Task scheduling method based on improved chimp optimization algorithm

Abstract

A task scheduling method based on an improved chimp optimization algorithm, relating to the technical field of task scheduling, and comprising: acquiring a task to be scheduled and a task scheduling model established in advance by using a chimp optimization algorithm, and using a two-dimensional function sequence Halton to initialize a population and parameters; performing iterative computation on chimps in the task scheduling model by means of the chimp optimization algorithm; and when an iteration termination condition is reached, ending iterative computation, outputting an optimal solution, and obtaining an optimal scheduling scheme. According to the solution, the problems in the prior art of a traditional chimp optimization algorithm being prone to falling into local optimization, and the global exploration capacity and the local mining capacity being not balanced are solved, and the improved chimp optimization algorithm has performance improvement in different aspects compared with general population intelligent algorithms, and also achieves remarkable effectiveness in task scheduling applications in an actual cloud computing environment.

Description

A task scheduling method based on improved Chimpanzee optimization algorithm Technical Field

[0001] The present invention relates to the technical field of task scheduling, and in particular to a task scheduling method based on an improved Chimpanzee optimization algorithm. Background Art

[0002] Cloud computing is a technology that provides virtual resources based on user needs and allows for customizable management. It can process massive amounts of tasks in parallel, facilitating large-scale data computation. With the rapid development of information technology and the internet, cloud computing has become widely used in various industries and fields, including economics, culture, military, and commerce.

[0003] Task scheduling strategies in cloud environments are closely related to the performance and efficiency of cloud computing systems. How to schedule cloud tasks while meeting demand and ensuring load balancing is a hot topic in cloud computing. At the same time, efficient cloud computing task scheduling algorithms can accelerate task scheduling while ensuring load balancing in cloud computing systems and reduce unnecessary resource consumption. Therefore, research on cloud computing task scheduling algorithms is beneficial for both reducing costs and improving cloud computing performance.

[0004] Optimizing task scheduling in cloud environments is an NP-complete problem. To address this problem, researchers both domestically and internationally have conducted extensive research on heuristic algorithms based on swarm intelligence, such as particle swarm optimization, cuckoo search, and gray wolf optimization. While these algorithms have improved the performance of cloud computing platforms to some extent, they have not fully addressed the issues of premature convergence and the tendency to get stuck in local optima. In recent years, with the continuous advancement of intelligent algorithms, more algorithms have been applied to various problems based on their respective strengths. Among them, the Chimp Optimization Algorithm (ChOA), a novel intelligent optimization algorithm based on the social behavior of chimpanzee populations, has attracted increasing attention from researchers both domestically and internationally due to its advantages over existing algorithms, such as fewer control parameters, ease of implementation, and high stability. However, in traditional chimpanzee optimization algorithms, individuals in the population obtain food satiate in the final stage of hunting. Subsequent individual motivations drive the chimpanzees to unleash their natural instincts, making it prone to getting stuck in local optima in the later stages of the algorithm's optimization process, leading to premature maturity or even iterative stagnation. At the same time, the traditional Chimpanzee optimization algorithm also has the disadvantage of an imbalance between global exploration capability and local exploitation capability.

[0005] Summary of the Invention

[0006] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a task scheduling method based on the improved Chimpanzee optimization algorithm to solve the problems in the existing technology that the traditional Chimpanzee optimization algorithm is prone to falling into local optimality and the imbalance between global exploration ability and local exploitation ability. On the basis of the random initialization of the Chimpanzee algorithm, a two-dimensional function sequence Halton is introduced to generate pseudo-random numbers to initialize the population, so that the individuals of the population are more evenly distributed in the entire solution space, the population diversity during algorithm initialization is improved, and the individuals can quickly find the location of high-quality solutions, thereby accelerating the convergence of the algorithm.

