27 results about "Crowding distance" patented technology

The invention belongs to the technical field of semi-active suspension control methods, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization (MOPSO) algorithm, which comprises the following steps of: performing adaptive grid division on an external file of the MOPSO algorithm to determine global optimum with the most diversity and global optimum with the most convergence; judging the evolution state of the population according to the distribution entropy variation of all optimal solutions in an external file, and selecting a global optimal gBest for guiding evolution; according to the variable quantity of the local crowding distance of the current population evolution, adjusting flight parameters of population evolution to balance the evolution trend of the population; and fusing the two core mechanisms, providing a multi-information fusion multi-target particle swarm optimization algorithm, and applying the multi-information fusion multi-target particle swarm optimization algorithm to determination of a semi-active suspension LQR control strategy weight coefficient. According to the novel multi-objective optimization algorithm, external archive information and current population dynamic information are cooperatively integrated, so that balance between convergence precision and population diversity is effectively realized.

The invention discloses an ecological flow process multi-objective optimization method based on an NSGA-II algorithm, and relates to the technical field of water conservancy projects. Comprising the steps of defining discrete decision variables, setting an objective function, constructing constraint conditions and a mixed penalty function, and configuring algorithm parameters; generating an initial solution meeting the ecological flow constraint, and repairing the solution which does not meet the constraint after genetic manipulation to ensure the feasibility of the solution; performing non-dominated sorting on the population to divide Pareto frontier layers, calculating a congestion distance of a solution in each frontier layer, realizing genetic manipulation through tournament selection, binary crossover simulation and polynomial variation, and updating the population in combination with environment selection; and extracting non-dominated solutions, screening optimization schemes from the non-dominated solutions, and outputting monthly scheduling schemes and water transfer and ecological flow shortage statistics of each reservoir under each scheme. According to the invention, automatic distribution of the ecological base flow, the basic ecological flow and the target ecological flow in the interannual period is realized.

The invention provides an axial flux motor multi-objective optimization method based on an improved homing religious algorithm. Firstly, key structure parameters are screened through sensitivity analysis, and the optimization range of the key structure parameters is determined; then, sample data is generated by adopting improved Latin hypercube sampling, and a data set is constructed through finite element simulation; then, performing automatic optimization on smooth factors of the generalized regression neural network based on a He-horse optimization algorithm, and establishing a high-precision agent model; improving a horizon algorithm by adopting Tent chaotic mapping, non-dominated sorting and congestion degree distance, and combining the horizon algorithm with the proxy model to carry out multi-objective optimization to generate a Pareto solution set; and finally, selecting an optimal motor parameter according to the target weight. According to the method, parameterized finite element analysis, an optimization agent model and an improved multi-target algorithm are deeply fused, so that the global optimization capability is ensured, the optimization efficiency and precision are remarkably improved, and an effective solution is provided for rapid and accurate design of the axial flux motor.

The invention discloses a heterogeneous cluster parallel multi-stage fuzzy job scheduling method, which comprises the following steps of: receiving a processing request which is submitted by a user and contains multi-stage jobs, and modeling task execution time, data transmission time and request deadline into triangular fuzzy numbers, constructing a scheduling model taking the minimization of the total lease cost and the minimization of the request tardiness as targets; carrying out global exploration by adopting an improved second-generation non-dominated sorting genetic algorithm to generate parent and offspring populations; based on population similarity threshold judgment, dynamically triggering local search guided by a multi-agent near-end strategy optimization algorithm, and adaptively selecting a local search operator for each individual to generate an adjacent population; combining various populations, performing non-dominated sorting and crowding distance calculation, and screening out a new generation of populations; and iterating the process until convergence, and outputting a Pareto optimal solution set. According to the method, the problem of multi-target scheduling with fuzzy time variables in a heterogeneous environment is effectively solved, and the quality of a scheduling scheme and the algorithm search efficiency are improved.

