349 results about "Multiobjective optimization problem" patented technology

Agent-based models (ABMs) / multi-agent systems (MASs) are one of the most widely used modeling-simulation-analysis approaches for understanding the dynamical behavior of complex systems. These models can be often characterized by several parameters with nonlinear interactions which together determine the global system dynamics, usually measured by different conflicting criteria. One problem that can emerge is that of tuning the controllable system parameters at the local level, in order to reach some desirable global behavior. According to one exemplary embodiment t of the present invention, the tuning of an ABM for emergency response planning can be cast as a multi-objective optimization problem (MOOP). Further, the use of multi-objective evolutionary algorithms (MOEAs) and procedures for exploration and optimization of the resultant search space can be utilized. It is possible to employ conventional MOEAs, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II) and the Pareto Archived Evolution Strategy (PAES), and their performance can be tested for different pairs of objectives for plan evaluation. In the experimental results, the approximate Pareto front of the non-dominated solutions is effectively obtained. Further, a conflict between the proposed objectives can be seen. Additional robustness analysis may be performed to assist policy-makers in selecting a plan according to higher-level information or criteria which is likely not present in the original problem description.

The invention relates to a multi-objective coordination-typed self-adaptive cruise control method for a vehicle, comprising the following steps: 1) according to the detail requirements of the multi-objective coordination-typed self-adaptive cruise control for a vehicle, the performance indicators and I / O restriction of MTC ACC are designed, and multi-objective optimization control problem is established; and 2) MTC ACC control law rolling time domain is used for solving the objective optimal control problem, and the optimal open-loop control quantity is used for carrying out feedback and achieving closed-loop control. Based on the steps, the control method comprises the following four parts of contents: 1. the modeling for the longitudinal dynamics of a traction system; 2. the performance indicators of MTC ACC; 3. the I / O restriction design of MTC ACC; and 4. solution by the MTC ACC control law rolling time domain. By constructing multi-objective optimization problem, the control method not only solves the contradiction among the fuel economy, the track performance and the feeling of the driver, moreover, on the same simulation conditions, compared with the LQ ACC control, the control method simultaneously reduces the fuel consumption and vehicle tracking error of the vehicle, and achieves the multi-objective coordinating control function.

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 discloses a task scheduling method applied to a multilayer shuttle vehicle automatic warehousing system. The task scheduling method comprises the steps of firstly establishing an operation time model of the system according to the operation execution process of a device; then changing the scheduling problems of a multilayer shuttle vehicle and an elevator into a production line parallel operation problem, establishing a task scheduling mathematic model for a task queue in a time window designated in size; for solving the multi-task multi-target optimization problem, designing a non-dominated sorting genetic algorithm with an elitist strategy based on a pareto optimality so as to perform model solution. By applying the task scheduling method, the waiting time of the shuttle vehicle and the idle time of the elevator can be effectively shortened, and accordingly the device utilization rate and handling capacity of a distribution center are improved.

The invention relates to an electric vehicle charging station planning method considering traffic network flow. The method comprises the following steps: referencing an intercepting location model in a traffic field, taking largest captured traffic flow, minimum network loss of a power distribution system and minimum node voltage deviation as an objective, establishing a multi-objective decision model, then determining a reasonable weight coefficient of each objective function after being normalized by using a super efficiency data envelopment analysis evaluation method, and transforming a multi-objective optimization problem to a single objective optimization problem, later putting forward an improved binary particle swarm optimization algorithm to solve a single objective optimization model. The method considers the urban traffic network traffic, electric car driving mileage and location problem of an electric vehicle charging station which affects a distribution network. The example results of the method show that the planned charging station location can provide convenient and fast charging services for more electric cars, and further improve the safety and reliability of the system operation.

The method for generating an integrated guidance law for aerodynamic missiles uses a strength Pareto evolutionary algorithm (SPEA)-based approach for generating an integrated fuzzy guidance law, which includes three separate fuzzy controllers. Each of these fuzzy controllers is activated in a unique region of missile interception. The distribution of membership functions and the associated rules are obtained by solving a nonlinear constrained multi-objective optimization problem in which final time, energy consumption, and miss distance are treated as competing objectives. A Tabu search is utilized to build a library of initial feasible solutions for the multi-objective optimization algorithm. Additionally, a hierarchical clustering technique is utilized to provide the decision maker with a representative and manageable Pareto-optimal set without destroying the characteristics of the trade-off front. A fuzzy-based system is employed to extract the best compromise solution over the trade-off curve.

