CN117474036A - Advancing method based on fusion strategy intelligent algorithm - Google Patents
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Abstract
The invention belongs to the technical field of algorithm prediction, in particular to a traveling method based on a fusion strategy intelligent algorithm, which comprises the following steps: s1, initializing a particle swarm, distributing an initial position and a speed for the particle swarm, and considering problem domain constraint; evaluating the particle fitness by using an objective function, and updating according to the history and global optimal information; fusing a deselected selection strategy and a parent selection strategy, screening in the current particle swarm, and recombining and evolving particles; and continuously iterating until the specific termination condition is met, and outputting an optimal path. The invention further develops a prototype of the multi-target traffic path planning system, and comprehensively evaluates the performance of the system in a simulation environment, so that the system can not only effectively solve the congestion problem, but also find application in other traffic related fields such as new energy automobile planning and the like. The research not only provides a new prediction algorithm framework, but also successfully applies the algorithm to actual scenes, and has higher practical and popularization values.
Description
Technical Field
The invention relates to the technical field of intelligent algorithm prediction, in particular to a traveling method based on a fusion strategy intelligent algorithm.
Background
At present, with the rapid increase of private car guarantee amount, urban traffic pressure is continuously increased, and traditional navigation software focuses on shortest distance and time, and comprehensive analysis such as global planning functions of multiple factors such as energy consumption, environmental influence and the like is lacking. This results in a truly green and modern travel decision making difficult to achieve. In fact, in a complex urban road traffic environment, drivers face various uncertainties, and the optimal solution is often not achieved by the shortest distance alone. In addition, the social concern for resource environment protection is that a new generation traffic navigation system capable of dynamically and comprehensively considering multiple targets is developed, synchronous optimization and balance of travel time, cost, carbon emission and the like are realized, travel decisions are more intelligent and green, and the system has important significance in relieving urban traffic pressure and reducing carbon dioxide and emission.
Technically, to realize multi-objective planning and optimization of traffic paths, a scientific time, cost and environmental impact prediction model needs to be established, and meanwhile, an intelligent algorithm capable of processing multi-objective optimization needs to be designed. The existing method based on multi-target evolutionary computation has the characteristics of strong multi-target particle swarm optimization and repair seeking capability, high convergence speed, easiness in implementation and the like, and becomes a potential technology. By adopting the multi-target particle swarm algorithm to dynamically adjust the weight of each target, the self-adaptive optimization of traffic path time, cost, carbon emission and the like can be realized, and an optimal scheme meeting different requirements is generated. Therefore, the application of the multi-target particle swarm algorithm in traffic path planning is researched, and the method has important significance for developing the next-generation intelligent green traffic navigation system.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the invention provides a traveling method based on a fusion strategy intelligent algorithm, which solves the problem of vehicle path planning.
(II) technical scheme
The invention adopts the following technical scheme for realizing the purposes:
the advancing method based on the intelligent algorithm of the fusion strategy comprises the following steps:
s1, initializing a particle swarm, distributing an initial position and a speed for the particle swarm, and considering problem domain constraint; evaluating the particle fitness by using an objective function, and updating according to the history and global optimal information; fusing a deselected selection strategy and a parent selection strategy, screening in the current particle swarm, and recombining and evolving particles; continuously iterating until a specific termination condition is met, and then outputting an optimal path;
s2, an evaluation process: calculating the fitness of each particle by using an objective function, and updating the speed and the position of the particle according to the historical optimal position and the global optimal position;
s3, fusion strategy: based on the fitness score, eliminating particles with lower fitness from the particle swarm by utilizing an elimination selection strategy, so as to ensure the excellent properties of the swarm; selecting a part of high-quality particles in the current particle swarm as a parent by using a parent selection strategy to provide genes for subsequent recombination; generating new particles by utilizing operations such as crossing, mutation and the like on the basis of parent particles, and incorporating the new particles into a particle swarm;
s4, checking a model result: checking whether a termination condition is met, if yes, outputting a current optimal solution, otherwise, returning to the step S2; particles with optimal fitness are output as a result of the travel prediction and give corresponding prediction errors and accuracy.
