CN111191840B - Multi-unmanned mobile platform task allocation method based on discrete particle swarm optimization algorithm - Google Patents
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Abstract
The invention relates to the technical field of unmanned aerial vehicle maneuvering platform task allocation, and provides a multi-unmanned aerial vehicle maneuvering platform task allocation method based on a discrete particle swarm optimization algorithm. Firstly, acquiring the position of each unmanned maneuvering platform and a task target; then, mapping the task allocation scheme into particles by encoding the particles, and randomly generating initial particles; then comprehensively considering the self-capability, task income cost, path cost, cooperative constraint and other factors of the unmanned mobile platform to construct an adaptability function of task allocation of multiple unmanned mobile platforms; and then, a mutation operator and a crossover operator are adopted to realize the study of the particle to the optimal value, all particles are updated, a greedy strategy is adopted to select particles with better fitness to enter into the next generation of particles, the individual optimal value and the global optimal value are updated at the same time, and the global optimal value is output as the optimal task allocation scheme when the maximum update times are reached. The invention can improve the task allocation efficiency of the unmanned mobile platform.
Description
Technical Field
The invention relates to the technical field of unmanned mobile platform task allocation, in particular to a multi-unmanned mobile platform task allocation method based on a discrete particle swarm optimization algorithm.
Background
Unmanned motorized platforms refer to mobile machine devices that interact with environmental awareness, travel autonomously, and carry equipment or personnel (without a driver). The unmanned motorized platform has the characteristics of strong autonomy, strong loading capacity, long endurance time, good concealment, high maneuverability and the like, and is widely applied to the civil and military fields. The task allocation sets constraint conditions according to the established tasks, and evaluates the constraint conditions by combining the data, and the design team completes the allocation scheme of the maximum benefit at the minimum cost. The task allocation reduces the burden of decision-making staff and operators, can greatly improve the success probability of task execution and reduce the cost, and is the basis of the collaborative execution of the tasks by the unmanned mobile platform.
In recent years, some researches have been started focusing on the problem of a task allocation method in a multi-unmanned mobile platform. At present, when the task allocation of the unmanned aerial vehicle mobile platform is researched, the situations that a plurality of unmanned aerial vehicle mobile platforms execute a single task and a plurality of tasks are generally considered, a mathematical model corresponding to the task allocation system of the unmanned aerial vehicle mobile platform is established by analyzing the task allocation system of the unmanned aerial vehicle mobile platform, and then the actual problem is solved according to constraint conditions. Many classical problem-based task allocation models have been proposed for both single-task and multitasking cases. The mathematical model of single task allocation of the unmanned aerial vehicle mobile platform mainly comprises the following steps: a vehicle path problem model; a multi-travel business problem model; a dynamic network flow optimization model; a multi-unmanned motorized platform collaborative task allocation model.
Aiming at an optimization solution algorithm of a task allocation problem model of a multi-unmanned mobile platform, the research result at the present stage mainly comprises an optimization method, a clustering method, a contract net method and a heuristic method. The optimization method simplifies the task allocation problem into a mathematical model, and obtains a task allocation scheme in polynomial time, wherein common methods include full arrangement, branch delimitation, hungary algorithm and the like; the method can obtain the optimal solution under the problem of small-scale task allocation, but the calculated amount increases exponentially along with the increase of the number of tasks, and the algorithm consumes long time, so the method is not suitable for the problem of large-scale task planning. The clustering method uses the distance of the task as a measure to divide the global task into several subtasks, and the common method is K-Means clustering; the method has good effect in solving the problem of large-scale task allocation, but the method cannot meet the multi-constraint condition of task allocation and cannot realize negotiation among multiple unmanned mobile platforms. The contract net method realizes task allocation by means of marking tasks with excessive cost and transferring the tasks to other individuals; the method realizes distributed task allocation among unmanned mobile platforms, but has low overall efficiency; in addition, the algorithm requires that the individuals be able to communicate with each other, and requires high demands on the network environment. The heuristic method comprehensively considers the distribution effect and the calculation time to obtain a near-optimal solution, has the characteristics of excellent performance and good instantaneity, and the common methods include a particle swarm algorithm, a neural network and the like. The method solves the problem of poor calculation efficiency caused by a complex model, but particles have continuous properties in a speed and position updating mode, and is not suitable for solving the problem of discrete domain distribution of multiple unmanned maneuvering platform tasks.
