67results about How to "Improve solution quality" patented technology

The invention discloses an optimization method for solving an omni-channel logistics distribution problem. In a first stage, a Lagrange relaxation technology is used to solve a LAP (Location-allocation Problem) problem. In a second stage, adaptive large-scale neighborhood search is used for solving a multi-vehicle-type vehicle path problem, a feasible solution can be searched in a large range in a solution space through one group of simple destruction and reconstruction algorithms, and a situation of falling into local optimum can be effectively avoided. Meanwhile, a simulated annealing acceptance criteria realized in the adaptive large-scale neighborhood search can guarantee the quality of a solution and the convergence of the algorithm, a result is output after the algorithm is executed for an appointed iteration number, a time constraint requirement can be met, and a good vehicle distribution scheme is solved for enterprises in limiting time. The simpleness of the Lagrange relaxation technology is combined with the efficiency of the adaptive large-scale neighborhood search, so that the integral solving efficiency of a mixed algorithm is high, and the problem of omni-channel logistics distribution can be effectively solved.

An airplane flight path planning method based on the pigeon-inspired optimization algorithm includes steps of establishing an uncertainty track prediction model, determining the path to be optimized within the specified area, and obtaining an optimal path using the pigeon-inspired optimization algorithm. The pigeon-inspired optimization algorithm uses map and compass operators and performs landmark operations to obtain the optimal path. The device that performs the path planning includes an access module for getting the regional path information; a building module for setting up the trajectory prediction model including uncertainties; a determining module, which utilizes the regional path information and the trajectory prediction model to determine the trajectories which need optimization; and an optimization module, which uses the pigeon-inspired optimization algorithm to optimize the trajectories.

The invention discloses a large-scale operation workshop scheduling method based on bottleneck equipment decomposition. The large-scale operation workshop scheduling method based on the bottleneck equipment decomposition comprises the following steps of (1) acquiring data and modeling; (2) carrying out recognition on bottleneck equipment based on a key path method; (3) sorting and encoding the bottleneck equipment and non-bottleneck equipment; (4) generating an initial chromosome population; (5) carrying out cross and mutation operations on the chromosome population; (6) inoculating an immune operator to the chromosome population; (7) carrying out decoding and fitness value calculation operations on chromosomes; (8) updating an optimal chromosome and an optimal fitness value of an algorithm; (9) judging whether a method ending rule is achieved or not, starting a step (10) if the method ending rule is achieved, and otherwise, jumping to the step (5) to carry out the next iteration; (10) finding out the optimal chromosome from the step (9) to decode, and obtaining a scheduling command to schedule. According to the large-scale operation workshop scheduling method based on the bottleneck equipment decomposition, which is disclosed by the invention, a satisfactory scheduling scheme can be obtained in a shorter time, the production efficiency of an operation workshop can be improved, and the large-scale operation workshop scheduling method based on the bottleneck equipment decomposition can be applied to scheduling management and optimization of the production process of the workshop.

A computer-based airplane flight path planning method based on the pigeon-inspired optimization (PIO) algorithm includes steps of establishing an uncertainty prediction model, determining the path to be optimized, and obtaining an optimal path using the PIO algorithm for a flight controller onboard to execute. The PIO algorithm treats a pigeon flock as a scale-free network, applies map and compass operators to the scale-free network, and performs landmark operations to obtain the optimal path. The device that performs the path planning includes an access module for obtaining the regional environment information and a flight controller onboard the airplane. The flight controller includes a building module for setting up the trajectory prediction model including uncertainties; a determining module to determine the trajectories which need optimization; an optimization module, which uses the PIO algorithm to optimize the flight path; and a computer memory module.

The invention relates to a multi-population simulated annealing hybrid genetic algorithm based on similarity expelling. The multi-population simulated annealing hybrid genetic algorithm includes the following steps: coding is carried out; initialization parameters are set; initial populations are created; fitness values are calculated; selecting operation is carried out; interlace operation is carried out; mutation operation is carried out; gene overturning operation is carried out; simulated annealing Metropolis rules are judged; migration operation based on similarity expelling is carried out; optimal storage is carried out; judgment is ended. The migration operation based on similarity expelling particularly includes the following steps: calculating the fitness values of individuals in a source population and a target population; selecting the individual with the largest fitness value from the source population to serve as the individual to be immigrated; conducting similarity calculation; conducting expelling replacement. The multi-population genetic algorithm with simulated annealing operation can improve the local search capability of the multi-population genetic algorithm, and the algorithm can search for approximate solutions even though optimal solutions to a larger extent. The individual similarity judgment is additionally carried out, attention is paid to differences between the individuals, the diversity of populations is maintained, premature convergence of the genetic algorithm is avoided, the solving quality of the algorithm is improved, and the algorithm is closer to the optimal solutions.

