256 results about "Mutation operator" patented technology

The invention relates to a mobile-robot route planning method based on an improved genetic algorithm. A raster model is adopted to preprocess a working space of a mobile robot, in a rasterized map, an improved rapid traversing random tree is adopted to generate connections of several clusters between a start point and a target point, portions for the mobile robot to freely walk on in the working space are converted into directed acyclic graphs, and a backtracking method is adopted to generate an initial population which is abundant in diversity and has no infeasible path on the basis of the directed acyclic graphs. Three genetic operators, namely a selection operator, a crossover operator and a mutation operator, are adopted to evolve the population, wherein the selection operator uses a tournament selection strategy, the crossover operator adopts a single-point crossover strategy, and the mutation operator adopts a mutation strategy which displaces an aberrance point with an optimal point in eight-neighbor points of the aberrance point. A quadratic b-spline curve is adopted to smooth an optimal route, and finally, a smooth optimal route is generated. According to the method, the route planning capability of the mobile robot under a complex dynamic environment is effectively improved.

The invention relates to a WSN multi-objective optimization routing method based on the genetic algorithm. According to the method, the advantages of sufficient storage space, sufficient energy and high computing capability of a base station are utilized, multi-path multi-objective optimized routes are searched globally from the WMSNs, and specific coding scheme and selection, intersection and variable operators are designed on the basis of a forward neighbor concept network model and the multi-objective optimization function. The structure is simple, the genetic algorithm and Pareto multi-objective optimization method are introduced, the specific coding scheme and selection, intersection and variable operators are designed, and finally optimized solution is realized; and the multipath multi-objective optimization routes are searched from the WMSNs globally, the stability of the method is improved, and the method is feasible and effective in multipath multi-objective optimization.

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.

Provided is a single-instruction-set heterogeneous multi-core system static task scheduling method. The method includes five steps: step 1, population initialization; step 2, fitness value calculation; step 3, selection operator operation; step 4, cross operator operation; and step 5 variation operator operation. A local sequence represents an executing sequence of two tasks without depending relations, population initialization efficiency and effective individual are greatly improved, the executing sequence of the tasks is determined through a pre-order-relation matrix, and defects of a traditional height value method are overcome. The method can widen a hunting range of optimal individuals. When the population scale is large enough, a part of optimal solutions missed by the height value method can be found so as to obtain a more optimal scheduling sequence. For the same task set, finishing time of the whole task set is short, power consumption is low, and a purpose of energy conservation and consumption reduction is achieved.

The invention relates to the technical field of artificial intelligence algorithms, and provides a method for optimizing a disaster emergency decision system path. The method comprises the steps that S11, an objective function and a fitness function are defined; S12, chromosome coding is carried out; S13, operators are selected according to the fitness function; S14, the operators are crossed; S15, the operators are mutated according to the fitness function and pheromone updating guiding mutation rules; S16, a plurality of sets of optimal solutions are generated, and the best solution is output through the fitness function. The quality and efficiency of the solutions are effectively improved through the pheromone guiding mutation rules of an ant colony algorithm. A large amount of useless redundant iteration of a genetic algorithm is avoided through the positive feedback mechanism and the parallelism of the ant colony algorithm, and the solution speed of the genetic algorithm is further improved. The decision model with a single saving point and multiple disaster points based on continuous supply losses is more suitable for emergency situations, and practical significance is achieved.

The invention discloses a parameter selection method for a support vector machine based on a hybrid bat algorithm. Regularization parameters and RBF kernel parameters have great influences on the learning performance and computation complexity. On the basis of analyzing the advantages and disadvantages of some classical parameter selection methods, an intelligent optimization algorithm is introduced to perform optimization on the parameters. The bat algorithm has the advantages of concurrency, high convergence speed and strong robustness. The bat algorithm is firstly utilized to perform optimization on the SVM parameters, then crossover, selection and mutation operators of differential evolution algorithm are introduced in allusion to a defect of early maturing of the bat algorithm, the position is further adjusted according to the three operators in each iteration process by using a bat individual, the search ability of the algorithm is enhanced, the algorithm is avoided from prematurely falling into a local optimal solution, and finally the SVM parameter selection is optimized by using an improved DEBA algorithm to obtain an excellent effect.