The conventional task allocation method cannot obtain satisfactory effects on time and global optimal solutions when solving problems, and for large-scale task allocation problems, problems of large data redundancy degree, low solving efficiency and poor instantaneity can occur.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a task allocation method for a multi-unmanned mobile platform based on a discrete particle swarm optimization algorithm, which can improve the task allocation efficiency of the multi-unmanned mobile platform.
The technical scheme of the invention is as follows:
a multi-unmanned mobile platform task allocation method based on a discrete particle swarm optimization algorithm is characterized by comprising the following steps:
step 1: acquiring the total number n of unmanned mobile platforms and the position of the ith unmanned mobile platformTotal number of task objects m, position of j-th task object +.>Wherein i=1, 2, n, j=1, 2, m;
step 2: initializing the update times t=0, and setting the maximum update times as T;
step 3: mapping a task allocation scheme into one particle, randomly generating K initial particles as { X } 1 (t),X 2 (t),...,X k (t),...,X K (t) }; wherein X is k (t) is the kth particle in the t-th update, K ε {1,2,., K }, X k (t)＝(x k1 (t),x k2 (t),...,x kj (t),...,x km (t))，x kj (t)∈{1,2,...,i,...,n}，x kj (t) is expressed in the particle X k The x-th of (t) kj (t) the unmanned motorized platform performing the j-th task;
step 4: constructing fitness function as
f(X k (t))＝e 1 ·f 1 (X k (t))+e 2 ·f 2 (X k (t))
s.t C 1 ,C 2 ,C 3
Wherein f (X) k (t)) is particle X k The fitness function value of (t); e, e 1 、e 2 Are correction coefficients;
f 1 (X k (t)) is particle X k Task revenue cost of (t)
f 1 (X k (t))＝1-P v (X k (t))
Wherein P is v (X k (t)) is particle X k Task revenue of (t)
Wherein P is i For the task execution efficiency of the ith unmanned aerial vehicle platform, V j The value of the j-th task; a, a k-ij As a decision variable, when particle X k A when the ith unmanned mobile platform in (t) performs the jth task k-ij When particle X is =1 k A when the ith unmanned motorized platform of (t) is not performing the jth task k-ij ＝0；V max For maximum value of all tasks, V max ＝max{V 1 ,V 2 ,...,V j ,...,V m }；
f 2 (X k (t)) is particle X k Journey cost of (t)
Wherein D is i For the maximum travel distance of the ith unmanned aerial vehicle platform, d ij For the distance between the ith unmanned motorized platform and the jth mission target,
C 1 、C 2 、C 3 constraint conditions for the collaborative execution of tasks for multiple unmanned motorized platforms:
constraint C 1 : any one task can only be performed once:
constraint C 2 : unmanned motorized platform path length constraints:
constraint C 3 : the maximum execution task number constraint of the unmanned mobile platform:
wherein r is i The number of tasks that are most completed for the ith unmanned motorized platform;
step 5: updating all particles to obtain K updated particles { X } 1 (t+1),X 2 (t+1),...,X k (t+1),...,X K (t+1)}；
Step 6: calculating fitness value { f (X) 1 (t+1)),f(X 2 (t+1)),...,f(X k (t+1)),...,f(X K (t+1))}；
Step 7: further updating all particles to obtain the K-th epsilon {1,2,., K } particles after further updating as
Step 8: updating individual optimum values to
Wherein p is k (t) is the individual optimum, i.e. the best position experienced by the individual particle, p k (t)＝argmin{f(X k (0)),f(X k (1)),…,f(X k (t))}；
Step 9: updating global optimum to
Step 10: let t=t+1, if T < T, go to step 5, if T is greater than or equal to T, execute step 11;
step 11: and outputting the global optimal value as the optimal task allocation scheme.
Further, in the step 5, all the particles are updated to obtain K-th e {1, 2..the K } updated particles as
Wherein omega p Is an individual variant factor, l 1 For individual learning factors, l 2 Is a group learning factor;
F 1 (X k (t)) is particle X k The individual variation function value of (t),
wherein r, s are two numbers randomly selected from {1,2,.. K } and r is not equal to s not equal to K;
let psi be k (t) is a temporary variable that is,
wherein k is 1 Is interval [0,1 ]]A random number thereon;
F 2 (ψ k (t),p k (t)) is a learning operation of the particle on the individual extremum: two different numbers a and b are randomly chosen in {1,2,..m } and ψ will be k The number between positions a and b of (t) is p k Replacing the number between the a position and the b position of (t);
phi is set k (t) is a temporary variable that is,
wherein k is 2 Is interval [0,1 ]]A random number thereon;
F 3 (φ k (t),p g (t)) is a learning operation of particles on the whole extremum: two different numbers a and b are randomly chosen in {1,2,., m }, will be phi k The number between positions a and b of (t) is p g Replacing the number between the a position and the b position of (t);
wherein k is 3 Is interval [0,1 ]]A random number on the same.