The invention discloses a sensor target assignment method for a multi-objective optimization differential evolution algorithm. The method includes the steps that objective importance degree calculation is carried out according to objective information, a sensor target assignment constraint multi-objective optimization function is built, distribution scheme codes and initial population chromosomes are generated, offspring scheme populations are generated through the differential evolution algorithm, population combination and screening are carried out, and a distribution scheme Pareto front-end solution set is obtained. The method is combined with the differential evolution algorithm, is easy to use in terms of population difference heuristic random search, is good in robustness and has the advantages of being high in global search ability and the like. A Pareto set multi-objective optimization assignment strategy is provided. A sensor utilization rate function is added on the basis of a sensor target monitoring efficiency function, an assignment problem is converted into a multi-objective optimization problem, sensor resources can be saved as much as possible on the condition that monitoring precision requirements are met, and reasonable and effective assignment of the sensor resources is achieved.

The invention discloses an aircraft multi-objective optimization method based on a self-adaptive agent model, relates to a multi-objective optimization method for treating complex aircraft design, and belongs to the field of aircraft design optimization. According to the aircraft multi-objective optimization method, an integrated preference function is constructed by use of a physical planning method to realize the conversion of the multi-objective optimization problem into a single-objective optimization problem reflecting design preference; next, the self-adaptive agent model is constructed from the integrated preference function and constraint conditions to take the place of a high-accuracy analysis model, and therefore, the problem of great time taken in calculation of optimization design is solved; finally, the constraint problem is converted into a non-constraint problem by use of an augmentation Lagrange multiplier method, and the non-constraint problem is solved by use of a genetic algorithm. The aircraft multi-objective optimization method has the advantages that the solving process of the aircraft multi-objective optimization method taking much time in calculation is simple and efficient, and therefore, a Pareto noninferior solution meeting the requirements of a user can be obtained quickly to shorten the design period of the aircraft, and the design cost is reduced. Besides, the aircraft multi-objective optimization method is high in universality and convenient for program development.

The invention relates to a hybrid electric vehicle reinforcement learning energy management control method. According to the hybrid electric vehicle reinforcement learning energy management control method, by combining an equivalent consumption minimization strategy and a reinforcement learning algorithm and optimizing fuel consumption and battery capacity degradation, a target of optimal use cost of a whole hybrid electric vehicle is achieved. The hybrid electric vehicle reinforcement learning energy management control method comprises the following steps of 1) collecting vehicle state and battery state data; 2) establishing an ICE (Internal Combustion Engine) model, an EM (Electric Motor) model and a transmission system model of the hybrid electric vehicle, a battery internal resistance model and a battery degradation model; and 3) combining the equivalent consumption minimization strategy and the reinforcement learning algorithm, establishing a hybrid electric vehicle energy management strategy, calculating a multi-target optimization problem, and generating a control signal according to a calculation result to distribute engine power and motor power.

One embodiment of the present invention provides a system that uses interval techniques to solve a multi-objective optimization problem. During operation, the system receives a representation of multiple objective functions (ƒ1, . . . , ƒn) at a computer system, wherein (ƒ1, . . . , ƒn) are scalar functions of a vector x=(x1, . . . , xn). The system also receives a representation of a domain of interest for the multiple objective functions. Next, the system performs an interval optimization process to compute guaranteed bounds on a Pareto front for the objective functions (ƒ1, . . . , fn), wherein for each point on the Pareto front, an improvement in one objective function cannot be made without adversely affecting at least one other objective function. While performing the interval optimization process, the system applies a direct-comparison technique between subdomains of the domain of interest to eliminate subdomains that are certainly dominated by other subdomains.