The invention discloses a frequency stabilization control method for an alternating-current/direct-current series-parallel system, which comprises the steps of designing an under-frequency load shedding optimization model considering emergency power generation control and high-voltage direct current power support, providing an objective function giving consideration to the frequency recovery performance and the load shedding amount, selecting the action frequency of an under-frequency load shedding program and the load shedding amount of each turn as control variables, carrying out optimization on a scheme by using a cloud adaptive particle swarm algorithm containing a dimension mutation operator, and realizing intelligent control for the frequency of the alternating-current/direct-current series-parallel system. The control method disclosed by the invention solves a problem of coordination and optimization between the system instability possibly caused by frequency oscillation after the alternating-current/direct-current series-parallel system breaks down and the economy of system frequency recovery; an under-frequency load shedding scheme is optimized, an optimal setting scheme is searched intelligently, the frequency recovery performance is considered, the load shedding amount is minimized, and the transient-state performance and the steady-state performance of the alternating-current/direct-current series-parallel system are improved.

The invention discloses a genetic ant algorithm-based unmanned aerial vehicle global path planning method. The method includes the following steps that: modeling is performed for an environment through an improved grid method; a part of optimal solutions obtained by an MMAS (Max-Min Ant System) algorithm are transformed into the initial solutions of an EGA (Elitist Genetic Algorithm); the MMAS algorithm and the EGA are utilized to simultaneously perform path optimization; and an improved mutation operator and the MMAS algorithm are utilized to perform further optimization, and an optimal path is obtained. According to the genetic ant algorithm-based unmanned aerial vehicle global path planning method of the invention, a new sub-region division method is adopted, and therefore, the expression forms of the solutions are simplified, and the computation quantity and storage quantity of data are decreased; according to the characteristics of the convergence rates of the MMAS algorithm and the EGA, a method according to which iteration optimal solutions and optimal solutions obtained through EGA optimization are adopted each time to jointly update pheromones is adopted, and therefore, the search efficiency of the optimal solution can be improved; and when the algorithm is stagnant, the number of ants is increased, and the EGA mutation operator is improved, and therefore, the search efficiency of the optimal path can be further improved.

Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise using a dynamic mutation rate. This genetic algorithm may comprise generating successive generations of child populations. In executing the genetic algorithm, children may undergo mutations based on a mutation rate. This mutation rate may be dynamic and be based on the characteristics of the children in the generation. Additionally, values may be frozen during execution of the genetic algorithm if the likelihood that the value has converged on a particular value exceeds a threshold.

The invention provides an optimization algorithm based on multi-objective resource-constrained project scheduling model. The scheduling model requires scheduling the start time of each activity to achieve the optimal goal under the condition of satisfying the relevant constraints. Based on the RCPSP model, the invention introduces the optimal resource balance as the objective, and expands the model to a multi-objective model. The solution of RCPSP is mainly based on the heuristic algorithm. When the task list is used to encode chromosomes in the heuristic algorithm, the task list initialized randomly may not satisfy the constraint relation between the top and bottom. The invention provides an individual generation mode based on control relation, which presents a new crossover operator andmutation operator based on the NSGA-II algorithm. The invention can greatly reduce the time complexity of the algorithm, realize the balanced allocation of resources, improve the production efficiencyand save the production cost while ensuring the solution precision of the algorithm, thereby improving the economic benefit of the resource scheduling production process.

The invention relates to a photovoltaic failure detection method based on an improved particle swarm optimization Elman network, which is characterized by comprising the following steps: (1) initializing particle swarm algorithm; (2) constructing an Elman neural network topology structure; (3) determining the particle evaluation function and calculating the particle fitness value; (4) updating theparticles and introducing the mutation operator to obtain new population particles: re-determining the individual extreme value and the global extreme value, and obtaining the optimal particle when reaching the set precision or the maximum number of iterations; (5) obtaining the optimal weight values according to the optimal particles obtained in the step (4) to carry out network training and result prediction. The method obtains the optimal weight value of the neural network through the improved particle swarm algorithm, overcoming the defect of the Elman neural network trapped in local optimal solution, greatly improving the prediction efficiency and speed, and facilitating the maintenance and management of the photovoltaic power generation system.