The invention discloses a limited transportation resource flexible job shop scheduling system and scheduling method based on a novel evolutionary algorithm, and relates to the technical field of optimal scheduling, and the method comprises the steps: calculating the process completion time of a new scheduling scheme, and generating an optimal scheduling plan; performing non-dominated sorting on the optimal scheduling plan, calculating a congestion distance of a solution, and performing solution set screening to generate an optimal Pareto solution set; and integrating the optimized objective functions, generating comprehensive objective functions, and selecting an optimal scheduling scheme corresponding to the minimum comprehensive objective function based on the optimized Pareto solution set. According to the invention, a self-adaptive crossover and mutation probability adjustment mechanism is introduced through the genetic scheduling module, the global search capability of the evolutionary algorithm is improved, an optimization objective function is constructed through operation data, and the equipment utilization rate and the production efficiency are improved in combination with a self-adaptive evolutionary strategy and multi-objective optimization.

This invention discloses a multi-objective optimization method for high-pile piers and abutments based on an improved Grey Wolf algorithm. The method includes the following steps: establishing a design variable with pile inclination and torsion angle as the design variables, and simultaneously minimizing the standard deviation of pile driving force, maximum pull-out force, and maximum pile bending moment as the optimization objective; designing an integer index discrete encoding strategy to map discrete inclination and torsion angle parameters in the project to integer indices; using an improved multi-objective Grey Wolf optimization algorithm for solving the problem, efficiently searching for Pareto optimal solutions by introducing a nonlinear convergence factor, external archive maintenance, and a leader selection mechanism based on crowding distance; and finally outputting a series of pile placement schemes that achieve a balance between pile driving force uniformity, pull-out force, and bending moment control. This invention achieves collaborative optimization of multiple engineering objectives, with strong algorithm adaptability and significant optimization effects, providing scientific and efficient design decision support for high-pile piers and abutments and other pile foundation projects.

The invention relates to the technical field of cloud computing and distributed computing scheduling, in particular to a server-free MapReduce multi-target scheduling optimization method. Comprising the following steps: constructing a directed acyclic graph, coding a resource allocation decision into a solution from a source node to a target node, generating an initial population by adopting a hybrid initialization strategy, evaluating fitness, calculating a congestion distance of a solution in each non-dominated leading edge, and sequentially executing tournament selection, a self-adaptive crossover strategy and a topological repair variation strategy. And periodically executing a solution injection strategy guided by heuristic search, carrying out crowding distance selection, updating the global Pareto optimal solution set, outputting the current global Pareto optimal solution set when the time is up, and otherwise, returning to iteration. The method has the positive effects that the execution cost and the execution time of the MapReduce operation are balanced, the global Pareto optimal solution set quality of the multi-target scheduling scheme is improved, and the convergence speed and the robustness of the algorithm are enhanced.

The embodiment of the invention relates to the field of agricultural chemical production base site selection, in particular to an agricultural chemical production base multi-target site selection method and device, equipment and a medium. A specific embodiment of the method comprises the following steps: determining an agricultural chemical production base multi-target site selection model; performing fitness non-dominated sorting processing on the initial population; determining iteration information; executing the following iteration steps: determining a filial generation site selection population corresponding to the initial site selection population; performing combination processing on the initial site selection population and the offspring site selection population; performing non-dominated crowding distance sorting processing on the combined site selection population to generate a target offspring site selection population; executing the following determination steps: updating the iteration information to generate target iteration information; determining a new population for site selection; and determining the site selection new population as the agricultural chemical production base multi-target site selection information. According to the embodiment, when a large-scale population is processed, the efficiency of non-dominated sorting can be improved.

The application belongs to the technical field of semi-active suspension control method, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization algorithm, which comprises self-adaptive grid division of an external archive of the MOPSO algorithm to determine a global optimum with the most diversity and a global optimum with the most convergence; evolution state of a population is judged according to a distribution entropy change quantity of all optimal solutions in the external archive, and a global optimum gBest for guiding evolution is selected; flight parameters of population evolution are adjusted according to a change quantity of local crowding distance of current population evolution to balance evolution trend of the population; the above two types of core mechanisms are fused, a multi-information fusion multi-objective particle swarm optimization algorithm is proposed, and the algorithm is used for determination of weight coefficients of a semi-active suspension LQR control strategy. The novel multi-objective optimization algorithm effectively realizes balance between convergence precision and population diversity by synergistically integrating external archive information and current population dynamic information.