Embodiments of the invention disclose a multi-target-based improved gray wolf optimization algorithm which is used to solve the technical problems that a standard gray wolf algorithm falls into a local optimal value easily and has a low convergence speed and other defects while processing a multi-target optimization problem in the prior art. The method of the embodiments comprises the following steps: S1, setting a wolf pack initialization parameter and a direction correction probability, and randomly initializing wolves' individual positions; S2, calculating an adaptability value of each wolf individual according to a solving target, and selecting the three wolf individuals ranking top; S3, optimizing the wolves' individual positions of a wolf pack, generating moderate wolves, and updating a wolf pack position; S4, executing direction correction operation on the updated wolf pack, controlling the upgraded wolf pack to participate correction of the size of dimensions according to the direction correction probability, and obtaining a corrected wolf pack position; and S5, determining whether an iteration frequency reaches a preset maximum iteration frequency, outputting the corrected wolf pack position as a final optimization result if the iteration frequency reaches the preset maximum iteration frequency, and, if the iteration frequency does not reaches the preset maximum iteration frequency, turning to the S3 so as to continue performing iteration searching.

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

The invention discloses a reactive power optimization method of a power distribution network. The method comprises the steps of 1), building a mathematical model of a fan; 2), initializing the node voltage; 3), building a reactive power planning mathematical model and converting into a multi-objective optimization problem; 4), inputting original data; 5), forming an initial particle swarm and initializing the speeds and the positions of particles; 6), performing load flow calculation and obtaining adaptive values and a current optimum value of the particles; 7), correcting the speeds and the positions of the particles by using an ecological niche weight flight time method; 8), calculating the adaptability of the whole population, obtaining the adaptability value and updating the current optimum solution pBestid; 9), adjusting a control variable of a border crossing point of an individual particle and modifying a border crossing state variable; and 10), determining whether a requirement of an end condition is met, if the requirement of the end condition is met, outputting the result, otherwise, returning to the 6).

The embodiment of the invention discloses a computing task unloading method and device and a computer readable storage medium. The method includes: respectively calculating the communication time forthe user terminal to unload the calculation task, the task execution time for the user terminal to unload the energy consumption to execute the local calculation task and the task execution time for the local execution energy consumption mobile edge calculation MEC server to execute the unloaded calculation task in the mobile edge calculation environment based on the average energy consumption ofthe user and calculating an average response delay based on the sum of the tasks; establishing a multi-objective optimization model for the average energy consumption of the user and the average response delay of the task; and solving the multi-objective optimization model to obtain a plurality of satisfactory calculation unloading decision schemes. According to the method, the calculation task unloading problem is converted into the multi-objective optimization problem and then solved, a plurality of satisfactory unloading decision schemes are obtained, a decision maker can have more choicesin a rapidly changing network environment, and the method is more favorable for adapting to the requirement of changing at any time.

The invention relates to a spiral bevel gear tooth surface loading performance multi-objective optimization method. The method is characterized by comprising the following steps that firstly, a mathematical model of a spiral bevel gear tooth surface loading performance multi-objective optimization problem is established, and test design sample points are obtained; secondly, a tooth surface loadingcontact analysis method considering tooth root bending stress is established, tooth surface loading contact analysis is conducted on the test design sample points, target functions corresponding to the test design sample points and response values of the target functions are obtained, and then an initial sample point set including the test design sample points and the corresponding response values is obtained; thirdly, a Kriging proxy model is fitted on the basis of the initial sample point set, the mathematical model of the spiral bevel gear tooth surface loading performance multi-objectiveoptimization problem is solved, and the optimal solution set of the spiral bevel gear tooth surface loading performance multi-objective optimization problem is obtained. The method is high in calculation efficiency, high in calculation accuracy and capable of being widely applied to spiral bevel gear tooth surface loading performance multi-objective optimization.

The present invention relates to an automobile self-adaptive cruising and sub-working condition control system, which is characterized in that it includes an information collection unit, a state recognition unit, a control decision-making unit and a vehicle dynamics unit; the information collection unit collects driving state information and sends it to State recognition unit, the state recognition unit recognizes the safety state of the self-vehicle and the state of the forward target vehicle relative to the self-vehicle, and sends the recognition result to the control decision-making unit; the control decision-making unit analyzes the actual needs of the driver in each state, and Taking vehicle safety, followability, fuel economy and driver comfort as indicators, a multi-objective optimization problem is established, and the optimal controller is selected according to the state recognition results, and the optimal control acceleration is calculated and processed continuously. The results are sent to the vehicle dynamics unit; the vehicle dynamics unit converts the optimal control acceleration into the desired throttle opening or brake pressure, and realizes the multi-state and working-condition control of the vehicle.

