64 results about "Crowding distance" patented technology

The invention discloses a many-objective optimized scheduling method for combined operation of cascade hydropower stations, and aims at solving main problems in engineering application of standard quantum-behaved particle swarm optimization and problems in solving single-objective optimized scheduling. A multi-population evolution strategy is realized by external file set, advantageous individual selection and a chaotic mutation operator strategies, diversity of individuals is ensured, calculation of the method is accelerated, and an approximate Pareto optimal leading edge with sound distribution is obtained. An external file set is introduced to store elite individuals, dynamic update and maintenance of the file set are realized via non-inferior layered ordering and crowd distance, and distribution of the individual is kept uniform; and a chaotic mutation operator is used to carry out local disturbance on a non-control solution, and the neighborhood exploration capability of the individuals is enhanced. According to the invention, the particle swarm optimization is improved, and effectively applied to making the many-objective optimized scheduling scheme of combined operation of the cascade hydropower stations, and a feasible and high efficiency calculating method is provided for many-objective optimized scheduling of the cascade hydropower stations.

The invention provides a multi robot path planning method based on a multi-target artificial bee colony algorithm and belongs to the technical field of path planning. The method includes path planning problem environment modeling, multi-target artificial bee colony algorithm parameter initialization, three-variety bee iteration optimization path and non-inferior solution determination, good path reservation by sequencing and optimum path set outputting. By means of the method, the standard artificial bee colony algorithm is improved based on the concept of non-domination sequence of Pareto domination and crowd distance, and the multi-target artificial bee colony algorithm applicable to solving the multi-target optimization problem is provided. In the path planning process, multiple performance indexes of path length, smoothness and safety are considered in the algorithm, and a group of Pareto optimum paths can be acquired through one-step path planning. The path planning method belongs to meta-heuristic intelligent optimization methods, is different from the traditional single-target path planning method, and can well adapt to path planning tasks in complex environment.

The invention discloses a method for selecting a multi-objective immune optimization multicast router path, which mainly solves the problem of the optimization of multicast routers. The method has the following steps of: (1) determining an optimized target to generate a network model, and setting running parameters to generate an initial population; (2) eliminating an individual path loop; (3) calculating the individual target value to generate a current non-dominant population; (4) judging finishing conditions, if the finishing conditions are met, the current non-dominant population is output, or else, carrying out step (5); (5) calculating the individual crowding distance of the current non-dominant distance to generate an active population; (6) carrying out cloning and local searching operation on the active population; (7) carrying out recombination, variation and local search operation on the cloned population; (8) combining the current non-dominant population with the populations obtained in the steps (6) and (7) to eliminate the individual path loop; (9) calculating the individual value to refresh the current non-dominant population, and carrying out the step (4). The invention has the advantage of providing a flexible optimization scheme and is suitable for selecting the multicast router path.

The invention, which belongs to the technical field of data processing, discloses a radar radiation source signal feature selection method based on a membrane particle swarm multi-target algorithm. According to the method disclosed by the invention, a membrane calculation optimization theory is combined with a particle swarm optimization algorithm and the uniformity and diversity of a crowding degree maintenance set are utilized; and a data object is optimized by using two objective functions of correlation and redundancy and application to in-pulse feature selection of a radar radiation source signal is carried out. According to the invention, with non-dominated sorting and a crowding distance mechanism in a skin membrane, rapid convergence of the multi-target particle swarm optimizationalgorithm is kept by the algorithm and the solution set has high diversity. And then with KUT and ZDT series test functions, comparison testing is carried out on the algorithm and MOPSO, SPEA2 and PESA2 algorithms. Therefore, rapid convergence to the real Pareto leading edge is realized and the provided algorithm is feasible and effective.