The invention belongs to the technical field of unmanned underwater vehicle path planning, and discloses an unmanned underwater vehicle multi-target path planning method and system. According to the multi-target path planning method for the unmanned underwater vehicle, the path length and the path safety serve as optimization targets, firstly, environment modeling is carried out by adopting a grid method combined with a topographic information gray value, and a path smoothness strategy is proposed according to a Bessel function; secondly, introducing an improved particle swarm optimization algorithm, and realizing the balance of diversity and convergence through crowding distance sorting and a Pareto frontier updating strategy; and finally, through non-dominated solution screening, outputting an optimal path solution set. According to the path planning method, the path searching capability and the path distribution diversity can be effectively improved during multi-target path planning, the method has high engineering applicability, and especially under the double consideration of the path length and the path safety index, the good solving and planning capability can be kept.

The invention relates to the technical field of snack product research and development, and discloses a snack product prototype promotion method and device based on deep learning and a cloud platform. The method comprises the following steps: constructing a time sequence multi-modal training set containing formula, process and business metadata and sensory evaluation values, firstly establishing a first model by using a deep neural network, and implementing optimization training for multiple stages such as initialization, data sampling, buffer area capacity, data disruption and hyper-parameter search; the second model takes the physical and chemical simulation value as the input and outputs the corrected predicted value to replace the original theoretical value, the first model is retrained, and then a Pareto frontier-crowding degree distance multi-target algorithm is utilized to efficiently screen out a prototype with a high sensory score from the candidate library for trial production; and if the actually measured deviation exceeds a threshold value, actually measured data is returned to trigger incremental learning, only the last two layers of the model are finely adjusted, and an exponential attenuation weight and an elastic weight are adopted for consolidation to prevent catastrophic forgetting, so that data-model closed-loop iteration is realized, and snack prototypes meeting market requirements are continuously output.

The invention discloses a pump station multi-objective optimization scheduling method based on a multi-strategy enhanced African vulture algorithm, and relates to the technical field of water conservancy projects. The method comprises the following steps: acquiring basic data of pump station operation, and establishing a multi-target optimization scheduling model which takes minimization of electric charge and minimization of the quadratic sum of water shortage of a water receiving area as target functions during the pump station operation period and takes water pump power constraint, water pump pumping capacity constraint and starting-up water pump number constraint as constraint conditions; according to the multi-strategy enhanced multi-target African vulture algorithm, a crowding degree distance sorting mechanism and a hypercube mechanism are adopted to select double leaders to guide a population, an archive-based crossover and mutation strategy is adopted to reduce the possibility that the multi-target algorithm falls into local optimum, and a progressive repair strategy is adopted to repair an out-of-border solution after a main loop is finished. And the multi-strategy enhanced multi-target African vulture algorithm is adopted to carry out pump station multi-target optimization scheduling simulation, so that the operation efficiency of the pump station can be improved.

The present application relates to cloud computing and distributed computing scheduling technical field, specifically belong to a kind of serverless MapReduce multi-objective scheduling optimization method, including: constructing directed acyclic graph, resource allocation decision is encoded as the solution from source node to target node, initial population is generated using hybrid initialization strategy, fitness evaluation, the crowded distance of solution in each non-dominated front is calculated, in turn execute tournament selection, adaptive crossover strategy, topological repair mutation strategy, periodically execute heuristic search guided solution injection strategy, crowded distance selection is carried out, the global Pareto optimal solution set is updated, the global Pareto optimal solution set of current time output is achieved, otherwise return iteration. The present application has the positive effect of balancing the execution cost and execution time of MapReduce job, improving the global Pareto optimal solution set quality of multi-objective scheduling scheme, enhancing the convergence speed and robustness of algorithm.

