In this paper, the design of consensus control law in leader-follower multi-agent Systems will be studied with agents with TS fuzzy dynamic model. In this study, assuming an excitation-event mechanism for updating the control signal of each agent, while reducing the number of control signal updates, the closed-loop system stability will be guaranteed. Moreover, in the proposed event triggered control strategy, the Zeno-behaviour is avoided. Each agent only sends data to other agents at the time of its trigger instants, and its control input is updated only at the same time. The design of the control law and the excitation-event mechanism will lead to the solution of a linear matrix inequality. The innovation of the study is in selecting a fuzzy model for agents that can extend the solution of the consensus problem in multi-agent Systems to more nonlinear Systems. Finally, the effectiveness of the proposed method is shown through a numerical example.

This study was conducted during summer and winter of 2018- 2019 in the agricultural research field of Shahid Chamran University. Experimental design was split- plot based on RCBD with three replications. The main plot was the type of agricultural system in three levels including conventional (Conv), organic (Org) and sustainable (Sust) (integrated between Conv and Org) and sup- plot was the type of pre- cultivated crop in sequence with wheat including cultivation of mung bean (M- W), corn (C- W), sesame (S- W) and fallow (F- W). Yield quantity (yield and its component) and quality (grain protein), an estimate of photosynthesis matter transfer index of wheat and soil organic carbon (SOC) after one double-cropping were measured. The result showed that the highest (545.04 g/m2) and the lowest (409.28 g/m2) seed yields were obtained in Conv and Org respectively. In contract, with the changing type of system from Conv to Org, grain protein was increased significantly (from 8.3 to 9.6 %). In addition, the highest (535.47 g/m2) yield of wheat was obtained from M- W double cropping. On the other hands the highest remobilization and current photosynthesis matter were obtained in the organic agricultural system with M- W and conventional with M- W double cropping. The situation of SOC showed that the highest (33.18 mg/g) SOC was obtained in the organic agricultural system with C- W double cropping. The reason for improving SOC in the organic and sustainable agricultural system was application of organic matter (compost and vermicompost) and crop residue management. Totally, from the crop ecology point of view, sustainable agricultural method with a sequence of M- W was the most desirable system.

This paper, studies the controller design for containment problem for a class of MultiAgent Systems with identical time-invariant continuous-time nonlinear dynamics and fixed directed communication graph. In this problem, the Lyapunov stability theorem, the graph theory and matrix linear inequality are used. The agents are divided into two groups of leaders and followers. In containment problem, all the followers are controlled under which will asymptotically converge to the convex hull spanned by the leaders so distributed communication protocol with fixed time delay is considered and four-step algorithm is proposed for obtaining parameters and gain matrix. The above case is proved to be sufficient condition under theorem. To illustrate the reliability and efficiency of the proposed method, numerical example with simulations are presented.

Recent researches show that diverse Systems in many different areas can be represented as complex networks. Examples of these include the Internet, social networks and so on. In each case, the system can be modeled as a complex and very large network consisting of a large number of entities and associations between them. Most of these networks are generally sparse in global yet dense in local. They have vertices in a group structure and the vertices within a group have higher density of edges while vertices among groups have lower density of edges. Such a structure is called community and is one of the important features of the network and is able to reveal many hidden characteristics of the networks. Today, community detection is used to improve the efficiency of search engines and discovery of terrorist organizations on the World Wide Web. Community detection is a challenging NP-hard optimization problem that consists of searching for communities. It is assumed that the nodes of the same community share some properties that enable the detection of new characteristics or functional relationships in a network. Although there are many algorithms developed for community detection, most of them are unsuitable when dealing with large networks due to their computational cost. Nowadays, MultiAgent Systems have been used to solve different problems, such as constraint satisfaction problems and combinatorial optimization problems with satisfactory results. In this paper, a new MultiAgent reinforcement learning algorithm is proposed for community detection in complex networks. Each agent in the MultiAgent system is an autonomous entity with different learning parameters. Based on the cooperation among the learning agents and updating the action probabilities of each agent, the algorithm interactively will identify a set of communities in the input network that are more densely connected than other communities. In other words, some independent agents interactively attempt to identify communities and evaluate the quality of the communities found at each stage by the normalized cut as objective function, then, the probability vectors of the agents are updated based on the results of the evaluation. If the quality of the community found by an agent in each of the stages is better than all the results produced so far, then it is referred to as the successful agent and the other agents will update their probability vectors based on the result of the successful agent. In the experiments, the performance of the proposed algorithm is validated on four real-world benchmark networks: the Karate club network, Dolphins network, Political books network and College football network, and synthetic LFR benchmark graphs with scales of 1000 and 5000 nodes. LFR networks are suitable for systematically measuring the property of an algorithm. Experimental results show that proposed approach has a good performance and is able to find suitable communities in large and small scale networks and is capable of detecting the community in complex networks In terms of speed, precision and stability. Moreover, according to the systematic comparison of the results obtained by the proposed algorithm with four state-of-the-art community detection algorithms, our algorithm outperforms the these algorithms in terms of modularity and NMI, also, it can detect communities in small and large scale networks with high speed, accuracy, and stability, where it is capable of managing large-scale networks up to 5000 nodes.

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NPhard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic MultiAgent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literatureinstances. New upper bounds are found, showing the effectiveness of the presented approach.

Exploration-exploitation balance through temperature regulation in MultiAgent reinforcement learning of different task types is studied. Considered tasks are AND-type, OR-type, and their compositions. The presented study shows that, in contrary to AND-type tasks, the temperature should be set high at the beginning of learning of OR-type tasks and be reduced very gradually during the learning. It is also proposed that, the temperature control policy in learning composite tasks is decided based on the ratio of the number of redundant agents in the learning team to the team population. This ratio shows the similarity of composite task to the two main task types. Learned individual knowledge and the team performance in a simulated benchmarking task are employed for analysis of the presented methods.