The invention discloses a thermal power plant comprehensive scheduling method based on environment protection and economical benefit. The method includes the following steps: building a mathematical model of thermal power plant environment economical scheduling problems; obtaining various types of parameters in the model; initiating a practicable scheduling generating process and encoding processing; evaluating practicable scheduling; updating the practicable scheduling; updating a non-bad scheduling solution set in an external file set; determining three important non-domination scheduling sequences affecting an iterative process by circulating crowding distance; judging if iteration t reaches a maximum value, on yes judgment, outputting practicable scheduling in the current non-bad solution set, and on no judgment, setting the iteration t=t+1, and returning to step 5 to conduct updating again. Due to the fact that increasing of electric energy production can inevitably cause increasing of pollution discharge, namely increasing of cost for treating environment pollution, the thermal power plant comprehensive scheduling method adjusts generating efficiency of all thermal power plant units in real time and achieves optimization of comprehensive benefit.

The invention relates to a meme evolution multiobjective optimization scheduling method based on objective importance decomposition, comprising following steps: randomly generating an initial population with volume of N; in every generation of the algorithm, selecting by the current population through binary championship, generating an offspring population by a genetic operator; carrying out fine search to the offspring population by a partial search strategy to obtain improved population; combining the current population, the offspring population and the improved population to generate a population, carrying out mutation operation to the individuals in the population with identical objective size; sorting the individuals in the population through using the rapid nondominated sorting and crowding distance method in the NSGA-II (Nondominated Sorting Genetic Algorithm II), thus selecting N best solutions as the next generation populations. According to the invention, a multiobjective flexible working shop system can be scheduled effectively; the scheduling effect is superior to the existing advanced algorithm; and the method of the invention can be widely applied in the computer application technical field and the production scheduling field.

The invention provides an intelligent algorithm for solving a multi-objective MDVRPTW (Multi-Depot Vehicle Routing Problem with Time Window), which comprises: a first step: by combining a non-dominant sorting genetic algorithm with an elitist strategy on the basis of an extreme value crowding distance with local searching, searching an extremal solution in a decision space; and a second step: by combining a multi-objective evolutionary algorithm based on decomposition with local searching, on the basis of a final population solved in the first step, further optimizing to obtain a group of solutions with both convergence and diversity. The two-step process enables convergence and diversity of the algorithm to be well balanced, and promotes quality of solving the multi-objective MDVRPTW by the algorithm.

The invention relates to a power distribution network multi-target reactive-power optimization method based on a non-dominated neighbor-domain immune algorithm. Active power loss and reactive compensation input are regarded as targets to be optimized, and a power distribution network multi-target reactive-power optimization model considering constraints such as active balance, reactive balance, power distribution network power limits, node voltage limits, reactive compensation capacity limits, transformer tap limits, compensation node limits and line transmission power limits is established. The power distribution network multi-target reactive-power optimization model is solved by utilizing the non-dominated neighbor-domain immune algorithm. According to the algorithm, the non-inferiority and distributivity of a finally obtained Pareto solution are ensured by adopting proportional cloning, combination, variation and other operations and selection based on crowding distances. The specific configurations of a reactive compensation device having minimum active power loss and lowest compensation input cost can be rapidly and reliably obtained, and the optimization method has a better engineering application prospect.

The invention discloses a sequential test optimization method based on a multi-objective genetic programming algorithm. The method comprises the steps of firstly initializing to obtain a fault diagnostic tree population, using the multi-objective genetic programming method to select, cross and mutate a fault diagnostic tree, during the iteration of each generation, grouping the fault diagnosis tree individuals, wherein, the adaptability of each individual is calculated by using grouping adaptability and a crowding distance; after multiple iterations, selecting non-domination individuals from a final generation population as a non-domination fault diagnosis tree of the sequential test of the system. The sequential test optimization method based on the multi-objective genetic programming algorithm can be used for acquiring a Pareto optimum solution of the fault diagnosis tree of the sequential test for which multiple test indexes can be used as optimized targets, the optimum solutions can be selected by testers to provide guidance for system testers.

The invention relates to an assembly line multi-target modeling method, a particle swarm algorithm and an optimization scheduling method, wherein the optimization scheduling method comprises the steps of S1 constructing a assembly line multi-target optimization model; S2 performing multi-target optimization design to the assembly line multi-target optimization model by the particle swarm algorithm and screening the optimization result to reconstruct the assembly line. According to the reconstructible assembly line optimization scheduling method, a crowding distance calculation method and an elite strategy are referred to; diversity maintenance and global optimal value update are conducted on the basis of individual crowding distance ordering; the complex fitness calculating process is avoided; and a small probability random mutation mechanism is introduced, thereby enhancing the global searching optimization capability gratly.

