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

Sustainable last-mile delivery is a critical part of urban logistics, especially in the context of normalization of epidemic prevention and control. The key of the last-mile delivery problem is to design an optimal set of delivery routes with multi-task logistics unmanned ground vehicles. This paper proposes an improved route planning algorithm that takes the recharging cost and total travel time into account. Specifically, based on the ant colony optimization (ACO) algorithm, the pheromone concentration of each path is updated by using particle swarm optimization (PSO) algorithms, which enhances the global exploring ability of the ACO algorithm. In addition, adaptive factors are introduced to balance the flexibility and stability of the considered algorithm. Finally, the simulation results demonstrate that the modified ACO algorithm has a better performance and can be suited to the sustainable last-mile delivery routing problem.

Digitalization and the use of autonomous systems challenge companies in new ways, particularly concerning the automatic distribution of tasks. Various approaches are being discussed in research, specifically in the field of multi-agent systems. However, there does not seem to be a universally applicable and generally accepted solution. Due to the multitude of different approaches and the associated challenges, traceability is of particular interest and requires further analysis. This paper reviews the traceability of different negotiation-based approaches for task allocation in multi-agent systems. We conducted a structured literature review and implemented a prototype using an existing workflow engine for the approach we identified as most suitable, applying a weighted scoring model. The evaluation of the implemented demonstrator, performed both with and without restarting negotiations, indicates that a traceable distribution of tasks with a negotiation mechanism is feasible.

Optimized evolutionary algorithm and supervised ACO mechanism to mitigate attacks and improve performance of adhoc network

A new problem named formation distance problem (FDP) in multi-agents formation control is presented, with the aim to minimize the sum of distance all the agents traveled during every formation rebuilding process. By defining the formation distance matrix (FDM), we prove the FDP is an NP-hard problem. A recursion algorithm is described to exact solve the FDP, whose time complexity and space complexity are too large. In order to simplify the calculation, a so-called intelligent ant colony optimization (ACO) algorithm is used to solve this problem. The FDP is converted to modified traveling salesman problem (TSP) using topology knowledge firstly. Then the distance each agent traveled in the formation process is changed to the length between two nodes in the graph. Finally, the problem is converted to graph traversal problem, and a modified ACO algorithm is proposed to solve the FDP. Numerical Simulation results show the effectiveness of the presented algorithm.

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

Potential game for dynamic task allocation in multi-agent system

Autonomous heterogeneous swarm consisting of large amount of cross-domain vehicles with different payloads is now accepted as a game-changing technique especially in the military field. Its autonomy level depends heavily on the solutions to the usually very complex task allocation problem. This paper considered a complex-dependencies task allocation problem, where each task should be cooperatively conducted by multiple vehicles, and each vehicle should carry out multiple tasks. What is more, temporal sequence constraints exist for the considered four types of tasks, reconnaissance, electromagnetic interference, attack, and damage assessment. The global feasible task set, the gross feasible task set and a retraction mechanism are introduced into the ant colony algorithm to successfully account for varieties of task constraints, which can be seen from a simulation example.

In the field of parallel computing, there is an essential problem which is called Task Allocation Problem(TAP). The task allocation problem (TAP) is a problem where many of tasks require to be allocated to a set of processors.The number of m tasks that is needed to be allocated with number of n processors where (m>n) so that the time needed to process all the tasks is minimized. This paper presents an efficient algorithm (TAP_ACO_GA) to solve the task allocation problem. The proposed algorithm is based on the idea of Ant Colony Optimization Algorithm(ACO) and the idea of Genetic Algorithm (GA) .The proposed algorithm is tested in different dataset and its results are compared with the results in[2] and the results show that TAP_ACO_GA is better than the other algorithm.

The paper presents a modified ant colony optimization approach for the network coding resource minimization problem. It is featured with several attractive mechanisms specially devised for solving the network coding resource minimization problem: 1) a multi-dimensional pheromone maintenance mechanism is put forward to address the issue of pheromone overlapping; 2) problem-specific heuristic information is employed to enhance the heuristic search (neighboring area search) capability; 3) a tabu-table based path construction method is devised to facilitate the construction of feasible (link-disjoint) paths from the source to each receiver; 4) a local pheromone updating rule is developed to guide ants to construct appropriate promising paths; 5) a solution reconstruction method is presented, with the aim of avoiding prematurity and improving the global search efficiency of proposed algorithm. Due to the way it works, the ant colony optimization can well exploit the global and local information of routing related problems during the solution construction phase. The simulation results on benchmark instances demonstrate that with the five extended mechanisms integrated, our algorithm outperforms a number of existing algorithms with respect to the best solutions obtained and the computational time.

