CN111080035A - Global path planning method based on improved quantum particle swarm optimization algorithm - Google Patents
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Abstract
The invention is suitable for the technical field of path planning, and provides a global path planning method based on an improved quantum-behaved particle swarm optimization algorithm, which comprises the following steps: s1, creating a grid map; s2, setting parameters; s3, initializing particle swarm; s4, adding one to the iteration number, and updating the individual best position and fitness value of each current particle; s5, updating the global best position and the fitness value; s6, calculating the average value of the individual best positions of all the particles in the iteration; s7, predicting the position of each particle in the next iteration; and S8, performing multipoint intersection operation, combining new particle swarms, detecting whether the global convergence is achieved or whether the iteration times reach the maximum iteration times, if the detection result is negative, executing the step S4, and if the detection result is positive, outputting the optimal position on each dimension to obtain the global planning path. The method solves the defects of 'early-maturing' phenomenon and local optimization of the traditional QPSO algorithm.
Description
Technical Field
The invention belongs to the technical field of path planning, and provides a global path planning method based on an improved quantum particle swarm optimization algorithm.
Background
Path planning is one of core technologies for realizing autonomous navigation of a mobile robot, and has become a hot problem for research of many experts and scholars in the field of mobile robots. The path planning methods for mobile robots are roughly classified into three categories, namely, a global path planning method, a local path planning method and a hybrid path planning method. The global path planning is aimed at finding a collision-free optimal path from the starting position to the target position for the mobile robot under the condition that the global static environment information is completely known. The global path planning comprises two parts of environment modeling and a path searching algorithm. Common environment models include a grid graph method, a visual graph method, a Voronoi graph method and the like; the path search algorithm includes a genetic algorithm, an ant colony algorithm, a Particle Swarm Optimization (PSO) algorithm, a Quantum Particle Swarm Optimization (QPSO) algorithm, and the like. In the existing path planning technical scheme, most of the path planning technical scheme is realized based on an intelligent algorithm. For example, a path planning method based on Genetic Algorithm (GA) gradually obtains an optimal solution to a problem in a population consisting of a plurality of chromosomes through operations such as selection, crossover, mutation and the like according to the principle of biological evolution. The method has the advantages that the algorithm is convenient to use, has better global search capability, is easy to fuse with other intelligent algorithms, and improves the performance of the algorithm. However, the method has the defects of easy local optimization, long operation time, low convergence rate and the like. For example, a path planning method based on a Particle Swarm Optimization (PSO) algorithm, which is a swarm algorithm with global optimization capability, has the advantages of high operation speed and strong local search capability, but cannot search in the whole feasible solution space due to the limitation of the particle speed, and is easy to fall into local optimization. The Quantum Particle Swarm Optimization (QPSO) algorithm is an intelligent algorithm which is put forward from the quantum mechanics perspective and solves the problem that the PSO algorithm cannot guarantee global convergence, is an improved algorithm which adds a quantum physical idea in an evolutionary search strategy of a classical PSO algorithm, updates the particle position by establishing a potential well model and a particle swarm with quantum behaviors and introducing an average best position, and has the defects of 'premature' phenomenon and local optimization falling in the traditional QPSO algorithm.
Disclosure of Invention
The embodiment of the invention provides a global path planning method based on an improved quantum particle swarm optimization algorithm, and the traditional QPSO algorithm has the defects of premature phenomenon and local optimization.
The invention is realized in such a way, and provides a global path planning method based on an improved quantum particle swarm optimization algorithm, which specifically comprises the following steps:
s1, creating a grid map and establishing an environment model;
s2, setting the number N of particles, the dimension D of the particles, the maximum iteration number M, the expansion-contraction coefficient α and the proportion lambda of the particles participating in the cross operation;
s3, initializing particle swarm, setting initial individual best position Pi(0) Global best position Pg(0) Calculating to obtain an initial average best position;
s4, adding one to the iteration number, and updating the individual best position P of the current particlei(t) and its corresponding fitness value;
s5, updating the global best position in the current iteration and the corresponding fitness value thereof, S6, calculating the average value of the individual best positions of all particles in the current iteration, namely the average best position;
s7, predicting the position of each particle in the next iteration;
s8, selecting partial particles based on the set proportion lambda to carry out multipoint intersection operation, combining the particles subjected to the multipoint intersection operation with the residual particles to form a new particle swarm, detecting whether the particle swarm is globally converged or whether the iteration times reaches the maximum iteration times, if the detection result is negative, executing a step S4, if the detection result is positive, outputting the optimal position in each dimension, and forming a global planning path based on the optimal position in each dimension.