The invention discloses a multi-objective multi-modal particle swarm optimization method based on Bayesian adaptive resonance. The method comprises the following steps: dividing all particles into a plurality of populations by using a Bayesian adaptive resonance theory; sorting the particles of each population according to a non-dominated sorting method and the special congestion distance; updating the particles in the population by using the individual optimization of the particles and the global optimization of the population; connecting the non-dominated solution sets of various groups endto end to form a closed loop topology, and performing local exploration by using a particle swarm optimization algorithm based on the loop topology; and repeating the two updating and exploring processes until a termination condition is met, and outputting all the non-dominated solution sets and the Pareto frontier. The method is suitable for optimization of solving the multi-target multi-modal problem, the distribution of the Pareto leading edge can be found in the target space, the corresponding Pareto optimal solution set can be found in the decision variable space, a redundant backup method is provided, and the reliability of engineering practice activities is improved.

The present invention discloses an improved crisscross optimization algorithm-based multi-objective reactive power optimization method and system. The method comprises the steps of calculating target values of each particle in an initial population, wherein the target values at least comprise target values of an active power network loss, a voltage offset and a voltage stability margin; performing horizontal cross and vertical cross on the initial population so as to generate sub-generation W and sub-generation R; screening the sub-generation R to obtain an excellent particle population; and combining the initial population, the sub-generation W and the excellent particle population so as to generate a population pool, selecting a new generation of population by using non-dominated sorting and crowding distance, and outputting a final result when an iteration number of times is greater than a preset threshold. In the method, the active power network loss, voltage offset and voltage stability margin are all considered in reactive power optimization of the system, and the system is optimized by using the improved crisscross optimization algorithm, so that multi-objective reaction power optimization is realized, and the algorithm is less likely to optimize locally.

The invention provides an irrigation water resource optimization configuration method based on a bi-objective immune particle swarm algorithm. Crops cannot grow under sufficient irrigation due to inadequate irrigation water in arid regions, the analysis of the crop growth characteristics and the rational distribution of water resources allow the crops to achieve optimum yields even in drought conditions. The optimization method is an immune particle swarm optimization algorithm based on congestion distance, simulates the antibodies and mutation mechanisms of the human immune system, improves the conventional particle swarm algorithm, selects a part of the new generation of particle swarm based on the congestion distance to be an antibody population, and then selects an antibody from the antibody population to cross and mutate the particles. The method is capable of carrying out reasonable water distribution during the growing season of the crops so as to maximize the yield of crops while minimizing the amount of irrigation water.

The present invention provides a multi-target optimization method for casting sequence selection, ranking and a casting time policy of a continuous casting machine. The method includes the following steps of establishing a multi-target optimization model, wherein the multi-target optimization model takes minimum total punishment, accumulated metal on the production line and non-effective use amount of high-quality molten iron of production batch plan execution conditions of a steel plant as a target function, and the multi-target optimization model is formed by constraint equations of related processing requirements; acquiring a production batch plan of the steel plant, coding based on casting sequence selection and performing population initialization; decoding based on a main constraint satisfaction method, calculating an adaptability value, and acquiring an initial solution set; performing non-dominated ranking and crowding distance ranking; selecting some individuals in the population as parents; performing crossing and variation on the parents; decoding a calculation result and calculating adaptability; determining an elitist solution set, and calculating a crowding distance and ranking; and outputting the elitist solution set, selecting the most satisfied scheme and transmitting the most satisfied scheme to a steelmaking-continuous casting production operating control system. Through adoption of the multi-target optimization method, a furnace casting period of continuous casting production is controlled stably, the algorithm efficiency is better than that of a traditional non-dominated ranking genetic algorithm and a strength pareto evolutionary algorithm.