The invention relates to the field of jacket structure optimization design, and particularly discloses a jacket structure multi-objective intelligent optimization method, which comprises the following steps: establishing a jacket structure multi-objective optimization mathematical model; performing parameter initialization and fitness value calculation, and screening initial schemes to form an initial Pareto solution set; calculating the congestion distance of each solution in the initial Pareto solution set; performing multi-stage iterative optimization, gradually approaching a non-dominated solution, and generating a new scheme; the multi-target fitness value of the new scheme is calculated, and the performance of the new scheme on multiple targets is evaluated; updating a Pareto solution set, and performing solution set fusion and screening on the initial scheme and the new scheme; performing non-dominated sorting on the updated Pareto solution set, and screening a rank 1 layer scheme as a core reference of the next round of iteration; and outputting an optimization result after the maximum number of iterations is reached. By adopting the method, the possibility of multi-target tradeoff can be more comprehensively covered, an optimization mechanism is more suitable for an actual engineering scene, the engineering conversion cost is reduced, and the engineering availability of a design scheme is improved.

The application discloses a distributed server resource allocation method and system, electronic equipment and storage medium. The method determines a variation pool containing a plurality of variation parents; generates a new population containing a plurality of new individuals according to the plurality of variation parents in the variation pool and the sorting values in the first sorting result corresponding to each variation parent; combines the new population and the current population as a total population, performs fast non-dominated sorting on each individual in the total population to obtain a second sorting result; selects a plurality of individuals from the total population according to the sorting values in the second sorting result, calculates the second dynamic crowding distance of each individual corresponding to the maximum sorting value meeting the first preset condition in the plurality of individuals; selects a plurality of elite individuals from the plurality of individuals according to the second dynamic crowding distance to construct an elite population, and iterates the elite population as the current population until a preset iteration number is reached to obtain a target elite population. The application can improve the rationality of distributed server resource allocation.

This invention discloses a method for coordinated optimization of MPPT (Multi-Level Testing) and capacity in a wind power-to-hydrogen system. Meteorological data and system operating parameters are collected, a component mathematical model is established, and the optimization objective and operating boundary are determined. A two-layer collaborative algorithm (upper quantum layer and lower myxomycete layer) is employed to optimize MPPT control parameters and system capacity configuration respectively, and a two-layer collaborative feedback update is performed. The parent generation's non-dominated optimal solution, quantum layer offspring, and myxomycete layer offspring are merged to form a joint population. The Pareto front solution set is screened through non-dominated sorting and crowding distance calculation. After iterative convergence, the Pareto optimal solution set is output, generating a matching MPPT control parameter and capacity configuration scheme. This invention can solve the problems of response lag, susceptibility to local optima, and disconnect between control and capacity matching under wind speed fluctuations, thereby improving wind energy utilization.

The invention discloses a multi-target energy-saving distributed flow shop group scheduling method and system, and relates to the technical field of intelligent production scheduling and optimizing.The method comprises the steps that operation parameters of a distributed flow shop group scheduling scene are obtained, constructing a multi-objective mixed integer linear programming model taking minimization of the maximum completion time and the total energy consumption as optimization objectives; generating an initial population containing multiple feasible solutions by adopting multiple initialization strategies, and dividing the initial population into two sub-populations by adopting a Pareto frontier-based classification mechanism; constructing a dual Q learning mechanism, establishing two independent Q tables to respectively optimize two targets, generating a candidate solution in each sub-population by adopting an adaptive iteration greedy algorithm, a Q learning algorithm and a hybrid algorithm of the adaptive iteration greedy algorithm and the Q learning algorithm, screening an optimal solution by adopting non-dominated sorting and crowding distance sorting to update the sub-population, and continuously iterating and updating until a termination condition is met; and combining the solutions of the two sub-populations to form a Pareto frontier solution set, and obtaining an optimal scheduling scheme with a better effect.

The application relates to a key region identification method based on a four-dimensional characterization index. The method comprises the following steps: based on a sample set, calculating an information surface entropy, a maximum gradient consistency, a local-global similarity divergence and a local-global extreme point ratio to form a four-dimensional characterization index vector of a key region; fusing the four-dimensional characterization index vector based on a Pareto dominance relationship, dividing candidate key regions into different front levels through non-dominated sorting, calculating the crowding distance of each front region, and screening the key region in the test sample space in combination with the front level and the crowding distance. The method can improve the utilization efficiency of a new type of aero-engine digital twin test resources.