[0007] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0008] In a first aspect, the present invention provides a task scheduling method based on an improved Chimpanzee optimization algorithm, comprising:

[0009] Obtain the tasks to be scheduled and the task scheduling model pre-established using the Chimpanzee optimization algorithm, and use the two-dimensional function sequence Halton to initialize the population and parameters;

[0010] Performing iterative calculations on the chimpanzees in the task scheduling model using a chimpanzee optimization algorithm;

[0011] When the iteration termination condition is reached, the iterative calculation ends, the optimal solution is output, and the optimal scheduling plan is obtained.

[0012] In combination with the first aspect, further, performing iterative calculation on the chimpanzee in the task scheduling model by using the chimpanzee optimization algorithm includes:

[0013] Obtaining an initial population position and an initial fitness of each chimpanzee individual in the population, sorting and dividing the population roles according to the fitness values, and obtaining an initial population role position;

[0014] Update the population position and population role position according to the sine-cosine optimization strategy to obtain the updated fitness of each chimpanzee individual in the population;

[0015] The task scheduling model established using the chimpanzee optimization algorithm includes: treating multiple virtual machines in cloud computing as multiple chimpanzees in a group, and assigning tasks to virtual machines as the process of chimpanzees driving and chasing prey. The process of chimpanzees driving and chasing prey is the process of finding the optimal solution.

[0016] In combination with the first aspect, further, the two-dimensional function sequence Halton includes: θ(n)=b0·p -1 +b1·p -2 +…+b m ·p -m-1; H(n)=[θ1(n),θ2(n)];

[0017] Where n is any integer in [1, N], N is the number of individuals in the population, p is a prime number not less than 2, and b i For b i ∈{0,1,2….p-1}, θ(n) is the defined sequence function, H(n) is the final two-dimensional Halton sequence, m is the number of nodes, n is the total number of tasks, 1≤i≤N, i is the number of cycles;

[0018] Among them, the individual position of the Nth chimpanzee is X ij ,i∈{1,2,…,m},j∈{1,2,…,n}.

[0019] In combination with the first aspect, further, sorting and dividing the population roles according to the fitness values ​​to obtain initial population role positions includes:

[0020] According to the fitness value, the population roles are sorted from high to low and divided, and the population roles include Attacker, Barrier, Chaser and Driver; Attacker is the current optimal solution, and the other three are ranked in descending order;

[0021] The mathematical model of the chimpanzee driving and chasing prey is obtained according to the population role, and the mathematical model is: d(t) = |c·X prey (t)-h·X chimp (t)|; X chimp (t+1)=X prey (t)-a·d;

[0022] Where t is the current iteration number, d is the distance between each chimpanzee and the prey, a and c are coefficient vectors, h is the chaotic mapping vector, h represents the influence of the chimpanzee population motivation during the hunting process, X prey is the prey position vector, X chimp is the current position vector of the chimpanzee, d(t) is the distance between each chimpanzee and the prey at the current iteration number, X chimp (t+1) is the position vector of the chimpanzee at the next iteration.

[0023] In combination with the first aspect, further, obtaining the initial population role position includes:

[0024] Based on the population roles and the mathematical model of the chimpanzee driving and chasing prey process, the four candidate solutions with the highest fitness are selected, and the position vectors of other chimpanzees and the initial population role position vector are obtained through the position vector calculation formula. The position vector calculation formula includes:Attacker =|c1·X Attacker -h1·X|; d Barrier =|c2·X Barrier -h2·X|; d Chaser =|c3·X Chaser -h3·X|; d Driver =|c4·X Driver -h4·X|; X1=X Attacker -a1·d Attacker ; X2=X Barrier -a2·d Barrier ; X3=X Chaser -a3·d Chaser ; X4=X Driver -a4·d Driver ;

[0025] Where, d Attacker is the distance between the attacker chimpanzee and the prey, d Barrier is the distance between the obstacle chimpanzee and the prey, d Chaser is the distance between the chaser chimpanzee and the prey, d Driver is the distance between the driving chimpanzee and the prey, X Attacker is the position vector of the attacker chimpanzee, X Barrier is the position vector of the obstructing chimpanzee, X Chaser is the position vector of the chasing chimpanzee, X Driver is the position vector of the driving chimpanzee, c1c2c3c4 and a1a2a3a4 are coefficient vectors, h1h2h3h4 represents the influence of the chimpanzee population's motivation during hunting, X1, X2, X3 and X4 are the position vectors in the calculation process, X(t+1) is the position vector at the next moment, and X is the position vector at the current moment.