Further, the calculation formula of the particle at the next iteration position is specifically as follows:
pi,j＝fj(t)·Pi,j(t)+|1-fj(t)|·Pg,j(t)，fj(t)～U(0,1)
wherein α is the contraction-expansion coefficient, Xi,j(t +1) represents the position of the ith particle in the jth dimension in the t +1 th iteration, pi,j(t) represents the individual best position of the ith particle in the jth dimension in the tth iteration, Xi,j(t) represents the position of the ith particle in the jth dimension in the tth iteration, mbestjRepresents the mean best position, u, of the jth dimension in the t iterationi,j(t)、fj(t) each represent a uniformly distributed function, P, of the obedient interval U (0,1)g,j(t) represents the global best position, p, of the ith particle in the jth dimension in the tth iterationi,jRepresenting the local attraction factor for the j-th dimension.
Further, the method for updating the global optimal position and the corresponding fitness value specifically includes:
taking the individual best position with the minimum fitness value as the global best position P of the iterationg(t) comparing the global best position P of the current iterationg(t) corresponding fitness value F [ P ]g(t)]Fitness value F [ G (t-1) corresponding to global best position G (t-1) of last iteration]Comparing the current global best position Pg(t) updating to the global best position corresponding to the smaller fitness value.
Further, the method for updating the individual optimal position and the corresponding fitness value specifically comprises the following steps:
calculating the fitness value F [ X ] of each particle after the iterationi(t)]The fitness value F [ X ] after the iteration is carried outi(t)]Fitness value F [ P ] after last iterationi(t-1)]Comparing, updating the current fitness to a smaller fitness value, and updating the position corresponding to the smaller fitness value to the individual best position P of each current particlei(t)。
The global path planning method based on the improved quantum particle swarm optimization algorithm provided by the invention solves the problems of 'precocity' phenomenon and local optimization falling in the traditional QPSO algorithm, and effectively plans a global optimal path.
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Fig. 1 is a flowchart of a global path planning method based on an improved quantum-behaved particle swarm optimization algorithm according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 is a flowchart of a global path planning method based on an improved quantum-behaved particle swarm optimization algorithm, which includes the following steps:
s1, creating a grid map and establishing an environment model;
s2, setting the number N of particles, the dimension D of the particles, the maximum iteration number M, the expansion-contraction coefficient α and the proportion lambda of the particles participating in the cross operation;
s3, initializing particle swarm, setting initial individual best position Pi(0) Global best position Pg(0) And calculating to obtain an initial average best position.
S4, adding one to the iteration number, and calculating the fitness value F [ X ] of each particle after the current iterationi(t)]The fitness value F [ X ] after the iteration is carried outi(t)]Fitness value F [ P ] after last iterationi(t-1)]Comparing, updating the current fitness to a smaller fitness value, and updating the position corresponding to the smaller fitness value to the best position P of each current particlei(t), the calculation formula is specifically as follows:
wherein, Fi(x) Representing the ith particle fitness; pi(t) represents the individual best position of the ith particle at the tth iteration, (x)j,yj) Denotes the coordinates of the ith particle in the jth dimension, (x)j-1,yj-1) Denotes the coordinates of the ith particle in the j-1 dimension, Xi(t) the current position, P, of the ith particle at the tth iterationi(t-1) represents the individual best position of the ith particle at the t-1 iteration.
S5, selecting the minimum value in the fitness values of the N particles, and taking the position corresponding to the minimum fitness value as the global best position P of the current iterationg(t) comparing the global best position P of the current iterationg(t) corresponding fitness value F [ P ]g(t)]Fitness value F [ G (t-1) corresponding to global best position G (t-1) of last iteration]Comparing the current global best position Pg(t) updating the global best position corresponding to the smaller fitness value, wherein the calculation formula is as follows:
wherein, Pg(t) represents the global best position among all particles at the tth iteration.
S6, calculating the average value of the individual best positions of all the particles in the iteration, namely the average best position;
wherein, mbestj(t) represents the average of the current individual best positions of all particles in the jth dimension at the tth iteration, pi,j(t) represents the individual best position of the ith particle in the jth dimension in the tth iteration.
