Task allocation method based on whale optimization algorithm framework

Abstract

The invention belongs to the technical field of mobile crowd sensing, and particularly relates to a task allocation method based on a whale optimization algorithm framework, and the method comprises the steps: constructing an immune genetic whale algorithm framework based on the whale optimization algorithm framework; designing a whale individual structure representing a task allocation strategy; defining a whale individual fitness evaluation function; initializing a first generation whale individual population; designing a crossover operator, a mutation operator and an immune operator based on an immune genetic method; designing a repairing operator for repairing the invalid whale individual; specific operations such as population initialization, crossover, variation, immunization and repair are integrated into an immune genetic whale algorithm framework, so that the design of a task allocation algorithm is completed. According to the method, the local and global optimization capabilities of the algorithm are considered, so that the platform can generate a task allocation strategy capable of maximizing profit at a relatively high speed, and the task allocation rate and the activeness of participants are improved.

Description

Technical Field

[0001] The present invention relates to the field of mobile crowd intelligence perception technology, and in particular to a task allocation method based on a whale optimization algorithm framework. Background Art

[0002] Since the mobile crowd-sensing multi-task allocation problem is an NP-complete problem, as the number of tasks and participants increases, the scale of the problem expands and it cannot be solved in polynomial time. However, some classic task allocation technical solutions have defects in solving such problems. For example, the random allocation algorithm (RAA) has uncertainty and inefficiency when generating task allocation solutions, and the greedy allocation strategy (GAA) easily causes the problem to fall into a local optimal solution when generating task allocation solutions, and cannot handle complex constraints. Therefore, some existing technical solutions use meta-heuristic optimization algorithms to generate task allocation strategies, such as genetic algorithms (GA), whale optimization algorithms (WOA), and genetic simulated annealing algorithms (GA-SA). These algorithms have extensive exploratory capabilities and can effectively find high-quality allocation solutions in the solution set space of task allocation problems. However, in complex task allocation scenarios, the allocation strategies of these algorithms do not have the ability to find optimal solutions to both global and local problems, and their ability to explore the optimal solution set for problems needs to be improved. Summary of the invention

[0003] 1. Technical issues to be resolved In view of the deficiencies in the prior art, the present invention provides a task allocation method based on a whale optimization algorithm framework, which solves the problem of insufficient effectiveness of task allocation strategies.