[0026] In combination with the first aspect, further, updating the population position and the population role position according to the sine-cosine optimization strategy includes:

[0027] The position update strategy of the original chimpanzee optimization algorithm is modified according to the sine-cosine optimization strategy. The position where the optimal solution exists is calculated while balancing the algorithm. The modified position update strategy is:

[0028] Where r3 is a number that decreases adaptively with the number of iterations, r4, r5, and r6 are random numbers in [0,1], X(t+1), X(t) are the updated position vectors of the chimpanzee individuals, and D θ Formula for the sine-cosine optimization strategy.

[0029] In combination with the first aspect, further, when the iteration termination condition is reached, the iterative calculation is terminated, the optimal solution is output, and the optimal scheduling solution is obtained, further comprising:

[0030] When t<T, the iteration termination condition is reached, the iterative calculation ends, the optimal solution is output, and the optimal scheduling plan is obtained;

[0031] When t≥T, the iteration termination condition is not met, and the two-dimensional function sequence Halton is used to initialize the population and parameters again, and the subsequent steps are continued until the iteration termination condition is met to end the iterative calculation.

[0032] In a second aspect, the present invention provides a task scheduling device based on an improved Chimpanzee optimization algorithm, comprising:

[0033] The initialization module is used to obtain the tasks to be scheduled and the task scheduling model pre-established using the Chimpanzee optimization algorithm, and initialize the population and parameters using the two-dimensional function sequence Halton;

[0034] A calculation module, configured to perform iterative calculations on the chimpanzees in the task scheduling model using a chimpanzee optimization algorithm;

[0035] The output module is used to end the iterative calculation when the iteration termination condition is reached, output the optimal solution, and obtain the optimal scheduling plan.

[0036] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the task scheduling method based on the improved chimpanzee optimization algorithm as described in any one of the first aspects is implemented.

[0037] In a fourth aspect, the present invention provides a device comprising:

[0038] a memory for storing instructions;

[0039] A processor is used to execute the instructions so that the device performs operations to implement the task scheduling method based on the improved Chimpanzee optimization algorithm as described in any one of the first aspects.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The present invention discloses a task scheduling method based on an improved chimpanzee optimization algorithm, comprising obtaining tasks to be scheduled and a task scheduling model pre-established using the chimpanzee optimization algorithm, initializing a population and parameters using a two-dimensional function sequence, Halton, and performing iterative calculations on the chimpanzees in the task scheduling model using the chimpanzee optimization algorithm. When an iterative termination condition is reached, the iterative calculation is terminated, the optimal solution is output, and the optimal scheduling scheme is obtained. Using a two-dimensional Halton sequence to initialize the chimpanzee population allows individuals in the population to be more evenly distributed throughout the solution space, improving population diversity during algorithm initialization. Individuals can quickly discover the location of high-quality solutions, thereby accelerating algorithm convergence and improving algorithm accuracy. The improved chimpanzee optimization algorithm has various performance improvements compared to general swarm intelligence algorithms and is significantly effective in task scheduling applications in actual cloud computing environments. It achieves a dynamic balance between the algorithm's global exploration and local mining phases, effectively alleviating the tendency of traditional chimpanzee optimization algorithms to fall into local optimal solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG1 is a flow chart of a task scheduling method based on an improved Chimpanzee optimization algorithm provided in Example 1 of the present invention;

[0043] FIG2 is a second flow chart of a task scheduling method based on an improved Chimpanzee optimization algorithm provided in Example 1 of the present invention;

[0044] FIG3 is a flow chart of a method for real-time updating of optimized algorithm parameters in a task scheduling method based on an improved Chimpanzee optimization algorithm provided by the first embodiment of the present invention. DETAILED DESCRIPTION

[0045] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0046] The term "and / or" in this document simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " in this document generally indicates an "or" relationship between the related objects.

