Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm

This paper presents an idea of how new agents can be added autonomously to a group of existing agents without changing the existing communication topology among them. Autonomous agent addition to existing Multi-Agent Systems (MASs) can give a strategic advantage during the execution of a critical beyond visual line-of-sight (BVLOS) mission. The addition of the agent essentially means that new connections with existing agents are established. It is obvious that the consensus control energy increases as the number of agent increases considering a specific consensus protocol. The objective of this work is to establish the new connections in a way such that the consensus energy increase due to the new agents is minimal. The updated topology, including new connections, must contain a spanning tree to maintain the stability of the MASs network. The updated optimal topology is obtained by solving minimum additional consensus control energy using the Two-Dimensional Genetic Algorithm. The results obtained are convincing.

[1]  Ahmad Taher Azar,et al.  Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System , 2020, Sensors.

[2]  Kenneth A. De Jong,et al.  A formal analysis of the role of multi-point crossover in genetic algorithms , 1992, Annals of Mathematics and Artificial Intelligence.

[3]  Thang Nguyen,et al.  Formation Control and Obstacle Avoidance of Multiple Rectangular Agents With Limited Communication Ranges , 2017, IEEE Transactions on Control of Network Systems.

[4]  Bogdan Filipič,et al.  An Interactive Genetic Algorithm for Controller Parameter Optimization , 1993 .

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Ming Wang,et al.  Consensus seeking in a network of discrete-time linear agents with communication noises , 2015, Int. J. Syst. Sci..

[7]  Wei Liu,et al.  Leaderless consensus of multi-agent systems with Lipschitz nonlinear dynamics and switching topologies , 2016, Neurocomputing.

[8]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[9]  Long Wang,et al.  Equilibrium topology of multi-agent systems with two leaders: A zero-sum game perspective , 2016, Autom..

[10]  H. Gea,et al.  Two-dimensional packing problems using genetic algorithms , 2005, Engineering with Computers.

[11]  Long Wang,et al.  LQR‐based optimal topology of leader‐following consensus , 2015 .

[12]  Jingying Ma,et al.  Optimal topology for consensus of heterogeneous multi-agent systems , 2016, Neurocomputing.

[13]  Guanghui Wen,et al.  Consensus Tracking of Multi-Agent Systems With Lipschitz-Type Node Dynamics and Switching Topologies , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[14]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  Ahmed Rahmani,et al.  Consensus tracking for second-order nonlinear multi-agent systems with switching topologies and a time-varying reference state , 2016, Int. J. Control.

[16]  Haisheng Yu,et al.  Decentralized stabilizability and formation control of multi-agent systems with antagonistic interactions. , 2019, ISA transactions.

[17]  Jianchang Liu,et al.  Consensus gain conditions of stochastic multi-agent system with communication noise , 2016 .

[18]  Jinde Cao,et al.  Global Synchronization in an Array of Delayed Neural Networks With Hybrid Coupling , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[19]  Chun-ling Xie,et al.  Nuclear power plant fault diagnosis based on genetic-RBF neural network , 2006 .

[20]  Sung-Kwun Oh,et al.  Hybrid identification in fuzzy-neural networks , 2003, Fuzzy Sets Syst..

[21]  Jingyuan Zhan,et al.  Asynchronous Consensus of Multiple Double-Integrator Agents With Arbitrary Sampling Intervals and Communication Delays , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[22]  Jinde Cao,et al.  Local Synchronization of a Complex Network Model , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Gilbert Syswerda,et al.  Uniform Crossover in Genetic Algorithms , 1989, ICGA.

[24]  Long Cheng,et al.  A Mean Square Consensus Protocol for Linear Multi-Agent Systems With Communication Noises and Fixed Topologies , 2014, IEEE Transactions on Automatic Control.

[25]  Cheng Li,et al.  Path Planning of Mobile Robots Based on a Multi-Population Migration Genetic Algorithm , 2020, Sensors.

[26]  Tzung-Pei Hong,et al.  A Two-Dimensional Genetic Algorithm and Its Application to Aircraft Scheduling Problem , 2015 .

[27]  Yasumasa Fujisaki,et al.  Multiagent Consensus With Noisy Communication: Stopping Rules Based on Network Graphs , 2016, IEEE Transactions on Control of Network Systems.

[28]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[29]  Yongcan Cao,et al.  Optimal Linear-Consensus Algorithms: An LQR Perspective , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  Padmavathi Kora,et al.  Crossover Operators in Genetic Algorithms: A Review , 2017 .

[31]  Gang Feng,et al.  Consensus of Single Integrator Multi-Agent Systems with Unbounded Transmission Delays , 2017, 2017 13th IEEE International Conference on Control & Automation (ICCA).

[32]  Jin Bae Park,et al.  Leaderless and leader-following consensus for heterogeneous multi-agent systems with random link failures , 2014 .

[33]  Ş. Niculescu.  Artificial neural networks and genetic algorithms in QSAR , 2003 .

[34]  Antonios Tsourdos,et al.  Optimal topology for consensus using genetic algorithm , 2020, Neurocomputing.

[35]  Aiping Wang,et al.  Event-based consensus control for single-integrator networks with communication time delays , 2016, Neurocomputing.