The beneficial effects of the invention are as follows:
the invention realizes the mapping from the task allocation scheme to the particle positions by sequentially encoding the particles on the basis of the particle swarm optimization algorithm, and each particle in the swarm adjusts the position of the particle by learning to the same generation particle, thereby enhancing the diversity of the particles. Meanwhile, the invention has the characteristic of memorizing the individual optimal value and the group optimal value, and realizes the study of the particle on the optimal value by adopting a mutation operator and a crossover operator, so as to promote the particle to advance to a more optimal position. According to the invention, the greedy strategy is adopted to select particles with better fitness to be the next generation particles, so that the population optimization can be quickly obtained, the optimal solution can be found in fewer iteration times, and the task allocation efficiency of the multi-unmanned mobile platform is improved.
Drawings
Fig. 1 is a flow chart of a task allocation method of a multi-unmanned mobile platform based on a discrete particle swarm optimization algorithm.
Fig. 2 is a schematic view of a particle in an embodiment.
Fig. 3 is a schematic diagram of the operation of particle learning individual optimal values in the task allocation method of the multi-unmanned mobile platform based on the discrete particle swarm optimization algorithm.
Fig. 4 is an operation schematic diagram of a particle learning global optimum in the task allocation method of the multi-unmanned mobile platform based on the discrete particle swarm optimization algorithm.
Detailed Description
The invention will be further described with reference to the drawings and detailed description.
As shown in fig. 1, the task allocation method of the multi-unmanned mobile platform based on the discrete particle swarm optimization algorithm comprises the following steps:
step 1: acquiring the total number n of unmanned mobile platforms and the position of the ith unmanned mobile platformTotal number of task objects m, position of j-th task object +.>Where i=1, 2,..n, j=1, 2,..m.
Step 2: the number of update times t=0 is initialized, and the maximum number of update times is set to T.
Step 3: mapping a task allocation scheme into one particle, randomly generating K initial particles as { X } 1 (t),X 2 (t),...,X k (t),...,X K (t) }; wherein X is k (t) is the kth particle in the t-th update, K ε {1,2,., K }, X k (t)＝(x k1 (t),x k2 (t),...,x kj (t),...,x km (t))，x kj (t)∈{1,2,...,i,...,n}，x kj (t) is expressed in the particle X k The x-th of (t) kj And (t) the unmanned motorized platform performs the j-th task. As shown in the figure2, assigning 5 task targets to 4 unmanned motorized platforms, generating a random particle representing: the 1 st unmanned motorized platform performs the 3 rd task, the 2 nd unmanned motorized platform performs the 1 st task, the 3 rd unmanned motorized platform performs the 2 nd and 5 th tasks, and the 4 th unmanned motorized platform performs the 4 th task.
Step 4: constructing fitness function as
f(X k (t))＝e 1 ·f 1 (X k (t))+e 2 ·f 2 (X k (t))
s.t C 1 ,C 2 ,C 3
Wherein f (X) k (t)) is particle X k The fitness function value of (t); e, e 1 、e 2 Are correction coefficients for adjusting the order of magnitude difference between different indexes and reflecting the importance degree of each sub-target;
f 1 (X k (t)) is particle X k Task revenue cost of (t)
f 1 (X k (t))＝1-P v (X k (t))
Wherein P is v (X k (t)) is particle X k Task revenue of (t)
Wherein P is i For the task execution efficiency of the ith unmanned aerial vehicle platform, V j The value of the j-th task; a, a k-ij As a decision variable, when particle X k A when the ith unmanned mobile platform in (t) performs the jth task k-ij When particle X is =1 k A when the ith unmanned motorized platform of (t) is not performing the jth task k-ij ＝0；V max For maximum value of all tasks, V max ＝max{V 1 ,V 2 ,...,V j ,...,V m }；
f 2 (X k (t)) is particle X k Journey cost of (t)
Wherein D is i For the maximum travel distance of the ith unmanned aerial vehicle platform, d ij For the distance between the ith unmanned motorized platform and the jth mission target,
C 1 、C 2 、C 3 constraint conditions for the collaborative execution of tasks for multiple unmanned motorized platforms:
constraint C 1 : any one task can only be performed once:
constraint C 2 : unmanned motorized platform path length constraints:
constraint C 3 : the maximum execution task number constraint of the unmanned mobile platform:
wherein r is i The number of tasks that are most completed for the ith unmanned motorized platform.