The invention discloses a method and a device for controlling a redundant mechanical arm. The method includes the steps of obtaining current point information and target point information of the redundant mechanical arm; determining a trajectory function corresponding to movement trajectories of the redundant mechanical arm moving from the current points to the target points according to the current point information and the target point information; establishing an equation corresponding to the trajectory function by adopting redundancy space vectors as independent variables; and solving the equation corresponding to the trajectory function according to a target approach method and obtaining the position and the speed of each joint in the redundant mechanical arm corresponding to the movement trajectories. According to the technical scheme, on the premise that a global optimal solution meeting design objectives is obtained, a search space for a multi-objective optimization problem of the redundant mechanical arm can be reduced, dimension explosion problems in the multi-objective solving process can be avoided, calculation amount needed in the control process of the redundant mechanical arm can be simplified, and the reaction speed of the redundant mechanical arm can be increased.

The invention discloses a method for scheduling multi-target testing task based on decomposition and optimal solution following strategies, which belongs to the field of scheduling parallel testing task. The method comprises the following steps: initializing parameter setting; decomposing a multi-target problem into a series of subproblems in an objective function space through the utilization of the method based on the decomposition, and updating and evolving the subproblems through exchanging informations with neighborhood areas; updating reference points and the neighborhood areas of every subproblem; adopting the optimal solution following strategy to allow a solution set to be improved as a whole; and obtaining a more optimal solution through repeated iterations, thereby obtaining a more optimal testing task sequence and a relevant testing scheme collection. The method solves the multi-target optimizing problem based on decomposition strategy, avoids the using of weighted sum method, reduces the effect of human factors, allows the quality of every generation of solution set to be improved as a whole with the adding of the optimal solution following strategy, and finally improves the efficiency of the method for scheduling the multi-target testing task.

The invention discloses a network link performance measurement method. A uniform performance analyzing linear model is established by analyzing the characteristics of network link performance; in the linear model, when rank of a route matrix is not equal to that of an augmented matrix and optimal solution of nonhomogeneous linear equation system can not be obtained, the problem is transformed to a multiobjective optimal problem by mathematic transformation, and genetic algorithm is used for solving sub-optimal solution of the nonhomogeneous linear equation system; and finally the network link performance is obtained by statistic probability distribution of sub-optimal solution in discretization interval. The invention uniforms the network link performance analyzing model as a linear analyzing model, transforms the link performance measurement problem to the multiobjective optimal problem, utilizes the genetic algorithm to solve the sub-optimal solution of the interior network link performance, and obtains link performance parameters by analyzing, such as link delay time and loss rate.

The invention aims at providing a multi-target spectrum allocation method based on an undisposal order preference quantum goose group algorithm, which comprises the following steps of: building a graph theory coloring model of cognitive radio spectrum allocation, initializing the position of the quantum geese and the quantum speed, carrying out the undisposal order preference and congestion degree calculation on individuals in the population according to the adaptability, sequencing the individuals with the same undisposal order preference levels in sequence from higher congestion degrees to lower congestion degrees, carrying out evolution on the population by a quantum goose group evolution rule, generating new quantum speed and position, carrying out undisposal order preference on obtained solutions in an elite solution set nonDomQGSAList and selecting the solutions with the undisposal solution level being 1 as the final Pareto front end solution set. The method solves the discrete multi-target optimization problem, designs the novel undisposal order preference quantum goose group algorithm as the evolution strategy and has the advantages that the convergence speed is high, and the precision is high. In addition, the method provided by the invention has a wider application range.

The embodiment of the invention provides a color matching scheme automatic generating method and device based on personality impression, and belongs to the field of data processing. The method comprises the steps of preprocessing obtained virtual character images to determine a color matching area, specified by a user, of the virtual character images, and adopting a multi-object interactive genetic algorithm (MOIGA) to generate a corresponding target color matching scheme which satisfies the user requirements and is based on the target personality impression according to the color matching area of the virtual character images. By means of the color matching scheme automatic generating method and device based on the personality impression, the multi-object interactive genetic algorithm is applied to color appearance design of virtual characters, a designer can be helped to effectively generate the color matching scheme which can deliver the target personality impression, meet the user preference and is harmonious visually, thus the user does not need to manually handle the multi-object optimizing problem during color planning, instead, what is only needed is to select the preferential color matching scheme from the color matching scheme which can deliver the target personality impression and is harmonious visually, the designer can be helped to accelerate the design process, thework time is shortened, and the workload is reduced.

