model is optimized and solved based on the application of the improved harmony search algorithm. Finally, through the analysis of examples, the reasonability and the superiority of the reactive voltage optimization method are verified.

The invention relates to a discourse domain based dynamic division and learning fuzzy scheduling rule mining method and belongs to the field of advanced manufacturing, automation and information. The method is characterized in that an attribute discourse domain dynamic fuzzy division method is raised, and a novel fuzzy scheduling rule form and Aprior algorithm based fuzzy scheduling rule intelligent mining method is given based on the dynamic fuzzy division method. In each fuzzy scheduling rule, each condition attribute has two linguistic variables to be output as a classification label to be used for representing the scheduling priority of two to machining operations. The corresponding attributes of the two linguistic variables are subjected to discourse domain division through simple fuzzy scheduling division dynamic fuzzy division, and a harmony search algorithm is designed to perform optimization learning on key parameters. A good scheduling effect can be produced by applying the method to the microelectronic production line scheduling problem with the minimum average running time serving as the scheduling target.

The invention discloses a vibration screen optimization design method using Gaussian harmony searching. According to the method, a Gaussian tone regulating strategy is designed in a harmony searching algorithm; searching information of outstanding individuals and random individuals in a harmony library is used; a Gaussian random number generation function is combined to generate new individuals; and the convergence speed is possibly accelerated while the diversity of the harmony library is maintained. In addition, a differential mutation strategy is used for performing local searching; the local searching capability of the algorithm is enhanced; the convergence speed of the vibration screen optimization design is accelerated; and an optimum individual obtained in the calculation process is the result of the vibration screen optimization design. Compared with a similar method, the method provided by the invention has the advantages that the convergence speed of the vibration screen optimization design can be accelerated; and the precision of the vibration screen optimization design can be improved.

The invention discloses a grapefruit image segmentation method based on a double-strategy harmony search algorithm, which mainly solves the problems of low segmentation speed and low segmentation precision of a traditional harmony search algorithm which is applied to grapefruit image segmentation. According to the grapefruit image segmentation method, an adjustment strategy guided by an optimal individual and an adjustment strategy guided by a random individual are fused in the tone adjustment process when a grapefruit image is segmented by using the harmony search algorithm, so that the balance between the convergence speed and the group diversity is realized, and the segmentation precision of the grapefruit image is increased; in addition, the local search capability of the algorithm and the segmentation speed of the grapefruit image are increased by utilizing an elite chaos search strategy. Compared with a similar method, the grapefruit image segmentation method has the advantage that the segmentation speed and segmentation precision of the grapefruit image can be increased.