The invention discloses a two-stage initiative separation method based on normalized spectral clustering and constrained spectral clustering. The method comprises the steps of: in the first stage, recognizing coherent generator groups by using normalized spectral clustering and forming pairwise constraints by using the coherent generator groups; and in the second stage, finding separation sections satisfying the pairwise constraints and with the minimum active power flow shock by using constrained spectral clustering. When a system needs to be separated into a plurality of islands, a recursive bisection method can be adopted. The algorithm provided by the invention takes two factors, i.e., generator coherency and the minimum active power flow shock, into account, and the initiative separation problem can be calculated in real time without simplifying system topology.

The invention discloses a hybrid ant colony algorithm for a VRP problem and an implementation system thereof. The algorithm comprises the steps of (S1) allowing all ants in an ant colony to independently construct a solution of the VRP problem and optimizing the solution by using a local search operation, (S2) performing a pheromone perturbation strategy to adjust a pheromone matrix if an iterative optimal solution remains unchanged in successive iterations, wherein the iterative optimal solution is an optimal solution in solutions constructed by all ants in a single time of iteration, (S3) starting a simulated annealing algorithm to search for a better solution if the optimal solution remains unchanged in the successive iterations so far and taking an optimal solution so far of the ant colony algorithm as an initial solution, (S4) updating the pheromone matrix according to the quality of an ant solution and updating the optimal solution so far, (S5) repeating the steps (S1), (S2), (S3) and (S4) until an obtained optimal solution satisfies a termination condition. According to the hybrid ant colony algorithm for a VRP problem and the implementation system thereof, the purposes of high-quality solution and strong robustness for the VRP problem can be achieved.

The invention belongs to the technical field of network communication, and discloses a network reliability model and a hybrid intelligent optimization method. According to the invention, the network reliability model adds related evaluation indexes of the network invulnerability, optimization design is performed from the two aspects of survivability and invulnerability, a node is allowed to lose effectiveness with a certain probability in the optimization design, optimization design is performed on the a network topology structure, and a defect of single limiting condition of an existing reliability model is overcome, thereby being more perfect and confirming to an actual application scenario. The optimization method disclosed by the invention provides a hybrid intelligent algorithm combining a generic algorithm and an ant colony algorithm in allusion to the characteristics that the generic algorithm is poor in local search ability but high in overall search ability and that the ant colony algorithm is poor in total search ability but high in local search ability, thereby making the best of the two algorithms, overcoming intrinsic defects of the single intelligent algorithm, and improving the solving quality of a solution.

The invention discloses a multi-target urban logistics distribution path planning method. The method comprises the following steps: decomposing a three-target vehicle path problem with a time window into a plurality of single-target sub-problems through a group of uniformly distributed weight vectors; initializing the sub-problems by adopting a heuristic strategy; generating a filial generation for the sub-problem by using an evolutionary operator, and designing a target-oriented neighborhood operator to be combined with a variable neighborhood descent algorithm to serve as a local search strategy so as to improve the solving quality of the sub-problem; updating the solution of the sub-problem by adopting a Chebyshev aggregation function; optimizing a non-dominated solution in the archivesby adopting an external archive strategy based on a sorting and congestion degree mechanism; and S3, repeating the steps S3 to S4 until the set maximum number of iterations is reached, and providinga group of feasible vehicle distribution schemes for multi-target urban logistics distribution. Compared with single-target optimization, the method can provide richer decision information for a decision maker, and considers the quality of the solution on the premise of ensuring the convergence and diversity of the algorithm.

A method for optimizing public traffic network features that the simulated annealing algorithm is used as a frame, and the whole-day total operation cost of operation company is minimized as an objective to obtain initial line network under the frame. The initial line network is scattered to form the line network unit, which is used as the input network, and the genetic algorithm is embedded to optimize it. The public transportation line network optimization model is constructed to minimize the total travel time of all travelers, and the simplified new line network is formed. The change of operation cost is compared to determine whether the convergence condition is reached. The invention combines the simulated annealing algorithm with the genetic algorithm, which ensures the global searching ability of the optimization process and avoids the algorithm from falling into the local optimal solution, thereby improving the solution quality. At the same time, the design concept of 'element'is proposed to promote the combination of multi-objective optimization process, and the convergence condition of sub-heuristic algorithm is improved by two-temperature cooperative control iteration, thus overcoming the common shortcomings of sub-heuristic algorithm that the convergence condition is difficult to define.