[0047] Example 1:

[0048] As shown in FIG1 , this embodiment provides a task scheduling method based on an improved Chimpanzee optimization algorithm, comprising the following steps:

[0049] Step 1: Based on the tasks to be scheduled, a task scheduling model is established using the chimpanzee optimization algorithm. The task scheduling model established using the chimpanzee optimization algorithm includes: treating multiple virtual machines in cloud computing as multiple chimpanzees in a group, and assigning tasks to virtual machines as the process of chimpanzees driving and chasing prey. The process of chimpanzees driving and chasing prey is the process of finding the optimal solution.

[0050] Step 2: Use the two-dimensional function sequence Halton to generate pseudo-random numbers to initialize the population and parameters, including the number of individuals N, the maximum number of iterations T, the dimension d, and the coefficient vectors a, h, and c;

[0051] The two-dimensional function sequence Halton includes: θ(n)=b0·p -1 +b1·p -2 +…+b m ·p -m-1 ; H(n)=[θ1(n),θ2(n)];

[0052] Where n is any integer in [1, N], N is the number of individuals in the population, p is a prime number not less than 2, and b i For b i ∈{0,1,2….p-1}, θ(n) is the defined sequence function, H(n) is the final two-dimensional Halton sequence, m is the number of nodes, n is the total number of tasks, 1≤i≤N, i is the number of cycles.

[0053] Among them, the position of the Nth chimpanzee individual is X ij (i∈{1,2,…,m},j∈{1,2,…,n}), m is the number of nodes, n is the total number of tasks, 1≤i≤N.

[0054] The population coefficient vectors a, c, and h are calculated by the equations a = 2·f·r1-f; c = 2·r2; and h = chaotic_value, where f is the adaptive convergence factor that decreases nonlinearly from 2.5 to 0 during the exploration and exploitation phases; and h is the chaotic mapping vector, representing the influence of the chimpanzee population's sexual motivation during the hunting process.

[0055] The original chimpanzee algorithm uses six deterministic chaotic process mappings with random behaviors. To simulate this social behavior, assuming that both the normal position update mechanism and the chaotic model process mapping have a 50% probability, its mathematical model is as follows:

[0056] Where, X chimp (t+1) is the position vector of the chimpanzee at the next iteration, X prey(t) is the prey position vector at the current iteration number, and μ is a random number in the range of [0,1].

[0057] Step 3: Calculate the fitness of each chimpanzee in the population, sort them from high to low according to the fitness value and divide the population into roles;

[0058] According to the fitness value, the population roles are sorted from high to low and divided, and the population roles include Attacker, Barrier, Chaser and Driver; Attacker is the current optimal solution, and the other three are ranked in descending order.

[0059] The mathematical model of the chimpanzee driving and chasing prey is obtained according to the population role, and the mathematical model is: d(t) = |c·X prey (t)-h·X chimp (t)|; X chimp (t+1)=X prey (t)-a·d;

[0060] Where t is the current iteration number, d is the distance between each chimpanzee and the prey, a and c are coefficient vectors, h is the chaotic mapping vector, h represents the influence of the chimpanzee population motivation during the hunting process, X prey is the prey position vector, X chimp is the current position vector of the chimpanzee, d(t) is the distance between each chimpanzee and the prey at the current iteration number, X chimp (t+1) is the position vector of the chimpanzee at the next iteration.

[0061] The obtaining of the initial population role positions includes:

[0062] Based on the population roles and the mathematical model of the chimpanzee driving and chasing prey process, the four candidate solutions with the highest fitness are selected, and the position vectors of other chimpanzees and the initial population role position vector are obtained through the position vector calculation formula.