[0004] (II) Technical solution In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions: A task allocation method based on the whale optimization algorithm framework is as follows: (1) Constructing the immune genetic whale optimization algorithm framework based on the whale optimization algorithm framework: The immune genetic whale optimization algorithm IGWOA solves the resource allocation problem of task allocation based on the intelligent search mechanism of the whale optimization algorithm WOA. At the same time, in order to improve the global and local search capabilities of the whale optimization algorithm, the crossover and immune ideas of the immune genetic method are used in the search and encirclement prey stages to increase the diversity of whale individuals representing task allocation strategies and improve the fitness value of whale individuals. In the bubble net predation stage, the mutation idea of ​​the immune genetic method is used to improve the algorithm's local search ability. IGWOA is based on task set and the participant set , use array structure to encode whale individuals representing task allocation strategy, and use random greedy strategy to generate population representing initial task allocation strategy ; Next, we select appropriate whale individuals to produce vaccines through the vaccine production strategy in the immunization operation. ; Afterwards, based on the introduction of vector coefficients in WOA The strategy enhances the global search capability and local search accuracy of the algorithm, as shown in the following formula: (1); in, for Random numbers generated with uniform distribution in the range, Indicates the number of iterations with the whale population The convergence factor that decreases linearly from the integer 2 to 0 is as follows: (2); in, Indicates the maximum number of iterations of the population. In each iteration of the whale individual, IGWOA will generate a random number , used to determine which algorithm stage IGWOA is in; when and| | When , IGWOA is in the stage of searching for prey, and random crossover operation is performed on whale individuals to increase individual diversity; when and| | When , IGWOA is in the stage of surrounding prey, at this time, the whale individuals are injected with vaccines to improve their individual fitness values; when When , IGWOA is in the bubble net predation stage, at this time, the whale individuals are mutated to improve the local search ability of the algorithm and find potential better solutions; Finally, IGWOA uses the repair operator to repair invalid whale individuals that do not meet the task allocation constraints and retain valid information; Cycle the above population evolution process times, the optimal task allocation strategy can be generated. The specific algorithm steps are as follows: a) Initialize the task allocation solution set population based on the participant set and the task set; b) Select the individual with the best fitness value in the population as the vaccine; c) Execution Loop iteration operation; d) Cross-species the individuals with the best fitness value and the second-best fitness value in the current population to generate candidate vaccines; e) Compare the fitness values ​​of the previous generation of vaccines, the best individuals of the current generation, and the candidate vaccines, and take the individual with the highest fitness value as the current generation of vaccines; f) Update the convergence factor according to formula (2) ,in is the current iteration number; g) For each individual in the current population, perform a traversal loop operation using various conditions in the population; h) Update the vector coefficients according to formula (1) ; i) If the random number ,and , then the first The individuals are crossed with random individuals to obtain the next generation population. Individual j) If the random number ,and , then the first The individuals are crossed with the current generation of vaccines to obtain the next generation population. Individual k) If the random number , then the first The individuals are mutated to obtain the next generation population. Individual l) End the loop operation of various conditions in the current population; m) Repair invalid individuals that do not meet the constraints in the next generation population into valid individuals; n) End Second loop iteration operation; o) Output the best individual in the last generation of the population that represents the optimal task allocation strategy; (2) Designing the individual whale structure to represent the task allocation strategy: The array structure is used to represent individual whales, as follows: Each individual whale is represented by The gene fragment index is used to represent the participant's ID. The gene fragment consists of several genes, each gene is used to represent the task ID. The whale individuals are divided into valid individuals that meet all constraints and invalid individuals that do not meet at least one constraint. (3) Define the individual fitness evaluation function of whales: The evaluation criteria of individual fitness is defined as platform profit, the higher the fitness value, the higher the profit; Assume a population , among which Whale individuals The fitness function The calculation method of is as follows: (3); in Indicates completion of task The profit earned by the platform Representation Task Allocation status, Representation Task is assigned, Representation Task Not assigned; (4) Initialize the first generation of whale population: Generated using a random greedy strategy The initial population of valid individuals, first, create a set of unassigned tasks and the set of candidate participants , and initialize a length of As an empty individual, we then randomly select candidate participants and unassigned tasks to generate valid gene fragments until the candidate participant set becomes an empty set, and iterate the above process. times, complete population initialization; (5) Design crossover operators, mutation operators and immune operators based on immune genetic methods: Inheriting the gene fragments with higher fitness values ​​in the parents to the new individuals; A gene is randomly selected from two gene fragments containing the task number in the whale individual and exchanged to produce a mutant individual; The immune operator consists of two parts: vaccine production and vaccine injection. The vaccine production operation aims to retain high-quality gene fragments in the whale population. Specifically, the whale individuals with higher fitness values ​​need to be injected into the vaccine at each iteration. and individual whales Perform crossover and repair operations to construct a valid individual and use it as a candidate vaccine , then, from the individual with the highest fitness value , Candidate vaccines And the previous generation of vaccines The individuals with the highest fitness value are selected as the new generation of vaccines , for the vaccination operation, the purpose is to pass on the high-quality gene fragments in the whale population to the next generation. Specifically, it is necessary to perform a cross operation between the new generation of vaccines and the selected individuals to generate new individuals with high-quality genetic fragments; (6) Design a repair operator to repair invalid whale individuals: Convert invalid whale individuals generated during crossover, mutation and immunization operations into valid whale individuals. For a whale individual, first confirm whether each of its gene fragments meets the constraints of task allocation. If not, take the largest subset of the task sequence of the gene fragment that meets the constraints and has no repeated tasks as a new gene fragment to replace the gene fragment that does not meet the constraints. Secondly, check whether the same tasks appear in different gene fragments. Based on the idea of ​​survival of the fittest, keep the repeated tasks only in the gene fragment with the highest fitness value and remove the repeated tasks in other gene fragments. Finally, the unassigned tasks are randomly assigned to participants who meet the constraints. By executing the above process, invalid whale individuals can be converted into valid whale individuals.