The invention discloses a software and hardware partitioning method on the basis of improved brainstorming algorithms. The software and hardware partitioning method includes initializing parameters; initializing cluster centers; starting iteration updating and ranking individuals from small to large according to fitness values; sequentially starting to compute the distance from each individual toeach cluster center from the first ranked individuals; updating optimal individuals in each cluster; randomly selecting an individual from the clusters and generating a new individual; shifting the randomly selected individual towards global optimal individuals by random lengths randomly generating a new individual meeting hardware area constraint conditions and replacing the randomly selected individual with the new individual; completing an iteration updating process; outputting the optimal individuals to be used as optimal software and hardware partitioning schemes. First-rank individuals sorted according to the fitness values are the global optimal individuals. The software and hardware partitioning method has the advantages that cluster modes and individual updating modes are improved, accordingly, the efficiency of each iteration process can be effectively enhanced, premature convergence can be prevented, the global optimization ability can be omitted, the solution quality can beeffectively enhanced, and the convergence speeds can be effectively increased.

The invention discloses a flexible job shop self-adaptive batch scheduling method with a preparation process, which considers a batch problem and a flexible job shop scheduling problem at the same time, and adjusts and optimizes a job process scheduling mode of a job process by means of dynamic adjustment greedy decoding through internal circulation in a genetic algorithm iterative processing process. A scheduling optimization result is used as a basis of self-adaptive batching, and batches and a batch division mode of the operation process are adjusted and optimized through external circulation by means of a self-adaptive batching strategy, so that simultaneous optimization of a batching problem and a flexible operation workshop scheduling problem is realized; by means of the method, the utilization rate of the machining equipment in the interval period can be effectively increased, the solving quality of the genetic algorithm is improved, the problems of large search space, low efficiency and the like existing in batching are solved, feasibility and effectiveness are achieved for solving and considering preparation procedures and unequal-batch and batched flexible job shop batch scheduling problems, and the method is suitable for batch scheduling of flexible job shops. And the batch scheduling efficiency can be optimized.

The invention discloses an optimal scheduling method for a take-out distribution process, and belongs to the technical field of vehicle scheduling intelligent optimal scheduling. According to the invention, a scheduling model and an optimization objective of the take-out distribution process are provided, so that the expression of the scheduling process is clear and definite; Initial pheromone intensity is obtained by adopting algorithm steps, an improved path selection mode and a mechanism of self-adaptive correction parameters are provided, and an algorithm is instructed to carry out effective global search; And a variable neighborhood local search strategy is executed on all vehicles serving the client, so that the local development capability of the algorithm is further improved, and the solution quality is remarkably improved.

The invention relates to an iterative learning control method based on a balanced single evolution cuckoo algorithm. a novel equilibrium single evolution evaluation strategy is given; each generationof evolution only randomly updates the single dimension of the target function; the dimensionality of random updating obeys integer uniform distribution; combining with other dimensions to form a newcandidate solution; evaluating the candidate solution, if the candidate solution is superior to the fitness value of the previous generation of function, keeping the updated candidate solution and continuing to evolve until an algorithm stop condition is met, and only receiving an update value capable of improving the current candidate solution by adopting a greedy rule, so that the targeted adjustment of a search direction in an optimization process is ensured, and the efficiency is not influenced. According to the method, the global search capability and the local optimization capability ofthe cuckoo algorithm can be effectively balanced, the hysteresis phenomenon appearing at the end of execution of the optimization algorithm is avoided, and therefore the global search speed and the convergence precision of the algorithm are improved.

A method is described for generating an undisturbed signal out of an audio signal including a disturbing signal. The method comprises the steps of: estimating auto-correlation matrices and cross-correlation vectors of the equation of the Wiener filtering problem, calculating the coefficients of the solution vector of the equation of the Wiener filtering problem, evaluating the quality of the calculated coefficients, controlling the estimation step depending on the quality of the calculated coefficients, generating a correction signal out of the disturbing signal depending on the calculated coefficients, and correcting the audio signal depending on the correction signal.

The invention relates to a bi-phase anisotropic medium reservoir parametric inversion method based on a niche master-slave parallel genetic algorithm. According to the method, the niche master-slave parallel genetic algorithm is used for solving bi-phase anisotropic medium reservoir parameters, the core ideology includes that a system comprises a master processor and a plurality of slave processors, the master processor monitors a whole population, at a fitness calculation stage, the master processor distributes calculation of the fitness to all slave processors, collects results after calculation and then performs operations such as niche elimination, selection, cross and variation to generate a new generation of population so as to finish one circulation, and the calculation efficiency of reservoir parametric inversion is improved greatly. According to the method, a concept of sharing degree is introduced in the reservoir parameter evolution solving process, substantial growth of some individuals are limited through adjustment of the fitness of each individual, niche evolution environments are created, and the capacity for solving multiple-peak reservoir parametric inversion optimization problems and the solving quality through the genetic algorithm are improved. The bi-phase medium parametric inversion method is widely applied to parametric inversion processes of oil and gas reservoirs.

A method for determining an alternative representation of an optimization model reduces model input through compact representation of model parameters. Model generation is performed at varying levels of complexity (approximation) depending on pre-defined, business approved thresholds.