[0063] Step 4: After selecting the four candidate solutions with the highest fitness, update the position vectors of the other chimpanzees and the position vectors of the four characters mentioned above according to the following formula;

[0064] The position vectors of the four roles are the initial population role position vectors, and the position vector calculation formula includes: Attacker =|c1·X Attacker -h1·X|; d Barrier =|c2·X Barrier -h2·X|; d Chaser =|c3·X Chaser -h3·X|; dDriver =|c4·X Driver -h4·X|; X1=X Attacker -a1·d Attacker ; X2=X Barrier -a2·d Barrier ; X3=X Chaser -a3·d Chaser ; X4=X Driver -a4·d Driver ;

[0065] Where, d Attacker is the distance between the attacker chimpanzee and the prey, d Barrier is the distance between the obstacle chimpanzee and the prey, d Chaser is the distance between the chaser chimpanzee and the prey, d Driver is the distance between the driving chimpanzee and the prey, X Attacker is the position vector of the attacker chimpanzee, X Barrier is the position vector of the obstructing chimpanzee, X Chaser is the position vector of the chasing chimpanzee, X Driver is the position vector of the driving chimpanzee, c1c2c3c4 and a1a2a3a4 are coefficient vectors, h1h2h3h4 represents the influence of the chimpanzee population's motivation during the hunting process, X1, X2, X3 and X4 are the position vectors in the calculation process, X(t+1) is the position vector at the next moment, and X is the current position vector.

[0066] Step 5: Introduce an optimization strategy based on the Sine-Cosine Algorithm (SCA) to modify the position update strategy of the original Chimpanzee optimization algorithm, balance the algorithm and calculate the possible location of the optimal solution;

[0067] The revised location update strategy is:

[0068] Where r3 is a number that decreases adaptively with the number of iterations, r4, r5, and r6 are random numbers in [0,1], X(t+1), X(t) are the updated position vectors of the chimpanzee individuals, and D θ Formula for the sine-cosine optimization strategy.

[0069] Step 6: Update the population position and the positions of the four leaders: Attacker, Barrier, Chaser, and Driver, and recalculate the fitness;

[0070] It should be noted that the fitness function should be designed according to the specific application of the algorithm. In different application scenarios, the calculation method of the fitness function is different.

[0071] Step 7: Determine whether the iteration termination condition is met. If the termination condition is met, the calculation ends and step 8 is executed. Otherwise, steps 2 to 6 are executed repeatedly.

[0072] When t<T, the iteration termination condition is reached, and step eight is executed to end the iterative calculation, output the optimal solution, and obtain the optimal scheduling plan;

[0073] When t≥T, the iteration termination condition is not met, and steps 2 to 6 are executed cyclically, the population and parameters are initialized again using the two-dimensional function sequence Halton, and the subsequent steps are continued until the iteration termination condition is met and the iterative calculation is ended.

[0074] Step 8: Output the optimal solution and obtain the optimal scheduling plan.

[0075] Example 2:

[0076] Based on the same inventive concept as the first embodiment, this embodiment introduces a task scheduling device based on an improved Chimpanzee optimization algorithm, including:

[0077] The initialization module is used to obtain the tasks to be scheduled and the task scheduling model pre-established using the Chimpanzee optimization algorithm, and initialize the population and parameters using the two-dimensional function sequence Halton;

[0078] A calculation module, configured to perform iterative calculations on the chimpanzees in the task scheduling model using a chimpanzee optimization algorithm;

[0079] The output module is used to end the iterative calculation when the iteration termination condition is reached, output the optimal solution, and obtain the optimal scheduling plan.

[0080] The specific functional implementation of each of the above modules can be found in the relevant content of the method in Example 1 and will not be elaborated on here.

[0081] Example 3:

[0082] Based on the same inventive concept as other embodiments, this embodiment introduces a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the task scheduling method based on the improved chimpanzee optimization algorithm as described in any one of the first embodiments.

[0083] Example 4:

[0084] Based on the same inventive concept as other embodiments, this embodiment introduces a device, including:

[0085] a memory for storing instructions;

[0086] The processor is configured to execute the instruction so that the device performs operations to implement the task scheduling method based on the improved Chimpanzee optimization algorithm as described in any one of the first embodiments.