[0005] (7) Integrate specific operations such as population initialization, crossover, mutation, immunization, and repair into the immune genetic whale optimization algorithm framework to complete the design of the task allocation algorithm.

[0006] (III) Beneficial effects Compared with the prior art, the present invention provides a task allocation method based on the whale optimization algorithm framework, which has the following beneficial effects: The present invention utilizes the whale optimization algorithm framework to solve the resource allocation problem in task allocation, adopts crossover, mutation and immune operations in the immune genetic method, and takes into account the local and global optimization capabilities of the algorithm, so that the platform can generate a task allocation strategy that can maximize profits at a faster speed, thereby improving the task allocation rate and the activity of participants. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 This is a diagram showing the structure of the whale individual array in the present invention; Figure 2 Schematic diagram of the crossover operator in the present invention; Figure 3 It is a schematic diagram of the variation operation in the present invention; Figure 4 This is a schematic diagram of the immune operation in the present invention; Figure 5 It is a schematic diagram of the repair operation flow in the present invention; Figure 6 This is a schematic diagram of the impact of various algorithms on platform performance under different numbers of participants in the present invention; Figure 7 This is a schematic diagram of the impact of various algorithms on platform performance under different numbers of tasks in the present invention; Figure 8 This is a schematic diagram of the impact of various algorithms on platform performance under different matching willingness thresholds in the present invention; Fig. 9 This is a schematic diagram of the comparison of the running time of each algorithm under different numbers of tasks in the present invention. DETAILED DESCRIPTION