Step 5: updating all particles to obtain K updated particles { X } 1 (t+1),X 2 (t+1),...,X k (t+1),...,X K (t+1)}。
In this embodiment, in the step 5, all particles are updated, get K e {1, 2..k } updated particles as
Wherein omega p Is an individual variant factor, l 1 For individual learning factors, l 2 Is a group learning factor; p is p k (t) is the individual optimum, i.e. the best position experienced by the individual particle, p k (t)＝argmin{f(X k (0)),f(X k (1)),…,f(X k (t))}；p g (t) is a global optimum representing the best location that all particles in the population have undergone.
F 1 (X k (t)) is particle X k The individual variation function value of (t),
wherein r, s are from {1, 2., -, two numbers selected randomly in K, r.noteq.s.noteq.k. Individual particles by reacting other particles X in the population r (t) and X s And (3) learning the position of (t) to realize variation of individual particles and enhance the diversity of the particles.
Let psi be k (t) is a temporary variable that is,
wherein k is 1 Is interval [0,1 ]]A random number on the same. Psi phi type k (t) is a probability of ω p The specific implementation process of the individual mutation operation of (a) is as follows: if k is 1 ＜ω p Then the particles are subjected to a mutation operation F 1 (X k (t)); if k is 1 ≥ω p Then psi is k (t)＝X k (t), i.e. the particles remain in the current position state.
F 2 (ψ k (t),p k (t)) is a learning operation of the particle on the individual extremum: as shown in fig. 3, two different numbers a and b are randomly chosen among {1,2,..m } and will be ψ k The number between positions a and b of (t) is p k A bit of (t)The number between the position and the position b is replaced to obtain a new individual, namely the psi k The unmanned motorized platform in (t) performing the a-th to b-th tasks all use the individual extremum p k And (c) replacing the unmanned mobile platform for executing the a-th task to the b-th task.
Phi is set k (t) is a temporary variable that is,
wherein k is 2 Is interval [0,1 ]]A random number on the same. Phi (phi) k (t) represents the learning of the particle's own experience, which is a probability of l 1 The specific implementation process of the learning operation of the device is as follows: if k is 2 ＜l 1 The particle proceeds to the individual extremum p k The cross operation of (t) causing the particles to learn the best locations traversed by the individual; if k is 2 ≥l 1 Phi is k (t)＝ψ k (t), i.e. the particles remain in the current position state.
F 3 (φ k (t),p g (t)) is a learning operation of particles on the whole extremum: as shown in fig. 4, two different numbers a and b are randomly chosen among {1,2,..m } and will be Φ k The number between positions a and b of (t) is p g The number between positions a and b of (t) is replaced, i.e. φ k The unmanned mobile platform for executing the a-th task to the b-th task in (t) uses all the extreme values p g And (c) replacing the unmanned mobile platform for executing the a-th task to the b-th task.
Wherein k is 3 Is interval [0,1 ]]A random number on the same. This part represents the learning of the particle to the optimal position in the population, which is a probability of l 2 Is a cross operation of (a). The particles are mutually interacted through information sharing and according to the total extreme value p g (t) adjusting the position of the device. The implementation method is as follows:if k is 3 ＜l 2 X is then k (t+1)＝F 3 (φ k (t),p g (t)) for the particles and the total extremum p g The cross operation of (t) causing the particles to learn the best positions through which the whole particles pass; if k is 3 ≥l 2 X is then k (t+1)＝φ k (t) the particle remains in the current position state.
Step 6: calculating fitness value { f (X) 1 (t+1)),f(X 2 (t+1)),...,f(X k (t+1)),...,f(X K (t+1))}。
Step 7: further updating all particles to obtain the K-th epsilon {1,2,., K } particles after further updating as
Step 8: updating individual optimum values to
Step 9: updating global optimum to
Step 10: let t=t+1, go to step 5 if T < T, and execute step 11 if T is greater than or equal to T.
Step 11: and outputting the global optimal value as the optimal task allocation scheme.
It should be apparent that the above-described embodiments are merely some, but not all, embodiments of the present invention. The above examples are only for explaining the present invention and do not limit the scope of the present invention. Based on the above embodiments, all other embodiments, i.e. all modifications, equivalents and improvements made within the spirit and principles of the present application, which are obtained by persons skilled in the art without making creative efforts are within the scope of the present invention claimed.