[0087] In summary, the present invention discloses a task scheduling method based on an improved chimpanzee optimization algorithm. On the basis of random initialization of the chimpanzee algorithm, a two-dimensional function sequence Halton is introduced to generate pseudo-random numbers to initialize the population, so that the individuals of the population are more evenly distributed in the entire solution space, the population diversity during algorithm initialization is improved, and the individuals can quickly find the location of high-quality solutions, thereby accelerating the convergence of the algorithm and improving the accuracy of the algorithm. At the same time, during the execution of the algorithm, a sine-cosine optimization strategy is introduced to update the position, so that the global exploration and local mining stages of the algorithm reach a dynamic balance, thereby effectively alleviating the traditional chimpanzee optimization algorithm from falling into the local optimal solution, and improving the rationality of task scheduling and resource utilization. Compared with general swarm intelligence algorithms such as particle swarm optimization algorithm (PSO), gray wolf optimization algorithm (GWO) and traditional chimpanzee optimization algorithm (ChOA), the improved chimpanzee optimization algorithm has different performance improvements, and is also significantly effective in task scheduling applications in actual cloud computing environments.

[0088] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A task scheduling method based on an improved chimpanzee optimization algorithm, characterized in that: include: Obtain the tasks to be scheduled and the task scheduling model established in advance using the chimpanzee optimization algorithm, and use the two-dimensional function sequence Halton to initialize the population and parameters; Iteratively calculating the chimpanzees in the task scheduling model by using a chimpanzee optimization algorithm; When the iteration termination condition is reached, the iterative calculation ends, the optimal solution is output, and the optimal scheduling plan is obtained.

2. The task scheduling method based on the improved chimpanzee optimization algorithm according to claim 1 is characterized in that: The iterative calculation of the chimpanzees in the task scheduling model by the chimpanzee optimization algorithm includes: Obtaining the initial population position and the initial fitness of each chimpanzee individual in the population, sorting and dividing the population roles according to the fitness values, and obtaining the initial population role position; Update the population position and population role position according to the sine-cosine optimization strategy to obtain the updated fitness of each chimpanzee individual in the population; The task scheduling model established using the chimpanzee optimization algorithm includes: treating multiple virtual machines in cloud computing as multiple chimpanzees in a group, and assigning tasks to virtual machines as the process of chimpanzees driving and chasing prey. The process of chimpanzees driving and chasing prey is the process of finding the optimal solution.

3. The task scheduling method based on the improved chimpanzee optimization algorithm according to claim 2 is characterized in that: The two-dimensional function sequence Halton includes: θ(n)=b0·p -1 +b1·p -2 +…+b m ·p -m-1 ; H(n)=[θ1(n),θ2(n)]; In the formula, n is any integer in [1, N], N is the number of individuals in the population, and p is a prime number not less than 2. b i ∈{0,1,2….p-1}, θ(n) is the defined sequence function, H(n) is the final two-dimensional Halton sequence, m is the number of nodes, n is the total number of tasks, 1≤i≤N, i is the number of cycles; Among them, the individual position of the Nth chimpanzee is X ij ,i∈{1,2,…,m},j∈{1,2,…,n}.

4. The task scheduling method based on the improved chimpanzee optimization algorithm according to claim 3 is characterized in that: The step of sorting and dividing the population roles according to the fitness values ​​to obtain the initial population role positions includes: According to the fitness value, the population roles are sorted from high to low and divided, and the population roles include attacker, barrier, chaser and driver; the attacker is the current optimal solution, and the other three are in descending order; A mathematical model of the process of chimpanzees driving and chasing prey is obtained according to the population roles, and the mathematical model is: d(t)=|c·X prey (t)-h·X chimp (t)|; X chimp (t+1)=X prey (t)-a·d; Where t is the current iteration number, d is the distance between each chimpanzee and the prey, a and c are coefficient vectors, h is the chaotic mapping vector, h represents the influence of the chimpanzee population motivation during the hunting process, and X prey is the prey position vector, X chimp is the current position vector of the chimpanzee, d(t) is the distance between each chimpanzee and the prey at the current iteration number, X chimp (t+1) is the position vector of the chimpanzee at the next iteration.