The invention relates to a wind farm multi-model draught fan optimized arrangement method based on a genetic algorithm. The method includes the following steps that (1) a wind farm region is divided into square meshes which are the same in size according to the diameter of a draught fan, and an integer matrix which is the same in line and row is generated randomly to be used as the initial solution of the algorithm; (2) the individual fitness value of a current generation is calculated; (3) parent individuals participating in crossover are selected through even random selection operators, and then filial generation individuals are generated by the adoption of improved crossover and mutation operators; (4) repairing operators are introduced to the individuals in a population; (5) a Tabu operator is introduced to an optimal solution of the current generation of the population, the optimal solution is used as the initial solution of a Tabu algorithm, and the neighborhood solution of the optimal solution is searched for; (6) whether the biggest number of iterations is reached or not is judged, if yes, the multi-model draught fan optimized arrangement is completed, and if not, the step (2) is executed again. Compared with the prior art, the wind farm multi-model draught fan optimized arrangement method based on the genetic algorithm has the advantages of being visual in coding mode, good in performance index, high in local search capacity, high in expansibility, high in practicability and the like.

The present invention provides a multi-intelligent robot task distribution method facing a dynamic task which mainly solves the multi-task distribution problem of a task state quantity with time-variant characteristics. The method comprises the steps of: obtaining dynamic task feature parameters, combining intelligent robot ability parameters, and establishing a feature equation of a task point state quantity; according to the feature equation, designing an intelligent robot revenue function; according to the revenue function, designing a genetic algorithm fitness function; further designing agenetic algorithm difference selection operator and a local mutation operator, and providing an algorithm repair strategy; and finally, employing the genetic algorithm to generate an intelligent robot task distribution scheme to complete multi-task distribution. The multi-intelligent robot task distribution method takes obtaining of system maximum return as a target to achieve dynamic multi-taskrapid distribution, solve the algorithm chromosome deadlock problem and avoid that search falls into local optimum, and through a multi-stage distribution strategy, the method can fully deploy intelligent robots in the system to participate in task completion so as to improve the whole efficiency of the system.

The present invention discloses an optimization method of hybrid energy storage capacity configuration of a microgrid. The method comprises the following steps of: S1: obtaining historical data in previous years and real-time data of a microgrid to construct a microgrid data matrix; S2: performing normalization processing of random variable; S3: constructing a microgrid energy storage capacity configuration optimization model; and S4: performing microgrid energy storage capacity configuration optimization by employing the multi-target genetic algorithm improved by FCM. The method provided by the invention employs the fuzzy C clustering and Euclidean distance selection intersection parent methods to combine with an adaptive crossover and mutation operator to greatly improve the diversity ofthe genetic algorithm population and avoid falling into local optimum. The optimization method of hybrid energy storage capacity configuration of the microgrid facilitates obtaining of an optimal solution set of the hybrid energy storage capacity configuration to allow decision makers to conveniently make a reasonable final selection and provide technical supports for stable operation and the economical efficiency of the microgrid.

The invention relates to a semantic mutation operator based test case generation and optimization method. The method is characterized by mainly including test case generation and initial test case set optimization, test case generation refers to that semantic mutation operators and mixed execution are combined to generate initial test case sets high in coverage rate as far as possible, and initial test case set optimization includes: capturing operation states and fault detect results of the initial test case sets in semantic mutant execution, optimizing according to related indexes provided by the method, and assessing the optimized test case sets.

The invention discloses a workshop logistics distribution path optimization method based on a genetic algorithm. The workshop logistics distribution path optimization method is used for effectively planning multi-target node logistics distribution paths with priorities in discrete workshops. And on the basis of the layout diagram and the adjacency matrix of the job shop, an algorithm is applied tooptimize the logistics distribution path of the shop, and the objective function is optimized. In traditional multi-target path planning, path planning is divided into a plurality of single target nodes and a path planning problem of a single starting node, but the path planning problem generally can only obtain local optimum rather than global optimum. And a multi-target node path optimization model is established, and a proposed cross operator and a proposed mutation operator are applied from the perspective of global optimization, so that the solving speed is increased, and the solving precision is improved. By the adoption of the method, the path distance of logistics distribution in the workshops can be effectively reduced, the logistics distribution operation efficiency in the workshops can be improved, and conditions are created for improving the production efficiency in the workshops and improving the enterprise income.