Aiming at the transformation of a micro grid multi-objective optimization problem into a single-objective optimization problem, the invention provides a micro grid multi-objective optimization methodbased on a Pareto file particle swarm optimization algorithm. The method includes the following steps: establishing multiple optimization objective functions; determining the constraint conditions ofa micro grid; transforming a multi-objective optimization problem represented by the optimization objective functions into a single-objective optimization problem; solving the micro grid multi-objective optimization problem using a Pareto file particle swarm optimization algorithm, and outputting a non-inferior solution set; and determining an optimal solution in the non-inferior solution set according to a preset satisfaction evaluation standard, and optimizing the operation of the micro grid. According to the invention, the output power of each distributed power supply and the charge / discharge of an energy storage device in the micro grid are optimized using the Pareto file multi-objective particle swarm optimization algorithm, external file maintenance and global best position selectionare combined, and the validity and feasibility of the algorithm are verified through a comparative analysis of optimization results.

The embodiment of the invention is to provide an electric energy quality improvement method of a low voltage distribution network distributed energy storage system. A distribution network equivalent model and a locating and sizing optimization model of the distributed energy storage system are established, and a multi-target weight coefficient is determined through an analytic hierarchy process so as to convert the multi-target optimization problem into the single target problem. The optimal access position and the optimal power capacity of the energy storage device in the system are calculated, and finally the optimal operation strategy of the distributed energy storage system is accordingly solved by combining the operation strategy planning model of the distributed energy storage system so that the electric energy quality of the low voltage distribution network can be improved, the economic performance of energy storage investment can also be considered, the risk of investment can be reduced and the utilization rate of equipment can be enhanced. According to the distributed energy storage system planning method, the defects of the existing method are compensated so that the electric energy quality of the low voltage distribution network can be effectively improved.

The invention relates to a particle swarm optimization method based on a complex network. The particle swarm optimization method is used for solving the multiobjective optimization problem in the real world. The particle swarm optimization method based on the complex network comprises the steps that the population network topology is established according to a scale-free network generation mechanism, the optimization space, the population size, the positions of particles and the speeds of the particles are determined, the adaptive value is calculated according to a fitness function, the historical best position of each particle, the historical best position of the corresponding neighbor particle and the global historical best position of the particles are recorded, the positions and the speeds of the particles are updated in an iteration mode every time, the adaptive value is calculated again until iteration is completed, and the global best position is output. The particle swarm optimization method based on the complex network further provides four indexes for evaluating the optimal performance of center particles and non-center particles, the influence in neighborhood, the information transmission capacity, the advantages and disadvantages of the adaptive value and the capacity for maintaining population activeness. By means of the particle swarm optimization method based on the complex network, the local optimum can be effectively avoided, and the convergence rate and the optimization effect for resolving targets are balanced through the application of the particle swarm optimization algorithm.

The invention discloses an optimal design method for an internal structure of a machine tool body. The optimal design method comprises topological optimization of the internal structure of the machine tool body and multi-objective optimization of thickness and height of a ribbed plate of the machine tool body. The topological optimization of the internal structure of the machine tool body comprises the following steps: establishing a body three-dimensional CAD (Computer Aided Design) model; determining a topological optimization region and a non-topological optimization region; establishing a finite element model of the topological optimization; determining boundary conditions; determining optimization objectives and constraint conditions and establishing a finite element model of the topological optimization; the multi-objective optimization of the thickness and the height of the ribbed plate of the machine tool body comprises the following steps: establishing a parameterized finite element model of the body according to the topological optimization result; designing sample points; determining optimization objectives and constraints; performing finite element analysis at the sample points to extract values of the optimization objectives and the constraints at each sample point; establishing agent models of the optimization objectives and the constraints; solving the multi-objective optimization problem. Through the method, the internal structure of the machine tool body is optimized, and the time is saved.