The invention provides a steel making factory multi-target scheduling plan compiling method considering molten iron supply time and molten iron resource utilization. The method comprises the following steps that a multi-target function and constraint conditions considering the molten iron supply condition are established, multiple Pareto optimal solutions about a decision variable are acquired by utilizing a multi-target genetic algorithm based on Pareto for iterative operation, and one concrete iterative process is that a matching scheme between charge and molten iron bottles is adopted to indicate chromosomes, and feasible solutions of all the chromosomes in the current population are acquired by utilizing a decoding heuristic method; a non-dominated solution construction method is designed to calculate non-dominated solutions of the feasible solutions; non-dominated hierarchical ranking is performed on the chromosomes corresponding to all the solutions and crowding distance between the solutions is calculated, and a parent population is selected; and selection, intersection and mutation are performed on the chromosomes in the parent population so that progeny populations are obtained. The problem of scheduling plan compiling considering the molten iron supply condition can be solved, and multiple Pareto optimal solutions are acquired so that decision makers are enabled to select more appropriate solutions to be applied to actual production.

The invention provides an optical waveguide array-optical fiber array automatic butt-coupling parallel index optimization method. The optical waveguide array-optical fiber array automatic butt-coupling parallel index optimization method comprises that mapping between an optical waveguide array-optical fiber array auto-aligning physical parameter and a parallel index optimization model is established, and a first objective function and a second objective function are obtained; a maximum evolutional generation is set, input single-mode fiber arrays and output single-mode fiber arrays enter specified spatial positions in a specified direction and attitude according to the regulation of each individual, optical power values of two or more than two waveguide channels are read, recorded and stored, and the Pareto sequence and the crowding distance are calculated; evaluation of each individual in one generation cluster is completed, a next generation cluster is generated by means of intersection, variation and lowliest place elimination, the evolutional generation is accumulated, whether the evolutional generation reaches the maximum evolutional generation or not is determined, if the evolutional generation reaches the maximum evolutional generation, the process is finished; if the evolutional generation does not reach the maximum evolutional generation, the intersection step is returned. According to the invention, the low-loss rapid butt-coupling automation degree and the working efficiency of an optical fiber array-waveguide device-optical fiber array system are effectively improved.

The invention discloses an image cutting method based on a multi-target intelligent body evolution clustering algorithm. The problems that the image cutting technology is prone to local optimum and an algorithm is not high in robustness are mainly solved. The image cutting problem is converted into a global optimization clustering problem. The process includes the steps of extracting gray information of pixel points of an image to be cut, initiating parameters and establishing an image intelligent body network, calculating the energy of an image intelligent body, conducting non-domination sequencing, conducting neighborhood competition operation, conducting Gaussian mutation operation, calculating the energy of the image intelligent body, conducting non-domination sequencing, conducting self-learning operation, selecting the optimal clustering result according to the crowding distance, outputting a clustering label, and achieving image cutting. Multiple targeting is achieved for the image processing process, the convergence effect is good, the robustness of the method is enhanced, the image cutting quality can be improved, the cutting effect stability can be enhanced, and the extraction, recognition and other subsequent processing of the image targets are facilitated.

The invention discloses a data-driven material reverse design method and system, and the method comprises the following steps: firstly, obtaining the material data of a formed material sample; then, according to the material data, respectively selecting a machine learning model of a corresponding relationship between each performance parameter and a design parameter; searching and selecting parameters of each machine learning model by adopting a cross validation method based on the material data to obtain a modified machine learning model of a corresponding relationship between each performance parameter and a design parameter, and taking the modified machine learning model as a fitness function of each performance parameter of the genetic algorithm; and finally, carrying out optimizationsolution by adopting a genetic algorithm. According to the invention, a non-dominated sorting mode and a crowded distance sorting mode are adopted in the genetic algorithm; multi-objective optimization solution is realized, a penalty factor does not need to be introduced, the technical defects that all possibilities cannot be exhausted in a search space in traditional grid search and a design result is inaccurate due to a penalty factor introduction process are overcome, and the comprehensiveness and the accuracy of material reverse design are improved.

