Optimal topology for consensus using genetic algorithm

Mondal, Sabyasachi; Tsourdos, Antonios

doi:10.1016/j.neucom.2020.04.107

Cited by 11 publications

(11 citation statements)

References 29 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The solution to this problem can be obtained using a two-dimensional genetic algorithm. The chromosome of 2D-GA is a matrix which is appropriate to represent an adjacency matrix [20]. Therefore, in this paper, the adjacency matrix is considered as a chromosome.…”

Section: Solution Method: Two-dimensional Genetic Algorithm (2d-ga)mentioning

confidence: 99%

“…The optimal topology obtained is a star graph which shows the direct links between the leader and the followers. In [20], the authors presented the optimal topology problem and solved it using a Two-Dimensional Genetic Algorithm (2D-GA), which relaxes the requirement of direct connection among the leader and the followers. It is important to note that an increase in the number of agents in a MASs will result in an increase in the consensus control energy.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…The chromosome for this problem is the modified adjacency matrices A mod , which is discussed in Section 3. In [20], the authors considered the adjacency matrix as a 2D chromosome. An example of such a chromosome is given in Figure 1.…”

Section: Two-dimensional Chromosome Representationmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm

Mondal

Tsourdos

2020

Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Solution Method: Two-dimensional Genetic Algorithm (2d-ga)mentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm

Mondal

Tsourdos

2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The time table or schedule is considered as a 2D chromosome. Also, it is used to obtain optimal communication topology for consensus in [17], [18]. In the algorithm, the genes of the chromosomes are created in a random manner.…”

Section: A Two-dimensional Genetic Algorithm (2d-ga)mentioning

confidence: 99%

Handover Prediction for Aircraft Dual Connectivity Using Model Predictive Control

2021

Self Cite

View full text Add to dashboard Cite

Providing connectivity to aircraft such as flying taxis is a significant challenge for tomorrow's aviation communication systems. One major problem is to provide ground to air (G2A) connectivity, especially in the airport, rural and sub-rural areas where the number of radio ground stations is not adequate to support the data link resulting in frequent interruption. Hence, effective handover decision-making is necessary to provide uninterrupted services to aircraft while moving from one domain to another. However, the existing handover decision is not efficient enough to solve the aircraft connectivity in such airspace. To overcome this problem, a prediction based optimal solution to handover decision making (handover prediction) would be appropriate to provide seamless dual connectivity to aircraft. In this paper, the handover prediction problem is formulated as a constrained optimization problem in the framework of the model for predictive control (MPC). The cost function and the constraints are derived in terms of dual connectivity variables over the prediction horizon. This problem is solved using a two-dimensional genetic algorithm (2D-GA) to obtain the predictive optimal handover solution. Simulation results show that the proposed dual connectivity handover can significantly improve the handover success probability. Finally, our results show that network densification and predictive control model have improved aircraft performance. INDEX TERMS Handover, Dual Connectivity, Model Predictive Control, Two-dimensional Genetic Algorithm (2D-GA).

show abstract

“…In [ 28 ], a genetic algorithm with a multidimensional chromosome is used to describe nodes belonging to multiple communities in dense overlapping communities. Another critical and new application of 2D-GA is found in [ 29 , 30 ]. The objective of this paper is to obtain an optimal communication topology to achieve consensus among agents such that the consensus control energy of the agents is minimized.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem

Mondal¹,

Tsourdos²

2021

Sensors

Self Cite

View full text Add to dashboard Cite

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.

show abstract

Optimal topology for consensus using genetic algorithm

Cited by 11 publications

References 29 publications

Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm

Autonomous Addition of Agents to an Existing Group Using Genetic Algorithm

Handover Prediction for Aircraft Dual Connectivity Using Model Predictive Control

Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem

Contact Info

Product

Resources

About