[0008] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0009] Example An embodiment of the present invention proposes: a task allocation method based on a whale optimization algorithm framework, which is as follows: (1) Constructing the immune genetic whale optimization algorithm framework based on the whale optimization algorithm framework: The immune genetic whale optimization algorithm IGWOA solves the resource allocation problem in task allocation based on the intelligent search mechanism of the whale optimization algorithm WOA. At the same time, in order to improve the global and local search capabilities of the whale optimization algorithm, the crossover and immune ideas of the immune genetic method are used in the search and encirclement prey stages to increase the diversity of whale individuals representing task allocation strategies and improve the fitness value of whale individuals. The mutation idea of ​​the immune genetic method is used in the bubble net predation stage to improve the algorithm's local search ability. IGWOA is based on task set and the participant set , the task set is represented as ,in is represented by the number of sensing tasks and the set of participants is represented by ,in Represented as the number of participants, using an array structure to encode the whale individuals representing the task allocation strategy, and using a random greedy strategy to generate a population representing the initial task allocation strategy ; Next, we select appropriate whale individuals to produce vaccines through the vaccine production strategy in the immunization operation. ; Afterwards, based on the introduction of vector coefficients in WOA The strategy enhances the global search capability and local search accuracy of the algorithm, as shown in the following formula: (1); in, for Random numbers generated with uniform distribution in the range, Indicates the number of iterations with the whale population The convergence factor that decreases linearly from the integer 2 to 0 is as follows: (2); in, Indicates the maximum number of iterations of the population. In each iteration of the whale individual, IGWOA will generate a random number , used to determine which algorithm stage IGWOA is in; when and| | When , IGWOA is in the stage of searching for prey, and random crossover operation is performed on whale individuals to increase individual diversity; when and| | When , IGWOA is in the stage of surrounding prey, at this time, the whale individuals are injected with vaccines to improve their individual fitness values; when When , IGWOA is in the bubble net predation stage, at this time, the whale individuals are mutated to improve the local search ability of the algorithm and find potential better solutions; Finally, IGWOA uses the repair operator to repair invalid whale individuals that do not meet the task allocation constraints and retain valid information; Cycle the above population evolution process times, the optimal task allocation strategy can be generated. The specific algorithm steps are as follows: a) Initialize the task allocation solution set population based on the participant set and the task set; b) Select the individual with the best fitness value in the population as the vaccine; c) Execution Loop iteration operation; d) Cross-species the individuals with the best fitness value and the second-best fitness value in the current population to generate candidate vaccines; e) Compare the fitness values ​​of the previous generation of vaccines, the best individuals of the current generation, and the candidate vaccines, and take the individual with the highest fitness value as the current generation of vaccines; f) Update the convergence factor according to formula (2) ,in is the current iteration number; g) For each individual in the current population, perform a traversal loop operation using various conditions in the population; h) Update the vector coefficients according to formula (1) ; i) If the random number ,and , then the first The individuals are crossed with random individuals to obtain the next generation population. Individual j) If the random number ,and , then the first The individuals are crossed with the current generation of vaccines to obtain the next generation population. Individual k) If the random number , then the first The individuals are mutated to obtain the next generation population. Individual l) For each individual in the current population, perform a traversal loop operation using various conditions in the population; m) Repair invalid individuals that do not meet the constraints in the next generation population into valid individuals; n) End Second loop iteration operation; o) Output the best individual in the last generation of the population that represents the optimal task allocation strategy; (2) Designing the individual whale structure to represent the task allocation strategy: Use array structure to represent individual whales, as follows Figure 1 As shown; Each individual whale is represented by The gene fragment index is used to represent the participant's ID. The gene fragment consists of several genes, each gene is used to represent the task ID. The whale individuals are divided into valid individuals that meet all constraints and invalid individuals that do not meet at least one constraint. (3) Define the individual fitness evaluation function of whales: The evaluation criteria of individual fitness is defined as platform profit, the higher the fitness value, the higher the profit; Assume a population , among which Whale individuals The fitness function The calculation method of is as follows: (3); in Indicates completion of task The profit earned by the platform Representation Task Allocation status, Representation Task is assigned, Representation Task Not assigned; (4) Initialize the first generation of whale population: Generated using a random greedy strategy The initial population of valid individuals, first, create a set of unassigned tasks and the set of candidate participants , and initialize a length of As an empty individual, we then randomly select candidate participants and unassigned tasks to generate valid gene fragments until the candidate participant set becomes an empty set, and iterate the above process. times, complete population initialization; (5) Design crossover operators, mutation operators and immune operators based on immune genetic methods: Inheriting the gene fragments with higher fitness values ​​in the parents to the new individuals, such as Figure 2 As shown; A random gene row is randomly selected from two gene fragments containing the task number in the whale individual to exchange, so as to produce a mutant individual, such as Figure 3 As shown; The immune operator consists of two parts, namely, the vaccine preparation operation and the vaccine injection operation, such as Figure 4 As shown; For the vaccine production operation, the goal is to retain the high-quality gene fragments in the whale population. Specifically, the whale individuals with higher fitness values ​​need to be and individual whales Perform crossover and repair operations to construct a valid individual and use it as a candidate vaccine , then, from the individual with the highest fitness value , Candidate vaccines And the previous generation of vaccines The individuals with the highest fitness value are selected as the new generation of vaccines , for the vaccination operation, the purpose is to pass on the high-quality gene fragments in the whale population to the next generation. Specifically, it is necessary to perform a cross operation between the new generation of vaccines and the selected individuals to generate new individuals with high-quality genetic fragments; (6) Design a repair operator to repair invalid whale individuals: The invalid whale individuals generated during the crossover, mutation and immunization operations are converted into valid whale individuals. For a whale individual, the repair operator algorithm flow is as follows: Figure 5 As shown; First, it is necessary to confirm whether each gene fragment meets the constraints of task allocation. If not, the largest subset of the task sequence of the gene fragment that meets the constraints and has no repeated tasks needs to be used as a new gene fragment to replace the gene fragment that does not meet the constraints. Secondly, check whether the same tasks appear in different gene fragments. Based on the idea of ​​survival of the fittest, keep the repeated tasks only in the gene fragment with the highest fitness value and remove the repeated tasks in other gene fragments. Finally, the unassigned tasks are randomly assigned to participants who meet the constraints. By executing the above process, invalid whale individuals can be converted into valid whale individuals.

