47results about How to "Solving Multi-Objective Optimization Problems" patented technology

The invention provides a route planning method based on a multi-target glowworm swarm algorithm and belongs to the technical field of route planning. The method includes: modeling a route planning problem, initializing the multi-target glowworm swarm algorithm, updating the glowworm position, determining a non-inferior solution set, updating an external archived file, judging whether a preset maximum iteration number is achieved or not, and determining a Pareto optimal route. The basic glowworm swarm algorithm is improved based on the Pareto dominant conception, and global searching and parallel computing capacities of the glowworm swarm algorithm are well used. Multiple route performance indexes are considered simultaneously during planning, a group of Pareto optimal solution sets can be obtained by planning once, and high flexibility is achieved. Further, the route panning method is different from traditional route planning methods for a single target and route planning methods of using a weighing method to convert multiple targets into the single target, and can better meet practical requirements of route planning.

The invention belongs to the field of path planning technology and specifically relates to a three-dimensional multi-UAV coordinated path planning method based on SAS. The method comprises the following steps: carrying out modeling on a path planning environment; initializing multi-target SAS calculation parameters consisting of the length of a minimum path section, a maximum turning angle, a maximum angle of climb/glide, a minimum safe distance of UAVs and a minimum flight altitude of UAVs; initializing the positions of the UAVs, wherein each UAV represents a path; updating the positions of the UAVs; expanding a current node; determining whether a path section collides with other path sections; updating a node table of the path section; executing a step (8) if minimum path cost set in the step (2) is met, and otherwise, executing the step (3); determining a cooperated planned optimal path so as to complete path planning. The method overcomes multi-objective optimization problems, has versatility, provides a reasonable optimal solution for a decision maker and better accords with practical needs.

The invention relates to a multi-target probabilistically optimal power flow fuzzy modelling and solving method for a power distribution network. The multi-target probabilistically optimal power flow fuzzy modelling and solving method comprises the following steps: obtaining n (n is less than t) random variables in t nodes in a network, and establishing a random variable model; obtaining 2<n> sample points corresponding to the n random variables and weighting coefficients omega<1> and k corresponding to the sample points by adopting a two-point estimation method; establishing a multi-target probabilistically optimal power flow nonlinear programming model; converting the multi-target probabilistically optimal power flow model into a single-target nonlinear programming model on the basis of a fuzzy set theory, and obtaining optimal power flow values Z<1> and k corresponding to the 2<n> sample points; and obtaining origin moments of various orders of an optimal power flow Z. According to the multi-target probabilistically optimal power flow fuzzy modelling and solving method provided by the invention, the random distributed power supply output problem is solved by adopting a point estimation method; therefore, the probabilistically optimal power flow problem is converted into deterministic optimal power flow calculation and characteristic value statistics; various samples are calculated by adopting the multi-target optimal power flow modelling and solving method respectively; and thus, the statistical characteristic value of a probabilistically optimal power flow can be finally obtained.

The invention relates to a nuclear main pump resin-based composite material tilting pad water lubrication thrust bearing optimized design method. The method is characterized in that the geometric dimension of the tilting pad and the thickness of a cylindrical tile surface serve as design variables, the bearing capacity and power loss of a bearing serve as optimization problems of an objective function, an initial sample point of the design variables is determined through sampling, and an elastohydrodynamic lubrication model simulating the coupling effect of interface slippage, water film cavitation and tile surface deformation is established; a Reynolds equation corrected by an ultimate shear stress model is converted into a linear complementary problem through a parameter quadratic programming algorithm, and the linear complementary problem and a solid elastic deformation equation are subjected to iterative solution to obtain the sample point of the design variables and a target function value data set corresponding to the sample point; a Kriging agent model algorithm is used, and accordingly to an optimization point adding rule criterion and iteration ending conditions, multi-objective optimization of the inner diameter, outer diameter and warp angle of a tilting pad slippage of the thrust bearing, the thickness of the tile surface and the like is finally achieved. The methodhas the advantage that high bearing capacity and the low power loss of the large low-elasticity-modulus material tilting pad thrust bearing are achieved.