The invention discloses a selective hierarchical integration Gaussian process regression soft measurement modeling method based on evolutionary multi-objective optimization. The method comprises the following steps: firstly, constructing a diversity input variable subset by combining Bootstrapping random resampling and partial mutual information; dividing the corresponding original sample subset into different modeling areas by using a Gaussian mixture model algorithm, establishing a corresponding Gaussian process regression base model, carrying out posteriori probability weighted fusion, constructing a first layer of integrated model EGPR, constructing a multi-objective optimization problem from the perspective of evolutionary optimization, and selecting an EGPR model which is good in performance and meets diversity for final integration. The diversity of sample information and input variable information is fully considered, and the diversity and prediction precision of the base modelcan be effectively ensured. Moreover, due to the introduction of the selective integration strategy, the defect that all local models are fused through traditional integrated learning is effectivelyovercome, the complexity of integrated modeling is remarkably reduced, and the model prediction performance is improved.

The invention provides a parameter optimization design method of a parallel hybrid electric vehicle based on genetic algorithm, aiming at avoiding the phenomenon that through the traditional matchingmethod, a vehicle body has obvious resonance and large noise in a certain vehicle speed range after the matching is completed, including the following steps: firstly, establishing a multi-objective optimization mathematical model to determine calculation formulas of 0-50 km / h acceleration time, fuel economy and vehicle body resonance probability; secondly, using a weight coefficient method to transform the multi-objective optimization problem into a single-objective problem, and applying the genetic algorithm to calculate a quasi-optimal solution of the optimization problem under the premise of meeting the constraint condition; and finally, comparing performances before and after optimization of transmission system parameters to analyze the improvement of the optimization effect.

The invention provides a multi-target peak regulation optimizing method and system. The method comprises the following steps: initial values are respectively assigned for corresponding target weights in pre-established multi-target peak regulation optimizing models; a first peak regulation scheme is obtained according to the multi-target peak regulation optimizing models after the initial values are assigned, and the value of each optimizing target in each multi-target peak regulation optimizing model is determined to be used as the original value of the corresponding optimizing target through the first peak regulation scheme; the entropy weight of each optimizing target is determined respectively according to each original value in an entropy weight method, and each entropy weight is assigned to the corresponding target weight in each multi-target peak regulation optimizing model; and a second peak regulation scheme is obtained according to the multi-target peak regulation optimizing model after the entropy weights are assigned. The multi-target peak regulation optimizing method disclosed by the invention is independent of artificial experiences, the randomness of dereferencing of a multi-target optimizing problem is effectively avoided, and the peak regulation optimizing effect is further improved.

The invention discloses a GEO (Geosynchronous orbit)-UAV (unmanned aerial vehicle) Bi-SAR (synthetic aperture radar) route planning method based on differential evolution. The GEO-UAV Bi-SAR route planning method based on differential evolution comprises 1) generating a three dimensional landform; 2) modeling a UAV accepting station route; 3) modeling the route planning as a multiobjective optimization problem for a constraint condition; 4) utilizing a multiobjective differential evolution algorithm to solve; and 5) obtaining the optimal solution, generating a UAV optimal path, and realizing autonomous navigation and Bi-SAR imaging of the UAV in the three dimensional complicated landform. The GEO-UAV Bi-SAR route planning method based on differential evolution models the UAV route planning problem which comprehensively considers the route length, the flight safety and the SAR imaging performance as a multiobjective optimization problem, and utilizes the improved differential evolution algorithm to solve and obtain a set of optimal UAV accepting station flight routes.

The invention discloses a decomposition-based train operation multi-objective differential evolution algorithm. The algorithm comprises the steps of 1, establishing a train elementary substance pointdynamical model; 2, establishing a train multi-objective optimization model according to the train multi-objective operation requirement; Step 3, decomposing the train operation multi-objective optimization problem into N single-objective optimization sub-problems by adopting a Chebyshev method; 4, in order to ensure the uniformity of the obtained Pareto solution, generating a weight vector by adopting a formula uniform design method; And 5, selecting an evolution strategy to form a differential evolution strategy pool, and improving the diversity and convergence of an evolution process by adopting a self-adaptive differential evolution strategy based on reputation. The train operation multi-objective optimization problem is converted into the single-objective problem, on the basis that the uniformly distributed weight vectors are obtained, multiple control strategies are provided for the train on the premise that safety is guaranteed through the self-adaptive differential evolution strategy, and safe, quasi-point, accurate parking and low-energy-consumption operation of the train are achieved.