[0010] (7) Integrate specific operations such as population initialization, crossover, mutation, immunization, and repair into the immune genetic whale optimization algorithm framework to complete the design of the task allocation algorithm.

[0011] By using the whale optimization algorithm framework to solve the resource allocation problem in task allocation, the crossover, mutation and immune operations in the immune genetic method are adopted to take into account the local and global optimization capabilities of the algorithm. In the prey search stage of the whale optimization algorithm, the crossover operation is integrated to increase the diversity of task allocation strategies. In the prey encirclement stage, the immune operation is integrated to improve the quality of the task allocation solution set. In the bubble net predation stage, the mutation operation is integrated to discover a better potential task allocation solution set. By combining the above-mentioned swarm intelligence algorithm ideas, rapid convergence and the generation of optimal task allocation strategies can be achieved, the platform profit can be maximized, and the platform task allocation rate and participant activity can be improved.

[0012] Compare the performance of the random allocation algorithm RAA, the greedy allocation algorithm GAA, the worker multi-task allocation genetic algorithm WMTA-GA, the whale optimization algorithm WOA, the genetic simulated annealing algorithm GA-SA and the immune genetic whale optimization algorithm IGWOA designed by the present invention in terms of profit, task allocation rate and the average number of tasks allocated to participants after being applied to the mobile crowd intelligence perception platform; Figure 6 , Figure 7 and Figure 8The specific situations of profit, task allocation rate and average number of tasks allocated to participants when tasks are evenly distributed as the number of participants, number of tasks and matching willingness threshold change are shown respectively. The proposed IGWOA performs better than other algorithms in terms of platform performance and converges faster than other algorithms. The reason is that the three algorithms, WMTA-GA, WOA and GA-SA, have a strong dependence on the initial population in the solution process, and WMTA-GA and GA-SA mainly rely on selection, crossover and mutation to find the optimal solution, while WOA updates the individual position according to the position of the current optimal solution and the distance between the individual whale and the optimal solution, so as to approach the optimal solution. Different from this, IGWOA incorporates the concepts of crossover, immunity and mutation in the immune genetic method into the search, encirclement of prey and bubble net predation stages of WOA, aiming to enhance both the global and local search capabilities of the algorithm. Therefore, the global search capability of IGWOA is superior to that of WMTA-GA, WOA and GA-SA. When the task scale is too large, RAA and GAA are prone to fall into the local optimal solution, so the algorithm performance indicators are relatively poor. Compare the performance of the random allocation algorithm RAA, the greedy allocation algorithm GAA, the worker multi-task allocation genetic algorithm WMTA-GA, the whale optimization algorithm WOA, the genetic simulated annealing algorithm GA-SA and the immune genetic whale optimization algorithm IGWOA designed by the present invention in terms of algorithm running time in the simulation experiment. Fig. 9 As shown; It can be clearly seen that IGWOA takes longer time than RAA and GAA, shorter than WMTA-GA, and much shorter than WOA and GA-SA. Since IGWOA uses the self-regulation mechanism in the immune genetic method, it speeds up the convergence process of the algorithm, reduces the number of unnecessary iterations, and reduces WOA's sensitivity to parameters to a certain extent, so it takes less time than WMTA-GA and WOA. The time consumption of GA-SA is mostly concentrated in the cooling operation of the simulated annealing algorithm, so it takes longer. As for RAA and GAA, they use direct strategies to deal with problems without complex searches and optimizations in the solution space, which takes less time than other algorithms. In summary, under the premise of maximizing platform profits, IGWOA takes relatively less time to run, and the performance of algorithm time complexity is relatively excellent.