With the increase in traffic volume day by day, cities are facing the problem of extreme congestion in most of the developing countries. The congestion on the road leads to increase in travel time and travel cost as well as having a significant impact on the health of people. This paper aims to provide the parameter tuning in the existing modified ant colony optimization (MACO) algorithm using particle swarm optimization (PSO) algorithm to handle the congestion for actual road conditions. The performance of the MACO algorithm depends on the value of its parameters. Having a very large combination of these parameter values, a selection of the optimal values for these parameters is done using trial and error method. Therefore, we have proposed an algorithm based on PSO algorithm to find an optimal combination of the parameters of MACO under actual road conditions. MACO algorithm works under the assumption that all roads are in working condition, whereas the proposed work finds the best path even when the roads are temporarily blocked may be due to an accident or permanently blocked due to road construction. The experiments have been done for the network of North-West Delhi, India. It was found that the travel time is reduced significantly after using the optimal combination of values of parameters.

The use of fog and edge computing extends the computational capabilities of IoT systems to the network edge, contributing to the minimization of delays during task execution. Osmotic computing complements distributed computing by providing seamless integration between computational environments through dynamic migration of micro-elements across different hierarchy tiers according to current load conditions and resource availability. However, within the concept of osmotic computing, a key challenge remains the effective management of task allocation under conditions of uncertainty, dynamism, and heterogeneity of the IoT environment. The aim of this study is to improve the efficiency of resource utilization and task allocation in hierarchical IoT systems based on osmotic computing under uncertain and dynamically changing environmental conditions. The object of the study is the process of task allocation in multi-tier IoT systems that include cloud, fog, and edge computing. The subject of the study is methods and models for task allocation and computing resource management in IoT systems using the osmotic computing paradigm. The paper presents a three-tier hierarchical management model built on cloud, fog, and edge environments, which implements a centralized-decentralized management approach. Each tier is represented by a set of computing nodes and a management system that performs local task allocation, resource state monitoring, and micro-element management. The management system of the lower tier is subordinate to the higher-tier management system in the hierarchy. A method for decentralized task allocation in hierarchical IoT systems using fuzzy logic has been developed. The allocation method includes two decision-making stages using a fuzzy inference system: determining the direction of task allocation and selecting the optimal computing node for its execution. The determination of task allocation direction is carried out based on task characteristics, and the suitability rating of computing nodes is determined considering task execution latency, resource utilization efficiency, and load balancing. The task is assigned to the node with the maximum rating. The use of fuzzy logic ensures rational decision-making under conditions of uncertainty in real-time, which is characteristic of highly heterogeneous and dynamic IoT environments. Experimental modeling and investigation of the method were carried out using the iFogSim simulation environment. The research results show that the percentage of locally executed tasks remains virtually unchanged with different numbers of tasks, indicating stability in decision-making. Increasing the intensity of task generation leads to an increase in task computation latency due to increased load on computing nodes, while task assignment latency and response latency remain unchanged. The method demonstrated adaptability in task allocation for different types of tasks.

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

This paper focuses on issues related to the selection of Web services in multi-attribute states. With the rapid development of the Internet, how to choose a quality service for users under the constraints of multi-attribute factors is a question worth thinking about. To select the optimum combination of services for the user among a large number of multi-attribute services is our goal. User requirements can be viewed as a workflow, and a complete requirements workflow contains multiple tasks. Each task consists of different services, and different tasks are used to satisfy different user requirements. How to select the optimal services from each task and combine them to form a complete service portfolio is the work we need to accomplish. In this work, we optimize the candidate service attributes and calculate the attribute weights using a weighted arithmetic mean operator, and then select the optimal service combination using an optimal ant colony algorithm. Compared with the original method in the service selection process, MDM based on OACO Algorithm can solve the multi-attribute service selection problem, while the service data after the weight calculation avoids the constraints of multiple attributes on the service selection results. The complexity of the algorithm traversal is greatly decreases by using the Cartesian product idea to group the services based on the same attribute values and then using the ant colony algorithm. For the experimental results, the multi-attribute optimization based on the optimized ant colony algorithm has significant benefits over the original algorithm in terms of time performance.

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.

This paper presents a methodology to solve the manufacturing cell creation and the production scheduling problems for designing virtual cellular manufacturing systems (VCMSs). The objective is to minimize the total materials and components travelling distance incurred. The methodology consists of (i) a mathematical model that describes the characteristics of a VCMS and includes constraints such as delivery due dates of products, maximum capacities of resources, critical tools limitation, and (ii) an ant colony optimization (ACO) algorithm for manufacturing cell formation and production scheduling. Since the proposed ACO algorithm does not indicate a feasible schedule explicitly, two simple heuristics are developed to assign workstations to the operations of the jobs, and to construct the final schedule. To demonstrate the effectiveness of the proposed methodology, both the ACO algorithm and the genetic algorithm are applied to design manufacturing cells for a company in China producing internal combustion engine components. Comparison of the results obtained with the results supplied by the company on the existing manufacturing system show that both the ACO algorithm and the genetic algorithm together with the virtual cellular manufacturing concept perform better than the current manufacturing practice in terms of average workstation utilization, product completion time and system throughput. Also, the results of a set of randomly generated numerical experiments show that the proposed ACO algorithm generates excellent final solutions in a much shorter computation time when compared with the genetic algorithm. Therefore, the mathematical model and the ACO algorithm proposed in this paper form a simple, but effective and efficient methodology to solve the manufacturing cell creation and production scheduling problems for designing VCMSs.