<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.</p>
</abstract>

This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

National Research University Higher School of Economics

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel...

Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

Pubblicato online: 07 dic 2021

Pagine: 127 - 144

Ricevuto: 11 mag 2021

Accettato: 15 giu 2021

DOI: https://doi.org/10.2478/cait-2021-0034

Parole chiave
Simulation-based optimisation, autonomous transportation systems, real-coded genetic algorithms, multi-agent systems, scalable nonuniform mutation

© 2021 Andranik S. Akopov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

Pubblicato online: 07 dic 2021

Pagine: 127 - 144

Ricevuto: 11 mag 2021

Accettato: 15 giu 2021

DOI: https://doi.org/10.2478/cait-2021-0034

Parole chiaveSimulation-based optimisation, autonomous transportation systems, real-coded genetic algorithms, multi-agent systems, scalable nonuniform mutation

© 2021 Andranik S. Akopov et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Simulation-based optimisation, autonomous transportation systems, real-coded genetic algorithms, multi-agent systems, scalable nonuniform mutation