[0013] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A task allocation method based on the whale optimization algorithm framework, characterized in that: include: 1) Based on the whale optimization algorithm framework, an immune genetic whale optimization algorithm framework is constructed to initialize the task allocation solution set population for the participant set and the task set, and iterate the algorithm to obtain the optimal individual with the optimal task allocation strategy; 2) Design a whale individual structure for representing the task allocation strategy. Use an array structure to represent whale individuals, which are divided into valid individuals that meet all constraints and invalid individuals that do not meet at least one constraint. 3) Define the whale individual fitness evaluation function, which is used to define the evaluation standard of individual fitness as platform profit. The higher the fitness value, the higher the profit. 4) Initialize the first generation of whale individual population, which is used to iterate the first generation of valid individuals to complete population initialization; 5) Based on the immune genetics method, the crossover operator, mutation operator and immune operator are designed to pass the gene fragments with higher fitness values ​​from the parents to the new individuals, thus generating new individuals with high-quality genetic fragments; 6) Design a repair operator for repairing invalid whale individuals, which is used to convert invalid whale individuals generated during crossover, mutation and immunization operations into valid whale individuals; 7) Integrate specific operations such as population initialization, crossover, mutation, immunity and repair into the immune genetic whale optimization algorithm framework to complete the design of the task allocation algorithm.

2. The task allocation method based on the whale optimization algorithm framework according to claim 1 is characterized in that: Based on the whale optimization algorithm framework, the immune genetic whale optimization algorithm framework is constructed: IGWOA is based on task set and the participant set , use array structure to encode whale individuals representing task allocation strategy, and use random greedy strategy to generate population representing initial task allocation strategy ; Next, we select appropriate whale individuals to produce vaccines through the vaccine production strategy in the immunization operation. ; Afterwards, based on the introduction of vector coefficients in WOA The strategy enhances the algorithm's global search capability and local search accuracy, as shown in the following formula: (1)； in, for Random numbers generated with uniform distribution in the range, Indicates the number of iterations with the whale population The convergence factor that decreases linearly from the integer 2 to 0 is as follows: (2)； in, Indicates the maximum number of iterations of the population. In each iteration of the whale individual, IGWOA will generate a random number , used to determine which algorithm stage IGWOA is in; when and| | When , IGWOA is in the stage of searching for prey, and random crossover operation is performed on whale individuals to increase individual diversity; when and| | When , IGWOA is in the stage of surrounding prey, at this time, the whale individuals are injected with vaccines to improve their individual fitness values; when When , IGWOA is in the bubble net predation stage, at this time, the whale individuals are mutated to improve the local search ability of the algorithm and find potential better solutions; Finally, IGWOA uses the repair operator to repair invalid whale individuals that do not meet the task allocation constraints and retain valid information; Cycle the above population evolution process times, the optimal task allocation strategy can be generated. The specific algorithm steps are as follows: a) Initialize the task allocation solution set population based on the participant set and the task set; b) Select the individual with the best fitness value in the population as the vaccine; c) Execution Loop iteration operation; d) Cross-species the individuals with the best fitness value and the second-best fitness value in the current population to generate candidate vaccines; e) Compare the fitness values ​​of the previous generation of vaccines, the best individuals of the current generation, and the candidate vaccines, and take the individual with the highest fitness value as the current generation of vaccines; f) Update the convergence factor according to formula (2) ,in is the current iteration number; g) For each individual in the current population, perform a traversal loop operation using various conditions in the population; h) Update the vector coefficients according to formula (1) ; i) If the random number ,and , then the first The individuals are crossed with random individuals to obtain the next generation population. Individual j) If the random number ,and , then the first The individuals are crossed with the current generation of vaccines to obtain the next generation population. Individual k) If the random number , then the first The individuals are mutated to obtain the next generation population. Individual l) End the loop operation of various conditions in the current population; m) Repair invalid individuals that do not meet the constraints in the next generation population into valid individuals; n) End Second loop iteration operation; o) Output the best individual in the last generation of the population that represents the optimal task allocation strategy.