The invention discloses an underwater wireless sensor network topology control method based on a genetic algorithm. The method comprises the following steps: a) constructing an underwater network model; b) establishing member node and cluster head node energy models and energy parameters; c) calculating the optimal cluster head number; d) determining an optimal cluster head by adopting a genetic algorithm, including the substeps: d-1) performing chromosome coding; d-2) selecting an initial population; d-3) constructing a fitness function; d-4) constructing a selection operator, a crossover operator and a mutation operator; d-5) calculating an individual fitness value; d-6) selecting an optimal cluster head; e) self-clustering and carrying out data communication. According to the underwaterwireless sensor network topology control method, the genetic algorithm is used in the cluster head selection stage, the multi-objective optimization problem can be solved, the energy and distance factors can be effectively balanced, the energy consumption of each node can be effectively balanced, and the overall energy consumption of the network is reduced, so the lifetime of the whole network isprolonged.

The invention discloses a reusable spacecraft surface impact damage feature recognition method. The method comprises the steps of representing a thermal image sequence of pixel points collected by a thermal infrared imager by a three-dimensional matrix; finding a temperature peak value in the three-dimensional matrix M; performing temperature division on the row of the frame number where the temperature peak point is located; performing temperature division on the column of the frame number where the temperature peak point is located; extracting transient thermal response in a blocking and step-by-step manner; adopting a mean shift algorithm to automatically classify the extracted typical transient thermal responses; adopting a decomposition multi-objective optimization algorithm based ongradient search weight adjustment to select representations of each type of transient thermal response to form a matrix Y; converting the three-dimensional matrix into a two-dimensional matrix, and performing linear change on the two-dimensional matrix by utilizing the matrix Y to obtain a two-dimensional image matrix R (x, y); and performing feature extraction on the two-dimensional image matrixR (x, y) by using a one-dimensional Ostu segmentation algorithm. According to the method, through the uniformly distributed solution sets, the difference and similarity are comprehensively considered,and the defect feature extraction accuracy is improved.

The invention discloses an underwater wireless sensor network security routing control method based on a quantum genetic algorithm. The method comprises the following steps: a) establishing an underwater clustering network model; b) establishing member node and cluster head node energy models, energy parameters and trust models; c) calculating the optimal cluster head number; d) determining an optimal cluster head by adopting a quantum genetic algorithm, including: d-1) chromosome coding; d-2) selecting an initial population; d-3) carrying out population measurement; (d-4) making fitness evaluation; (d-5) updating the population; (d-6) selecting an optimal cluster head; and e) carrying out self-clustering, and establishing routing and data communication. The quantum genetic algorithm is used in the cluster head selection stage, the multi-objective optimization problem can be solved, the average energy consumption of global nodes can be effectively averaged, the overall service life ofthe network is prolonged, the adaptability is achieved, node malicious behaviors are evaluated through trust values, the safety of the network is guaranteed, and the wide application value is achieved.

The invention discloses a park electricity distribution and sale planning operation method considering wind, light and storage. The method comprises the following steps: S1, establishing an investmentstrategy independent optimization model in which park electricity distribution and sale participate; S2, converting the optimization model into a mixed integer second-order cone programming model according to second-order cone relaxation, a bigM method and an auxiliary variable adding method; S3, determining a topological line structure, a reference capacity value, a planned age limit, a system load growth coefficient, typical load daily load data and wind-solar output data of the park power distribution network system; and S4, according to the parameters determined in the S3 and the planningmodel in the S2, performing calculation by utilizing Matlab to obtain planning capacities of wind, light and storage. According to the planning operation method, wind power, photovoltaic and energy storage are comprehensively considered, asset operation optimization configuration is effectively achieved, the optimal equilibrium solution can be rapidly achieved, the stability of the solution is good, and powerful support is provided for improving the operation capacity of companies in the power industry.

The invention discloses a reliable vehicle-mounted edge calculation unloading method based on reinforcement learning, which is used in scenes such as limited coverage of road side units (RSUs) caused by urban obstacles or insufficient calculation capability of the road side units, and a vehicle can perform reliable calculation unloading by means of an unmanned aerial vehicle (UAV). According to the method, a multi-objective optimization problem of maximizing system utility and minimizing power consumption is converted into two sub-problems of a power distribution problem and a calculation unloading problem, and task distribution and calculation are completed by the unmanned aerial vehicle and the road side unit together. According to the method, the chance constraint is created for the transmission power, the chance constraint is converted by using a Chebyshev inequality, the minimum transmission power is derived, and the reliability of task transmission is ensured. According to the method, the calculation unloading efficiency is improved through the improved deep reinforcement learning model, the predicted target Q value in the deep reinforcement learning model is adjusted by using the result of the multi-target optimization problem, and offline training and online updating of the edge server are realized.