S7, predicting the position of each particle in the next iteration;
the method comprises the following steps of self-adaptively adjusting the size of a local search space according to the search state of particles, adding a random variable to each dimension of a local attractor, updating the positions of the particles of each dimension, and predicting the position of the particle of the next iteration, wherein the formula is as follows:
pi,j＝fj(t)·Pi,j(t)+|1-fj(t)|·Pg,j(t)，fj(t)～U(0,1)
wherein α is contraction-expansion coefficient, generally, α value is linearly reduced from 1 to 0.5, and better effect can be obtained, Xi,j(t +1) represents the position of the ith particle in the jth dimension in the t +1 th iteration, pi,j(t) represents the individual best position of the ith particle in the jth dimension in the tth iteration, Xi,j(t) represents the position of the ith particle in the jth dimension in the tth iteration, mbestjRepresents the mean best position, u, of the jth dimension in the t iterationi,j(t) represents, fj(t) denotes a uniform distribution function, P, of the obedient interval U (0,1)g,j(t) represents the global best position, p, of the ith particle in the jth dimension in the tth iterationi,jRepresenting the local attraction factor for the j-th dimension.
S8, selecting partial particles based on the set proportion lambda to carry out multipoint intersection operation, combining the particles subjected to the multipoint intersection operation with the residual particles to form a new particle swarm, detecting whether the particle swarm is globally converged or whether the iteration times reaches the maximum iteration times, if the detection result is negative, executing a step S4, if the detection result is positive, outputting the optimal position in each dimension, and forming a global planning path based on the optimal position in each dimension.
The global path planning method based on the improved quantum particle swarm optimization algorithm provided by the invention solves the problems of 'precocity' phenomenon and local optimization falling in the traditional QPSO algorithm, and effectively plans a global optimal path.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (4)
1. A global path planning method based on an improved quantum particle swarm optimization algorithm is characterized by specifically comprising the following steps:
s1, creating a grid map and establishing an environment model;
s2, setting the number N of particles, the dimension D of the particles, the maximum iteration number M, the expansion-contraction coefficient α and the proportion lambda of the particles participating in the cross operation;
s3, initializing particle swarm, setting initial individual best position Pi(0) Global best position Pg(0) Calculating to obtain an initial average best position;
s4, adding one to the iteration number, and updating the individual best position P of the current particlei(t) and its corresponding fitness value;
s5, updating the global best position in the current iteration and the corresponding fitness value;
s6, calculating the average value of the individual best positions of all the particles in the iteration, namely the average best position;
s7, predicting the position of each particle in the next iteration;
s8, selecting partial particles based on the set proportion lambda to carry out multipoint intersection operation, combining the particles subjected to the multipoint intersection operation with the residual particles to form a new particle swarm, detecting whether the particle swarm is globally converged or whether the iteration times reaches the maximum iteration times, if the detection result is negative, executing a step S4, if the detection result is positive, outputting the optimal position in each dimension, and forming a global planning path based on the optimal position in each dimension.
2. The global path planning method based on the improved quantum-behaved particle swarm optimization algorithm as claimed in claim 1, wherein the calculation formula of the particle at the next iteration position is as follows:
pi,j＝fj(t)·Pi,j(t)+|1-fj(t)|·Pg,j(t)，fj(t)～U(0,1)
wherein α is the contraction-expansion coefficient, Xi,j(t +1) represents the position of the ith particle in the jth dimension in the t +1 th iteration, pi,j(t) represents the individual best position of the ith particle in the jth dimension in the tth iteration, Xi,j(t) represents the position of the ith particle in the jth dimension in the tth iteration, mbestjRepresents the mean best position, u, of the jth dimension in the t iterationi,j(t)、fj(t) each represent a uniformly distributed function, P, of the obedient interval U (0,1)g,j(t) represents the global best position, p, of the ith particle in the jth dimension in the tth iterationi,jRepresenting the local attraction factor for the j-th dimension.
3. The global path planning method based on the improved quantum-behaved particle swarm optimization algorithm as claimed in claim 1, wherein the method for updating the global optimal position and the corresponding fitness value specifically comprises the following steps:
taking the individual best position with the minimum fitness value as the global best position P of the iterationg(t) comparing the global best position P of the current iterationg(t) corresponding fitness value F [ P ]g(t)]Fitness value F [ G (t-1) corresponding to global best position G (t-1) of last iteration]Comparing the current global best position Pg(t) updating to the global best position corresponding to the smaller fitness value.
4. The global path planning method based on the improved quantum-behaved particle swarm optimization algorithm as claimed in claim 1, wherein the updating method of the individual optimal position and the corresponding fitness value is specifically as follows:
calculating the fitness value F [ X ] of each particle after the iterationi(t)]The fitness value F [ X ] after the iteration is carried outi(t)]Fitness value F [ P ] after last iterationi(t-1)]Comparing, updating the current fitness to a smaller fitness value, and updating the position corresponding to the smaller fitness value to the individual best position P of each current particlei(t)。
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