A novel approach to task assignment in a cooperative multi-agent design system

The task assignment problem is an important topic in multi-agent systems research. Distributed real-time systems must accommodate a number of communication tasks, and the difficulty in building such systems lies in task assignment (i.e., where to place the tasks). This paper presents a novel approach that is based on artificial bee colony algorithm (ABC) to address dynamic task assignment problems in multi-agent cooperative systems. The initial bee population (solution) is constructed by the initial task assignment algorithm through a greedy heuristic. Each bee is formed by the number of tasks and agents, and the number of employed bees is equal to the number of onlooker bees. After being generated, the solution is improved through a local search process called greedy selection. This process is implemented by onlooker and employed bees. In greedy selection, if the fitness value of the candidate source is greater than that of the current source, the bee forgets the current source and memorizes the new candidate source. Experiments are performed with two test suites (TIG representing real-life tree and Fork–Join problems and randomly generated TIGs). Results are compared with other nature-inspired approaches, such as genetic and particle swarm optimization algorithms, in terms of CPU time and communication cost. The findings show that ABC improves these two criteria significantly with respect to the other approaches.