The invention discloses an overload real-time system non-preemptive scheduling method based on MaxSAT optimal solution, which comprises the following steps: (S1) determining scheduling problem and coding task attributes into MaxSAT hard clauses; (S2) encoding the scheduling target as a MaxSAT clause; (S3) connecting the MaxSAT hard clause obtained from (S1) and the MaxSAT clause obtained from (S2)by using conjunction operator to obtain the MaxSAT problem; (S4) calculating the optimal solution of the MaxSAT problem by the MaxSAT solver, and obtaining the optimal scheduling scheme. By adoptingthe scheme, the invention solves the problems of difficulty in solving the optimal scheme and low efficiency existing in the overload real-time system, and finally improves the efficiency of the target task scheduling method, so that the method has strong expansibility and execution efficiency, but also has high practical values and popularization values.

The invention discloses a preemptive scheduling method of an overload real-time system based on a MaxSAT optimal solution. The method comprises the following steps: (S1) determining a scheduling problem and encoding a task attribute as a MaxSAT hard clause; (S2) encoding the scheduling target as a MaxSAT clause; (3) connecting the MaxSAT hard clause obtained from (S1) and the MaxSAT clause obtained from (S2) by using conjunction operator to obtain the MaxSAT problem; (S4) calculating optimal solution of the MaxSAT problem i by the MaxSAT solver, and the optimal scheduling scheme is obtained. The invention improves the efficiency of task preemptive scheduling in an overloaded real-time system through the scheme, so that the invention not only has strong expandability and execution efficiency, but also has high practical value and popularization value.

The invention discloses a multi-objective optimization method and system for dynamically using a neural network, and relates to the technical field of multi-objective optimization algorithms, and the method comprises the steps: obtaining a decision variable of a first-generation individual of a genetic algorithm, and putting the obtained decision variable into a simulator to obtain a corresponding performance index; training the neural network model; converting a performance index corresponding to a first generation of individuals of the genetic algorithm into a fitness function, obtaining a dominating relationship between a feasible solution set and a non-feasible solution set based on a rapid non-dominating sorting algorithm, and then selecting a next generation of parent individuals from non-dominating leading edges of the feasible solution set and the non-feasible solution set by using an accessibility algorithm; obtaining a decision variable of the current generation individual, and putting the decision variable into a simulator to obtain a performance index; the feasible parent individuals and the infeasible parent individuals of the next generation are selected through a reachability algorithm, and the number of the individuals generated by each operator of the next generation is dynamically allocated. According to the method, the problem of multi-objective optimization can be solved under the condition that physical simulation is relatively complex and computing resources are limited.

The invention discloses a method and system for selecting the capacity and installation position of a static var generator for a power distribution network, and the method comprises the steps: enabling the capacity Qi of reactive compensation equipment arranged at each node position of the power distribution network to form a chromosome gene, and enabling each chromosome to represent a scheme forthe site selection and capacity configuration of the reactive compensation equipment; forming a phenotype of the gene by adopting a plurality of target functions representing the operation effect andthe economic index of the reactive power compensation scheme, and taking the phenotype as a basis for gene selection; initializing a population and population algebra of the gene, and giving the maximum number of iterations; and if the current iteration is completed, outputting an optimization result, selecting a scheme with the minimum voltage square deviation, and determining the capacity and the installation position of the reactive power compensation equipment. According to the method and the system, the multi-objective optimization problem of reactive power compensation configuration canbe quickly and efficiently solved, and the method and the system have important engineering value and wide expansibility.

The invention discloses a distributed photovoltaic power supply medium and long term locating and sizing method based on an NSGA-III algorithm. The method comprises the following steps: constructing a distributed photovoltaic site selection optimization model by taking probability voltage sensitivity and network loss as objective functions; and constructing a distributed photovoltaic site selection capacity optimization model by using a voltage out-of-limit risk, annual network loss and a photovoltaic installed capacity objective function. In order to solve the problem that an existing optimization algorithm is poor in effect when facing a multi-objective optimization problem, an NSGA-III algorithm is adopted to solve an optimization model, and a Pareto solution set of the multi-objective optimization problem is generated. Besides, in consideration of the influence of medium and long term load change on an optimization result, the method carries out rolling solution on the optimization model by taking a year as a unit, and provides a decision basis for the future photovoltaic installed capacity expansion of the region.