This invention discloses a multi-objective parameter intelligent optimization method and system for axial magnetic field permanent magnet motors. The method includes generating a population based on the design variables and their value range constraints of the axial magnetic field permanent magnet motor; generating a set of candidate design schemes based on the current population using an optimizer; calling a simulation evaluator to perform boundary clipping, integer variable rounding, and geometric constraint prediction; performing electromagnetic simulation and calculating the objective vector and total constraint violation degree of the candidate design schemes; backfilling the data to the NSGA-II algorithm; performing non-dominated sorting of the parent and child populations of the current population based on the Deb constraint dominance relationship and calculating the crowding distance within the same non-dominated layer; selecting individuals from the population according to the principle of prioritizing non-dominated levels and having larger crowding distances to obtain an updated population; and finally outputting the optimal candidate design scheme. This invention aims to reduce the proportion of invalid simulations and improve the optimization efficiency of multi-objective parameter optimization for axial magnetic field permanent magnet motor design.

This application relates to a method, apparatus, device, storage medium, and program product for coupling optimization of AC quantum parameters. The method includes: initializing an initial population to obtain an initialized first population; spatially mapping the individuals in the first population according to a preset coupling index model to obtain a second population; selecting individuals in the second population based on their non-dominance level and crowding distance to obtain a third population; performing crossover and mutation operations on the individuals in the third population to generate a fourth population; performing iterative optimization based on the fourth population, and outputting the second parameters of the optimal individuals obtained during the iterative optimization process after completion. This method can effectively improve the performance of AC quantum signals, thereby achieving efficient optimization of AC quantum signals.

The invention discloses a buoyancy tank type photovoltaic connector rigidity rapid discrimination and design optimization method, and relates to the connector rigidity optimization field, the method comprises the following steps: constructing a coupling calculation framework of a DMB method, a standard genetic algorithm and a non-dominated sorting genetic algorithm NSGA-II algorithm, using the DMB method to calculate the hydroelastic response of a buoyancy tank type photovoltaic OFPV system, and determining the rigidity of the buoyancy tank type photovoltaic connector. An NSGA-II algorithm is used to obtain a Pareto optimal solution set, a tradeoff relation among multiple targets is calculated and coordinated through non-dominated sorting and congestion distance, and the multiple targets comprise displacement targets, non-displacement targets and connecting piece bending moments in the buoyancy tank type OFPV system; and constructing a heaving displacement attenuation mode as a key criterion for quickly evaluating the performance of the hinged structure, and quickly judging whether the rigidity of the connecting piece needs to be optimized or not. According to the method, a multi-objective optimization framework is established to solve conflicts among displacement targets, non-displacement targets and connector bending moment, an obtained Pareto solution set is superior to a hinge structure, and a low connector bending moment level is kept.

The invention discloses a multi-objective optimization method for cross-regional scheduling of agricultural machinery in multiple regions of a mountainous region, and the method comprises the steps: firstly, employing an improved multi-constraint density peak clustering algorithm, calculating the local density through a Gaussian kernel function, and carrying out the intelligent grouping of scattered farmlands through comprehensive consideration of multiple factors, such as geographical distance, terrain constraint, time window limitation, and the like; secondly, establishing a dual-objective optimization model covering starting cost, distance cost, penalty cost, waiting time, transfer time and operation time, and meanwhile, minimizing scheduling cost and completion time; a core adopts a memory enhanced grey wolf optimization (MEGWO) algorithm to solve, integrates improvement strategies such as optimal point set initialization, a double-population memory mechanism, a differential evolution operator, random local search and linear population reduction, and processes a multi-objective optimization problem in combination with non-dominated sorting and a crowding distance mechanism. And the intelligent level and the operation efficiency of agricultural machinery dispatching under the complex mountain terrain are obviously improved.