Disclosed are a coding and genetic algorithm suitable for a power distribution network and application in distribution network reconfiguration. Coding is conducted through names of nodes, depth of the nodes and the degrees of the nodes, male parents are selected, a later generation is obtained by cross and mutation breeding through a cross operator and a mutation operator, when the male parents are selected to be crossed and mutated, one male parent is selected at random while the other male parent is selected optimally. The cross operator is used for exchanging a connection relation between a certain node and a father node in each tree mapped by two codes. The mutation operator is used for changing a father node of a certain node in each tree mapped by the codes, wherein the certain node is provided with spare branches. Radial constraint does not need to be verified, the situation that an invalid network is operated and repaired repeatedly to obtain a valid network is avoided, and computing time is greatly shortened. The valid network can be obtained certainly in networks of any scales, and the problem that an existing method cannot be applied to reconfiguration of large-scale actual power distribution networks.

The invention provides a robot grid sub-map fusion method based on immune adaptive genetic algorithm. A matrix corresponding to two grid sub-maps is regarded as an antigen. An antibody is plane transformation made by a second grid sub-map. An antibody colony generates a next antibody in operations of copying, crossing and mutation operator basing on affinity degree of the antigen and the antibody. A selection probability calculated according to similar vector distance guarantees multiformity of the antibody. On the base of an immune principle, a crossover probability and a mutation probability are adaptively adjusted according to sufficiency of the antibody to reduce a probability of local optimum. The invention has a high searching efficiency and can effectively search the best plane transformation randomly distributed in a searching space. The invention is especially fit for a multiple mobile robot grid sub-map fusion problem in complex environment. And the invention can realize information sharing among robots as soon as possible and effectively realize coordinating exploration among robots, and improve exploration efficiency.

The invention discloses a method for synthesizing directional diagrams of linear antenna arrays on the basis of wavelet mutation wind drive optimization algorithms. The method includes steps of building models of the linear antenna arrays and determining comprehensive radiation characteristic requirements and objective functions of the antenna arrays; determining the wind drive optimization algorithms and wavelet mutation operator parameters and setting population sizes, weight values of fitness functions and speeds and position boundaries of air particles; randomly generating initial speeds and positions of the air particles, substituting the positions of the air particles into the fitness functions, sorting fitness values according to ascending order, updating population sequences and determining the global optimal positions and the local optimal positions; updating the speeds and the positions of the air particles; selectively carrying out wavelet mutation on the positions of the air particles according to mutation probability; computing fitness values of the air particles at novel positions, sorting the fitness values according to ascending order again, updating the population sequences and updating the global optimal positions and the local optimal positions until the maximum number of iterations are carried out. The method has the advantages of high solving precision and convergence speed.

The invention discloses a method for optimizing the project duration of an engineering project based on potential anti-key working procedures in the technical field of engineering project duration control technologies. The method comprises the following steps: identifying potential anti-key working procedures in an engineering project based on certain technical characteristics; dividing all working procedures of the project into a potential anti-key working procedure set X and a non-potential anti-key working procedure set Y; coding the execution modes of all the working procedures in the potential anti-key working procedure set X to generate an initial group of which the size is NP, and adopting a mode of fastest execution for all the working procedures in the non-potential anti-key working procedure set Y; calculating the total duration value corresponding to each single body in the group and converting the reciprocal of the total duration value into the adaptation value of the single body; adjusting the start time of non-key working procedures; selecting a parent, and producing a child by a single-point crossover operator and a single-point mutation operator; combining the parent and the child to form a new group; and if the maximum genetic algebra is obtained, stopping calculation and outputting an optimal solution, thus obtaining the optimal duration scheme of the project.

The demand for automation design of power electronic circuits becomes higher and higher with the development of the power electronic technology. In the invention, a particle swarm algorithm is applied to the design and optimization of the power electronic circuits, and the invention mainly relates to the power electronics field and the intelligent computation field. In the optimization method, an optimization process is divided into two parts by a decoupling technology to respectively optimize the power transmission of the power electronic circuit and a feedback network. Meanwhile, a mutation operator is introduced into the particle swarm algorithm to increase diversity of the swarm and improve the optimization efficiency of the algorithm. The optimization design of a buck converter is taken as an example for testing, which proves that the optimization method is very effective.