Further, the process of combining the phase out selection with the parent selection strategy further comprises: based on fitness scoring, particles with lower fitness are eliminated from the current particle swarm by using an elimination selection strategy, particles with higher fitness are selected from the rest particles by using a father selection strategy to serve as father, genes are provided for subsequent particle recombination and evolution, and the optimal position and the global optimal position of each particle in the historical iteration process are recorded for comparison and updating in the subsequent iteration, so that the convergence rate of an algorithm is improved.
Further, for implementation of the fusion strategy, new particles are generated by operations such as crossing and mutation on the basis of parent particles, and the newly generated particles are incorporated into the current particle swarm, so that diversity and adaptability of the particle swarm are maintained.
Further, for the model result inspection, in step S4, a termination condition, a preset fitness scoring threshold, etc. are set to ensure the convergence and the calculation efficiency of the algorithm; outputting a current optimal solution and corresponding prediction errors and accuracy according to the termination condition so as to improve the practicability of the algorithm; and if the termination condition is not met, returning to the step S2 to continuously execute the algorithm until the termination condition is met or the preset maximum iteration number is reached, so as to improve the solving accuracy and the practicability of the algorithm.
(III) beneficial effects
Compared with the prior art, the invention provides a traveling method based on a fusion strategy intelligent algorithm, which comprises the following steps of
The beneficial effects are that:
the invention combines a maximum fitness value elimination selection strategy and a child multiplexing parent selection strategy, is a great innovation based on the traditional particle swarm optimization algorithm, has stronger advancement and originality, can solve multi-objective conflict and complex constraint in the actual path optimization problem, has high practicability and wide application prospect, has potential application value in the fields of logistics, transportation, network design and the like, can more accurately position high-quality solutions while maintaining diversity, realizes high-precision and high-efficiency optimization, has better performance than the traditional algorithm, has stronger self-adaptability and universality due to the combination of the two strategies, can adapt to the path optimization problems of different types and different properties, is favorable for realizing reasonable utilization and optimal allocation of resources and reduces invalid travelling and carbon emission through better path planning, and has environmental protection value.
Drawings
FIG. 1 is a schematic diagram of a model training process in accordance with the present invention;
fig. 2 is a schematic diagram of a simple path for the application of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
As shown in fig. 2, one embodiment of the present invention proposes: the particle swarm optimization algorithm fused with the strategy optimizes the traffic path by combining the global updating strategy and the local updating strategy through the double updating strategy particle swarm optimization algorithm in the running path optimization problem; and comprehensively considering a plurality of indexes such as path length, path time, carbon emission and the like by utilizing a multi-objective optimization method to obtain an optimal traffic path. The advantage of this patent lies in: the optimal traffic path can be quickly found, and the traffic efficiency and economic benefit are improved; the system can adapt to different traffic environments and requirements, and has good flexibility and expandability;
the advancing method based on the intelligent algorithm of the fusion strategy specifically comprises the following steps:
s1, initializing a particle swarm, distributing an initial position and a speed for the particle swarm, and considering problem domain constraint; evaluating the particle fitness by using an objective function, and updating according to the history and global optimal information; fusing a deselected selection strategy and a parent selection strategy, screening in the current particle swarm, and recombining and evolving particles; and continuously iterating until the specific termination condition is met, and outputting an optimal path.
S2, an evaluation process: calculating the fitness of each particle by using an objective function, and updating the speed and the position of the particle according to the historical optimal position and the global optimal position;
s3, fusion strategy: based on the fitness score, eliminating particles with lower fitness from the particle swarm by utilizing an elimination selection strategy, so as to ensure the excellent properties of the swarm; selecting a part of high-quality particles in the current particle swarm as a parent by using a parent selection strategy to provide genes for subsequent recombination; generating new particles by utilizing operations such as crossing, mutation and the like on the basis of parent particles, and incorporating the new particles into a particle swarm;
s4, checking a model result: checking whether a termination condition is met, if yes, outputting a current optimal solution, otherwise, returning to the step S2; particles with optimal fitness are output as a result of the travel prediction and give corresponding prediction errors and accuracy.