Claims (1)
1. A multi-unmanned mobile platform task allocation method based on a discrete particle swarm optimization algorithm is characterized by comprising the following steps:
step 1: acquiring the total number n of unmanned mobile platforms and the position of the ith unmanned mobile platformTotal number of task objects m, position of j-th task object +.>Where i=1, 2, …, n, j=1, 2, …, m;
step 2: initializing the update times t=0, and setting the maximum update times as T;
step 3: mapping a task allocation scheme into one particle, randomly generating K initial particles as { X } 1 (t),X 2 (t),…,X k (t),…,X K (t) }; wherein X is k (t) is the kth particle in the tth update, K ε {1,2, …, K }, X k (t)＝(x k1 (t),x k2 (t),…,x kj (t),…,x km (t))，x kj (t)∈{1,2,…,i,…,n}，x kj (t) is expressed in the particle X k The x-th of (t) kj (t) the unmanned motorized platform performing the j-th task;
step 4: constructing fitness function as
f(X k (t))＝e 1 ·f 1 (X k (t))+e 2 ·f 2 (X k (t))
s.t C 1 ,C 2 ,C 3
Wherein f (X) k (t)) is particle X k The fitness function value of (t); e, e 1 、e 2 Are correction coefficients;
f 1 (X k (t)) is particle X k Task revenue cost of (t)
f 1 (X k (t))＝1-P v (X k (t))
Wherein P is v (X k (t)) is particle X k Task revenue of (t)
Wherein P is i For the task execution efficiency of the ith unmanned aerial vehicle platform, V j The value of the j-th task; a, a k-ij As a decision variable, when particle X k A when the ith unmanned mobile platform in (t) performs the jth task k-ij When particle X is =1 k A when the ith unmanned motorized platform of (t) is not performing the jth task k-ij ＝0；V max For maximum value of all tasks, V max ＝max{V 1 ,V 2 ,…,V j ,…,V m }；
f 2 (X k (t)) is particle X k Journey cost of (t)
Wherein D is i For the maximum travel distance of the ith unmanned aerial vehicle platform, d ij For the distance between the ith unmanned motorized platform and the jth mission target,
C 1 、C 2 、C 3 constraint conditions for the collaborative execution of tasks for multiple unmanned motorized platforms:
constraint C 1 : any one task can only be performed once:
constraint C 2 : unmanned motorized platform path length constraints:
constraint C 3 : the maximum execution task number constraint of the unmanned mobile platform:
wherein r is i The number of tasks that are most completed for the ith unmanned motorized platform;
step 5: updating all particles to obtain K updated particles { X } 1 (t+1),X 2 (t+1),…,X k (t+1),…,X K (t+1)}；
In the step 5, all the particles are updated to obtain the K-th E {1,2, …, K } updated particles as
Wherein omega p Is an individual variant factor, l 1 For individual learning factors, l 2 Is a group learning factor;
F 1 (X k (t)) is particle X k The individual variation function value of (t),
wherein r and s are two numbers selected from {1,2, …, K } at random, and r is not equal to s not equal to K;
let psi be k (t) is a temporary variable that is,
wherein k is 1 Is interval [0,1 ]]A random number thereon;
F 2 (ψ k (t),p k (t)) is a learning operation of the particle on the individual extremum: randomly selecting two different numbers a and b from {1,2, …, m }, and then phi k The number between positions a and b of (t) is p k Replacing the number between the a position and the b position of (t);
phi is set k (t) is a temporary variable that is,
wherein k is 2 Is interval [0,1 ]]A random number thereon;
F 3 (φ k (t),p g (t)) is a learning operation of particles on the whole extremum: randomly selecting two different numbers a and b from {1,2, …, m }, and adding phi k The number between positions a and b of (t) is p g Replacing the number between the a position and the b position of (t);
wherein k is 3 Is interval [0,1 ]]A random number thereon;
step 6: calculating fitness value { f (X) 1 (t+1)),f(X 2 (t+1)),…,f(X k (t+1)),…,f(X K (t+1))}；
Step 7: further updating all particles to obtain the K-th E {1,2, …, K } particles as
Step 8: updating individual optimum values to
Wherein p is k (t) is the individual optimum, i.e. the best position experienced by the individual particle, p k (t)＝argmin{f(X k (0)),f(X k (1)),…,f(X k (t))}；
Step 9: updating global optimum to
Step 10: let t=t+1, if T < T, go to step 5, if T is greater than or equal to T, execute step 11;
step 11: and outputting the global optimal value as the optimal task allocation scheme.
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