The invention provides a BP neural network and MBFO algorithm-based aluminum electrolysis energy conservation and emission reduction control method. The method comprises the following steps: carrying out modeling on the aluminum electrolysis production process by utilizing a BP neural network; and optimizing the production process model by utilizing a crowding distance-based multi-target bacterial foraging optimization algorithm so as to obtain a group of optimum solutions of each decision variable as well as current efficiencies and greenhouse gas emissions corresponding to the optimum solutions, wherein the crowding distances of the non-inferior solutions need to be calculated during the optimization of the production process model and the external files are updated according to the crowding distances so that the floras rapidly move toward the target to ensure quick convergence under the premise of ensuring the population diversity. According to the method, the optimum values of the process parameters in the aluminum electrolysis production process are determined, the current efficiency is effectively improved and the greenhouse gas emission is decreased, so that the aim of conserving energy and reducing emission are really achieved.

The invention claims a multi-objective reactive power optimization method for a power system based on a DSICA algorithm. The method comprises the following steps of: establishing a multi-objective reactive power optimization mathematical model of a power system, and setting system parameters and algorithm parameters; initializing the individual country in the algorithm, and getting the target function value by load flow calculation; replicating individual countries to external storage spaces; performing algorithm iteration to update the individual position of the countries by using the proposed individual position updating method and distance strategy; performing non-inferior ranking and crowding distance calculation on the updated individual countries together with the previous generationof individual countries; judging whether the iterative number is satisfied, if so, entering the next step, if not, returning to the iterative number repeatedly; outputting the Pareto optimal solutionset formed by the external storage space, using a fuzzy group decision method to find the optimal compromise solution and outputting the solution. The invention has the advantages of wide searching range, strong searching ability and high solving quality in processing the multi-objective reactive power optimization problem of the electric power system, and proves the effectiveness and superiorityof the invention.

The invention relates to a pseudoknot-carrying nucleic acid structure prediction method based on a multi-target genetic algorithm. The method comprises the following steps: determining a K continuousmatching set according to a minimum stem zone number and a minimum base number in cycles, generating an initial population, carrying out selection, crossover and variation on an RNA (Ribonucleic Acid)molecular sequence by using a multi-target genetic algorithm, carrying out nondominant sequencing and crowding distance sequencing so as to obtain an optimal solution set with a Pareto molecular structure, and finally selecting an RNA molecular structure with the minimum free energy from the optimal solution set as a final prediction result. By adopting the method, the time complexity and the space complexity are degraded, and the pseudoknot-carrying RNA molecular structure prediction accuracy is improved.

The invention discloses a hydropower station short-term multi-target power generation plan making method and system for power grid peak regulation, and the method comprises the steps: firstly, building a cascade hydropower station short-term multi-target power generation plan making model; Determining a power station economic operation mode according to the input condition; Randomly generating a plurality of particles in the feasible space, determining an individual extreme value of each particle based on an objective function value of each particle, and storing a part of particles which are not mutually dominated in an external archive set; Updating the position and speed of each particle, randomly selecting a part of particles for polynomial variation, repairing the infeasible solution,and calculating the objective function value of each particle again; Updating the individual extremum of each particle according to the re-calculated objective function value of each particle; And updating the external archive set according to the dominance relationship of the particles and the crowding distance, and if a preset termination condition is met, outputting the external archive set soas to obtain a global extreme value. The peak regulation pressure of the system can be effectively reduced, and the power supply quality is improved. The power grid can safely, stably and economicallyoperate.

The invention discloses a multi-target weapon-target distribution method based on an artificial fish swarm algorithm, aiming to overcome the defect that solving for a WTA problem in the prior art deviates from a real Pareto leading edge greatly. The multi-target weapon-target distribution method comprises the following steps: firstly, randomly generating an initial population, calculating a non-dominated solution set of the initial population, and sorting according to a congestion distance to obtain a globally optimal solution of the initial population; then, according to the clustering behavior of the artificial fish swarm algorithm, enabling other individual fishes in the fish swarm to approach the optimal solution to obtain a new population and a previous non-dominated solution set, andcalculating a new non-dominated solution set; and finally, carrying out crossover variation on the clustered population to increase population diversity, combining with the previous non-dominated solution set again, and carrying out multiple iterations to obtain a final Pareto leading edge. The multi-target weapon-target distribution method is mainly used in the field of fire fighting decision making, is closer to the real Pareto frontier compared with the prior art, has small dependence on parameters, and has great application value in multi-target weapon-target distribution.