2. The process of fusing a phase out selection with a parent selection strategy of claim 1, further comprising: based on fitness score, eliminating particles with lower fitness from the current particle swarm by using an elimination selection strategy, and selecting particles with higher fitness from the rest particles by using a parent selection strategy as parents to provide genes for subsequent particle recombination and evolution.
The fusion strategy adopts a fusion maximum fitness value elimination selection strategy and a filial generation multiplexing type parent selection strategy, and the implementation process is as follows:
a. initialization of
An initial parent population is generated and fitness values for all individuals are calculated. Population speed and location update formula:
if the parent population is P in size and the offspring population is C in size, then when selecting a new generation parent, we will select P individuals from the P+C combined population. If fitness scaling is used, the probability that each individual is selected can be expressed as:
wherein f i Is the fitness value of the ith individual, so that individuals with higher fitness have a greater probability of being selected to enter the next generation.
b. Selection of
And selecting a part of individuals from the parent population to cross and mutate according to the fitness value of each individual, and generating offspring individuals.
c. Crossover and mutation
From the selected individuals, offspring are generated by crossover and mutation operations.
d. Assessing fitness of offspring
And calculating the fitness value of each individual in the offspring population.
e. Fusion and elimination
The parent and offspring are combined to form a large population.
In the large population, the optimal individuals are selected as the new parent population based on the fitness value, and the selection strategy can be eliminated by utilizing the maximum fitness value, so that the individuals with the highest fitness are preferentially reserved.
f. Checking termination conditions
Judging whether a termination condition, such as a preset iteration number or an adaptability threshold value, is met, outputting a current optimal individual and ending the algorithm; otherwise, returning to the step 2, and continuing the next iteration.
g. Outputting the result
When the algorithm is terminated, the found optimal solution or optimal individual is output.
3. In claim 1, for implementation of the fusion strategy, new particles are generated by operations such as crossing and mutation on the basis of parent particles, and the newly generated particles are incorporated into the current particle swarm, so as to maintain the diversity and adaptability of the particle swarm.
Crossing: assume that two parents A and B are present, whose gene sequences are as follows:
A:11001|10011
B:00110|01101
two subunits obtained after single-point crossing at "|" are:
C:11001|01101
D:00110|10011
variation: the fourth mutation was performed on individual "1100110", so that a new individual "1101110" was obtained.
The probability of crossover and mutation often appears as a parameter of the algorithm. The crossover probability is P c The mutation probability is P m 。
Wherein:
P c,i is the crossover probability of the ith individual, P m,i Is the variation probability of the ith individual.
P c,min And P c,max Is the minimum and maximum possible value of the crossover probability, P m,min And P m,max Is the smallest and largest possible value of the probability of variation.
f i Is the fitness value of the i-th individual.
f min And f max Is the minimum and maximum value of fitness values in the population.
For each pair of parents:
with P c And (3) performing cross operation on the probabilities of the sub-individuals to generate the sub-individuals.
Then, at each gene locus, P m Is mutated.
Execution process
a. Selecting a parent individual: based on fitness, two individuals are selected from the current parent population to cross.
b. Performing crossover: with a certain probability (e.g. P c ) And executing the crossover operation to generate the child.
c. Performing mutation: at each genetic locus of a child, with a certain probability (e.g.P m ) And executing mutation operation.
d. Assessing the sub-individuals: and calculating the fitness value of the sub-individuals obtained after the crossing and the mutation.
4. In the method according to claim 1, for model result inspection, in the step S4, a termination condition, a preset fitness scoring threshold value, etc. are set to ensure the convergence and the calculation efficiency of the algorithm; outputting a current optimal solution and corresponding prediction errors and accuracy according to the termination condition so as to improve the practicability of the algorithm; and if the termination condition is not met, returning to the step S2 to continuously execute the algorithm until the termination condition is met or the preset maximum iteration number is reached, so as to improve the solving accuracy and the practicability of the algorithm.