3. The task allocation method based on the whale optimization algorithm framework according to claim 1 is characterized in that: Design a whale individual structure to represent the task allocation strategy: The array structure is used to represent individual whales, as follows: Each individual whale is represented by The gene fragment index is used to represent the participant's ID. The gene fragment consists of several genes, each gene is used to represent the task ID. The whale individuals are divided into valid individuals that meet all constraints and invalid individuals that do not meet at least one constraint.

4. The task allocation method based on the whale optimization algorithm framework according to claim 1 is characterized in that: Define the individual fitness evaluation function of whales: The evaluation criteria of individual fitness is defined as platform profit, the higher the fitness value, the higher the profit; Assume a population , among which Whale individuals The fitness function The calculation method of is as follows: (3)； in Indicates completion of task The profit earned by the platform Representation Task Allocation status, Representation Task is assigned, Representation Task Not allocated.

5. The task allocation method based on the whale optimization algorithm framework according to claim 1 is characterized in that: Initialize the first generation of whale population: Generated using a random greedy strategy The initial population of valid individuals, first, create a set of unassigned tasks and the set of candidate participants , and initialize a length of As an empty individual, we then randomly select candidate participants and unassigned tasks to generate valid gene fragments until the candidate participant set becomes an empty set, and iterate the above process. times, completing population initialization.

6. The task allocation method based on the whale optimization algorithm framework according to claim 1 is characterized in that: Design crossover operator, mutation operator and immune operator based on immune genetic method: Inheriting the gene fragments with higher fitness values ​​in the parents to the new individuals; One gene is randomly selected from two gene fragments containing the task number in the whale individual and exchanged to produce a mutant individual; The immune operator consists of two parts: vaccine production and vaccine injection. The vaccine production operation aims to retain high-quality gene fragments in the whale population. Specifically, the whale individuals with higher fitness values ​​need to be injected into the vaccine at each iteration. and individual whales Perform crossover and repair operations to construct a valid individual and use it as a candidate vaccine , then, from the individual with the highest fitness value , vaccine candidates And the previous generation of vaccines The individuals with the highest fitness value are selected as the new generation of vaccines For the vaccination operation, the purpose is to pass on the high-quality gene fragments in the whale population to the next generation. Specifically, it is necessary to perform a cross operation between the new generation of vaccines and the selected individuals to generate new individuals with high-quality genetic fragments.

7. The task allocation method based on the whale optimization algorithm framework according to claim 3 is characterized by: A repair operator designed to repair invalid whale individuals: Convert invalid whale individuals generated during crossover, mutation and immunization operations into valid whale individuals. For a whale individual, first, it is necessary to confirm whether each of its gene fragments meets the constraints of task allocation. If the constraints are not met, the largest subset of the task sequence of the gene fragment that meets the constraints and has no repeated tasks needs to be used as a new gene fragment to replace the gene fragment that does not meet the constraints. Secondly, check whether the same tasks appear in different gene fragments. Based on the idea of ​​survival of the fittest, keep the repeated tasks only in the gene fragment with the highest fitness value and remove the repeated tasks in other gene fragments. Finally, the unassigned tasks are randomly assigned to participants who meet the constraints. By executing the above process, invalid whale individuals can be converted into valid whale individuals.

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