A method for optimize pipeline layout based on an improved distance strategy imperialist algorithm comprise that following steps: respectively establishing three objective function of pipeline length,elbow number and installation convenience, and integrating obstacle avoidance constraint functions into three objective functions by using a penalty function method; Initializing the population, i.e.The country, calculating the objective function value of each country, constraining the total value of the violation and the state variables corresponding to the control variables, and copying the initial population to the external storage space; The algorithm iterates, adopts the improved distance strategy imperialist algorithm to optimize and update the country, copies the updated country to the external storage space and calculates the non-inferior ranking and congestion distance with the previous generation of countries, and then cuts according to the ranking to keep the size of the external storage space unchanged; According to the genetic algorithm, the optimal compromise solution is found from the Pareto optimal solution set, and the design is carried out according to the optimal compromise solution.

The invention discloses a multi-broadcasting route optimization searching method based on improving the clone niche algorithm in the technical field of communication, which comprises obtaining network information and generating an alternative route base, randomly generating a first antibody aggregation according to the first alternative route base, determining an antibody in a memory pool, calculating the affinity degree of the first antibody aggregation, forming final n groups of antibody clusters according to the affinity degree, carrying about the clone proliferation for n groups of the antibody clusters and variation then, selecting a Pareto solution in each group of the antibody clusters and then putting the Pareto solution in a second antibody aggregation, processing the second antibody aggregation, selecting out the Pareto solution of the second antibody aggregation and then putting the Pareto solution in the memory pool, carrying out the similarity suppression and gradient decision, determining whether the partial crowding distance of the antibody in the memory pool is smaller than or equal to a preset upper-limit threshold value, operating a partial crowding system if the partial crowed distance is larger than the threshold value, determining whether reaching iteration times, and completing the optimization searching if reaching the iteration times. The invention can optimize a plurality of QoS parameters in the optimizing process of multi-broadcasting route.

The invention relates to a power distribution network reactive power optimization method based on an improved NAGAII algorithm, and the method comprises the steps: S1, determining an optimization target, and building a target function; S2, determining the number of control variables and constraint conditions; S3, initializing control parameters of the improved NSGAII algorithm, and generating an initial parent population; S4, performing genetic operation on the parent population individuals, performing updating, generating new individuals to generate a test population, and calculating target function values and overall constraint violation degrees of the test population individuals; S5, enabling individuals in the parent population and the test population to compete to generate a temporarypopulation; S6, performing non-dominated solution sorting and congestion distance sorting on individuals in the temporary population, and selecting a new parent population; S7, judging whether the number of iterations is greater than the maximum number of iterations or not, if so, stopping iteration, and outputting a reactive power optimization result; and if not, returning to S4. According to the method, the problems that the original NAGAII algorithm is easy to fall into local optimum and the convergence speed is low are effectively solved.

The invention discloses a hybrid scheduling method based on maximum consumption of new energy and optimal power generation cost. The method comprises the following steps: firstly, constructing an objective function, an energy balance constraint condition, a system energy supply equipment constraint condition, an energy storage device constraint condition and a spinning reserve constraint condition with maximum new energy consumption and minimum integrated energy system operation cost; taking the constraint conditions as constraint conditions of a non-dominated sorting genetic algorithm and a multi-objective particle swarm algorithm, and solving an optimal solution for the multi-objective function by fusing the non-dominated sorting genetic algorithm and the multi-objective particle swarm algorithm; finally, in the iteration process, the populations are sorted according to the crowding distance, the whole population is divided into two parts according to the sorting result, the best half of the population is optimized through a non-dominated sorting genetic algorithm, the other half of the population is optimized through a multi-target particle swarm algorithm, and the populations are converged around the optimal solution. The method provided by the invention can effectively promote the consumption of new energy and reduce the operation cost of the system.