Task Allocation In Multi-agent Systems Research Articles

Ant Colony Optimization Modelling for Task Allocation in Multi-Agent System for Multi-Target

Task allocation in multi-agent system can be defined as a problem of allocating a number of agents to the task. One of the problems in task allocation is to optimize the allocation of heterogeneous agents when there are multiple tasks which require several capabilities. To solve that problem, this research aims to modify the Ant Colony Optimization (ACO) algorithm so that the algorithm can be employed for solving task allocation problems with multiple tasks. In this research, we optimize the performance of the algorithm by minimizing the task completion cost as well as the number of overlapping agents. We also maximize the overall system capabilities in order to increase efficiency. Simulation results show that the modified ACO algorithm has significantly decreased overall task completion cost as well as the overlapping agents factor compared to the benchmark algorithm.

Potential game for dynamic task allocation in multi-agent system

This paper proposes a novel distributed multi-agent dynamic task allocation method based on the potential game. Consider that the workload of each task may vary in a dynamic environment, and the communication range of each agent constrains the selectable action set. Each agent makes the decision independently based on the local information. Firstly, a potential game-theoretic framework is designed. Any Nash equilibrium is guaranteed at least 50% of suboptimality, and the best Nash equilibrium is the optimal solution. Furthermore, a time variant constrained binary log-linear learning algorithm is provided and the global convergence is proved under certain conditions. Finally, numerical results show that the proposed algorithm performs well in terms of global searching ability, and verify the effectiveness of the distributed dynamic task allocation approach.

Ant Colony Optimization for task allocation in Multi-Agent Systems

Task allocation is a key issue of agent cooperation mechanism in Multi-Agent Systems. The important features of an agent system such as the latency of the network infrastructure, dynamic topology, and node heterogeneity impose new challenges on the task allocation in Multi-Agent environments. Based on the traditional parallel computing task allocation method and Ant Colony Optimization (ACO), a novel task allocation method named Collection Path Ant Colony Optimization (CPACO) is proposed to achieve global optimization and reduce processing time. The existing problems of ACO are analyzed; CPACO overcomes such problems by modifying the heuristic function and the update strategy in the Ant-Cycle Model and establishing a three-dimensional path pheromone storage space. The experimental results show that CPACO consumed only 10.3% of the time taken by the Global Search Algorithm and exhibited better performance than the Forward Optimal Heuristic Algorithm.

Distributed clustering for group formation and task allocation in multiagent systems: A swarm intelligence approach

Most clustering methods rely on central data structures and/or cannot cope with dynamically changing settings. Besides, these methods need some hints about the target clustering. However, issues related to the current use of Internet resources (distribution of data, privacy, etc.) require new ways of dealing with data clustering. In multiagent systems this is also becoming an issue as one wishes to group agents according to some features of the environment in order to have agents accomplishing the available tasks in an efficient way. In this paper we discuss the application of a clustering algorithm that is inspired by swarm intelligence techniques such as organization of bee colonies and task allocation among social insects. This application involves a complex task allocation scenario, the RoboCup Rescue, where tasks with different characteristics must be allocated to agents with different capabilities. Our results have shown that clustering agents is effective in this scenario as agents act in a more coordinated way.

A Distributed Anytime Algorithm for Dynamic Task Allocation in Multi-Agent Systems

We introduce a novel distributed algorithm for multi-agent task allocation problems where the sets of tasks and agents constantly change over time. We build on an existing anytime algorithm (fast-max-sum), and give it significant new capa- bilities: namely, an online pruning procedure that simplifies the problem, and a branch-and-bound technique that reduces the search space. This allows us to scale to problems with hundreds of tasks and agents. We empirically evaluate our algorithm against established benchmarks and find that, even in such large environments, a solution is found up to 31% faster, and with up to 23% more utility, than state-of-the-art approximation algorithms. In addition, our algorithm sends up to 30% fewer messages than current approaches when the set of agents or tasks changes.

Bidding-based optimization of task allocation in multi-Agent system

To avoid the problem of determining task allocation schema only by bidding results which obtain partial optimization in multi-Agent system assisted production task allocation,a method of global optimization was put forward.An objective function of production task allocation was established based on bidding results.Annealing evolution algorithm was designed to realize the synthesized bid evaluations.Instances indicate that this method is feasible and convenient to achieve global optimization of production task allocation.

Multi-attributes task allocation model in MAS and VIKOR based on interval grey number

This paper modeled a multi-attributes task allocation in Multi-Agent System (MAS), proposed an extended VIKOR method and raised the principle of optimal task allocation for empirical analysis on the basis of interval grey number’s norm. The results demonstrate that this method is very effective and able to help decision-maker make reasonable decision for multi-attributes task allocation.

Time-out protocol for task allocation in multi-agent systems

In multi-agent system, the implementation of a time-out protocol can improve workflow coherence and reduce the communication congestion due to the coordination process among agents. This approach is part of effective CIM workflow planning and control for agent-based operations emerging in assembly, manufacturing, service, and logistics. The case study illustrated in this article concerns industrial testing and rework services provided by a shared resource. Results of analyses have been verified by simulation to illustrate how performance improves or deteriorates when different time-out protocols are applied. A general model is introduced for numerical evaluation, identifying the recommended time-out threshold, and the regions where time-out protocols are, or are not useful for better workflow. Measures of workflow coordination performance, including time-oriented and viability-oriented measures are discussed.