5. The task scheduling method based on the improved chimpanzee optimization algorithm according to claim 4 is characterized in that: The obtaining of the initial population role position comprises: According to the population roles and the mathematical model of the chimpanzee driving and chasing prey, the four candidate solutions with the highest fitness are selected, and the position vectors of other chimpanzees and the initial population role position vector are obtained through the position vector calculation formula. The position vector calculation formula includes: d Attacker =|c1·X Attacker -h1·X|; d Barrier =|c2·X Barrier -h2·X|; d Chaser =|c3 X Chaser -h3 X|; d Driver =|c4·X Driver -h4·X|; X1=X Attacker -a1·d Attacker ; X2=X Barrier -a2·d Barrier ; X3=X Chaser -a3·d Chaser ; X4=X Driver -a4·d Driver ; Where, d Attacker is the distance between the attacker chimpanzee and the prey, d Barrier is the distance between the obstacle chimpanzee and the prey, d Chaser is the distance between the chaser chimpanzee and the prey, d Driver is the distance between the chimpanzee and the prey, X Attacker is the position vector of the attacker chimpanzee, X Barrier is the position vector of the obstructionist chimpanzee, X Chaser is the position vector of the chaser chimpanzee, X Driver is the position vector of the chimpanzee that drives it away, c1c2c3c4 and a1a2a3a4 are coefficient vectors, h1h2h3h4 represents the influence of the chimpanzee population’s sexual motivation during hunting, X1, X2, X3 and X4 are the position vectors in the calculation process, X(t+1) is the position vector at the next moment, and X is the position vector at the current moment.

6. The task scheduling method based on the improved chimpanzee optimization algorithm according to claim 5 is characterized in that: The updating of the population position and the population role position according to the sine-cosine optimization strategy includes: The position update strategy of the original chimpanzee optimization algorithm is modified according to the sine-cosine optimization strategy. The position where the optimal solution exists is calculated while balancing the algorithm. The modified position update strategy is: Where r3 is a number that decreases adaptively with the number of iterations, r4, r5, and r6 are random numbers in [0,1], X(t) is the updated position vector of the chimpanzee individual, and D θ Formula for the sine-cosine optimization strategy.

7. The task scheduling method based on the improved chimpanzee optimization algorithm according to claim 1 is characterized in that: The iterative calculation is terminated when the iteration termination condition is reached, the optimal solution is output, and the optimal scheduling solution is obtained, and the following further includes: When t<T, the iteration termination condition is reached, the iterative calculation ends, the optimal solution is output, and the optimal scheduling plan is obtained; When t ≥ T, the iteration termination condition is not met, and the two-dimensional function sequence Halton is used again to initialize the population and parameters, and the subsequent steps are continued until the iteration termination condition is met to end the iterative calculation.

8. A task scheduling device based on an improved chimpanzee optimization algorithm, characterized in that: include: An initialization module is used to obtain the tasks to be scheduled and the task scheduling model established in advance using the chimpanzee optimization algorithm, and initialize the population and parameters using the two-dimensional function sequence Halton; A calculation module, used for iteratively calculating the chimpanzees in the task scheduling model by using a chimpanzee optimization algorithm; The output module is used to end the iterative calculation when the iteration termination condition is reached, output the optimal solution, and obtain the optimal scheduling plan.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the task scheduling method based on the improved chimpanzee optimization algorithm as described in any one of claims 1 to 7 is implemented.

10. A device, characterized in that: include: A memory for storing instructions; A processor is used to execute the instruction so that the device performs the operation of implementing the task scheduling method based on the improved chimpanzee optimization algorithm as described in any one of claims 1 to 7.

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