5. In claim 1, for the travel path optimization, the path is a moving path in the real world, and the obtained optimal travel path can be integrated with other technologies or tools, such as map services or network monitoring tools, so as to realize visual display and evaluation of the path, and provide modes of simulation and actual execution for selection by users.
1. Objective function:
a. path length:
wherein c e Is the length of path e.
b. Transit time:
the transit time may be related to traffic flow and speed, so is defined as:
wherein v is e Is the maximum speed of path e, f e Is the traffic flow of path e.
c. Carbon emission:
carbon emissions may be related to both velocity and distance, we can define
Where a and b are constants reflecting the extent of influence of distance and velocity on carbon emissions.
2. Path optimization:
since there are three targets, we need to combine these three targets into one integrated objective function using a multi-target optimization method, such as weighted sum method:
minZ＝w 1 ·Z 1 +w 2 ·Z 2 +w 3 ·Z 3
w 1 +w 2 +w 3 ＝1
wherein w is 1 ,w 2 ,w 3 Is a weight, and can be adjusted according to actual conditions and preferences.
Fitness value calculation formula:
3. constraint conditions:
a. time constraint: maximum time limit to ensure travel efficiency.
t total ≤T max
b. Speed constraint:
v min ≤v i ≤v max
c. path selection constraints:
d. carbon emission constraint:
Z total ≤Z max
finally, it should be said that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection of the present invention.
Claims (4)
1. The advancing method based on the intelligent algorithm of the fusion strategy is characterized by comprising the following steps of:
s1, initializing a particle swarm, distributing an initial position and a speed for the particle swarm, and considering problem domain constraint; evaluating the particle fitness by using an objective function, and updating according to the history and global optimal information; fusing a deselected selection strategy and a parent selection strategy, screening in the current particle swarm, and recombining and evolving particles; continuously iterating until a specific termination condition is met, and then outputting an optimal path;
s2, an evaluation process: calculating the fitness of each particle by using an objective function, and updating the speed and the position of the particle according to the historical optimal position and the global optimal position;
s3, fusion strategy: based on the fitness score, eliminating particles with lower fitness from the particle swarm by utilizing an elimination selection strategy, so as to ensure the excellent properties of the swarm; selecting a part of high-quality particles in the current particle swarm as a parent by using a parent selection strategy to provide genes for subsequent recombination; generating new particles by utilizing operations such as crossing, mutation and the like on the basis of parent particles, and incorporating the new particles into a particle swarm;
s4, checking a model result: checking whether a termination condition is met, if yes, outputting a current optimal solution, otherwise, returning to the step S2; particles with optimal fitness are output as a result of the travel prediction and give corresponding prediction errors and accuracy.
2. The fusion policy intelligent algorithm-based travel method according to claim 1, wherein: the process of fusing the obsolete selection with the parent selection strategy further comprises: based on fitness scoring, particles with lower fitness are eliminated from the current particle swarm by using an elimination selection strategy, particles with higher fitness are selected from the rest particles by using a father selection strategy to serve as father, genes are provided for subsequent particle recombination and evolution, and the optimal position and the global optimal position of each particle in the historical iteration process are recorded for comparison and updating in the subsequent iteration, so that the convergence rate of an algorithm is improved.
3. The fusion policy intelligent algorithm-based travel method according to claim 1, wherein: for implementation of the fusion strategy, new particles are generated through operations such as crossing and mutation on the basis of parent particles, and the newly generated particles are incorporated into the current particle swarm so as to keep the diversity and adaptability of the particle swarm.
4. The fusion policy intelligent algorithm-based travel method according to claim 1, wherein: for model result checking, in step S4, setting termination conditions, preset fitness scoring threshold values and the like to ensure the convergence and calculation efficiency of the algorithm; outputting a current optimal solution and corresponding prediction errors and accuracy according to the termination condition so as to improve the practicability of the algorithm; and if the termination condition is not met, returning to the step S2 to continuously execute the algorithm until the termination condition is met or the preset maximum iteration number is reached, so as to improve the solving accuracy and the practicability of the algorithm.
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