The invention discloses an optimal selection method for tension control parameters of a steel wire rope of a roller of a drilling machine, and the optimal selection method is based on a multi-target grey wolf-particle swarm optimization algorithm (ITCMMOGWOPSO of improved Tent chaos mapping. The optimal selection method comprises the following steps of: selecting the optimal selection method for the tension control parameters of the steel wire rope of the roller of the drilling machine, and selecting the optimal selection method for the tension control parameters of the steel wire rope of the roller of the drilling machine according to the optimal selection method for the tension control parameters of the steel wire rope of the roller of the drilling machine, wherein the optimal selection method is based on the ITCMMOGWOPSO of the multi-target grey wolf-particle swarm optimization algorithm. The implementation process of the method comprises the following steps: (1) generating an initial PID parameter population based on improved Tent chaotic mapping; (2) carrying out a hybrid optimization process combining grey wolf optimization and particle swarm optimization, and updating candidate solutions with the assistance of a double-chaos learning mechanism; (3) updating the Pareto solution set by adopting non-dominated sorting and congestion degree distance; and (4) judging whether a termination condition is met or not, if so, outputting the optimal PID parameter, and otherwise, turning to the step (2). The dynamic performance and robustness of steel wire rope tension control are improved, efficient and accurate tension adjustment is achieved, and the method is suitable for an automatic bit feeding system.

The invention discloses a reservoir group scheduling rule optimization method based on an early stop strategy, and belongs to the technical field of hydraulic engineering, and the method comprises the following steps: obtaining an early stop index based on a time period guarantee rate in reservoir scheduling and individual diversity in an evolutionary algorithm; obtaining an individual grade index according to the non-dominated grade and the congestion distance; according to the early stop index and the individual grade index, an early stop time period threshold value and an early stop index threshold value are obtained; according to the early stop time period threshold value and the early stop index threshold value, early stop judgment and optimization are conducted on individuals in the multi-target parameter-simulation-optimization algorithm, and optimization of the reservoir group scheduling rule is achieved. According to the method, the overall optimization period of the reservoir group is remarkably shortened, the optimization efficiency is improved, the calculation cost is reduced, and a new technical approach is provided for solving the reservoir group rule optimization problem caused by huge calculation amount in the prior art.

The invention discloses an optimization method for a multi-modal multi-target charging station site selection problem, and the method comprises the steps: 1, initializing a population, and generating a layout scheme of an initial charging station based on demand point data; step 2, generating a new layout scheme through self-adaptive selection, crossover and mutation operation; and step 3, retaining a multi-modal solution, maintaining population diversity by adopting an improved crowding distance and a special crowding distance, and screening out a high-quality scheme by applying a non-dominated sorting and proportional selection mechanism. According to the novel multi-modal multi-objective optimization method for charging station site selection, optimization is carried out at the same time from the two dimensions of multi-modal features and multi-objective tradeoff, more multi-modal solutions can be found while the Pareto optimal solution is found, and a more efficient solution is selected according to the specific requirements of a user.

The application discloses a site selection method and device and a storage medium. A first candidate population corresponding to M to-be-sited objects is determined, and the first candidate population includes N candidate solution vectors. The candidate solution vectors correspond to candidate addresses of at least one to-be-sited object. For each candidate solution vector in the first candidate population, the fitness of the candidate solution vector is determined based on a crowded distance, a cost and a blind area loss corresponding to the candidate solution vector. The first candidate population is adjusted based on the fitness of each candidate solution vector in the first candidate population, and a new first candidate population is obtained. If the number of adjustment times of the first candidate population is less than an adjustment threshold, the fitness of each candidate solution vector in the new first candidate population is continuously determined, and the new first candidate population is adjusted based on the fitness, until the number of adjustment times of the first candidate population is equal to the adjustment threshold. A target solution vector is determined from the first candidate population, and the to-be-sited objects can be reasonably deployed.

The invention discloses an improved optimization method for internal elements of a comprehensive energy service system, which introduces a multi-objective optimization thought based on non-dominated sorting and crowding distance sorting on the basis of the traditional improved optimization steps, and forms an initialized primer population by using chaotic mapping to improve the exploration capability and global optimization performance of an algorithm, so as to improve the optimization efficiency of the algorithm. And then continuing to introduce an approximate ideal solution sorting method based on subjective and objective combination to take an optimal compromise solution of a clear algorithm as an optimal solution. According to the method, maximum utilization of effective output power of multiple elements in the comprehensive energy service system is facilitated, overall planning and balancing among multiple targets are achieved, and important supplement is provided for a theoretical system in the fields of energy economics and management science.