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Multi-Agent Task Allocation is an emerging technology that changes the world in the epidemic scenario through its power to serve the needs of any hospital that requires unmanned operation. In this environment, the end user may want to have a better quality of unmanned service at low loss and high efficiency. We defined a new multi-agent task allocation problem (MATAP) in the epidemic scenario, and then MATAP was formulated. This paper presents a novel hybrid discrete approach that is based on the Differential Evolution Algorithm (DE) and Partial Swarm Optimization (PSO), namely D-DEPSO, for handling this problem. First, the initial personal population was handled by "mutation operation". Modulus operations in the "mutation operation" modify the numerical overflow of a variable. Second, when updating the speed matrix, the speed matrix is discretized using the "round" function we have defined. Then, a random permutation was used to delete repeated numbers and to reinsert integers in the "crossover operation". The diversity of the population was expanded by introducing the discrete mutation operation of the DE into the PSO and preserving the optimal solution for each generation using the properties of PSO. It can be used for optimizing a single objective function. Experimental results are compared with other existing metaheuristic algorithms, such as discrete DE, discrete PSO, improved discrete DE, improved discrete PSO, and improved discrete genetic algorithm, in terms of running time and loss. The experiments show that the optimal solutions obtained by D-DEPSO are better than those obtained by other five algorithms. For the actual problem, D-DEPSO can generate an optimal solution by optimal parameter setting to allocate tasks rationally. It can achieve a rational distribution of tasks in the prevention of disease.

Multi-Agent Task Allocation Based on Discrete DEPSO in Epidemic Scenarios