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

<p>This work presents an improved parallel biobjective hybrid real-coded genetic algorithm (MORCGA-MOPSO-II). The approach is based on the combined use of the parallel Multi-Objective Real-Coded Genetic Algorithm (MORCGA) and the Multi-Objective Particle Swarm Optimization (MOPSO). At the same time, clustering-based selection techniques are used to form subpopulations of parent individuals. Using well-known clustering algorithms (e.g., k-Means, hierarchical clustering, c-means, and DBSCAN) in combination with the proposed clustering-based mutation (the CL-mutation) directed toward the obtained cluster centers allows for improving the quality of the Pareto fronts’ approximations. The results of the MORCGA-MOPSO-II were compared with other well-known multi-objective evolutionary algorithms (e.g., SPEA2, NSGA-II, FCGA, MOSPO, etc.). Moreover, the MORCGA-MOPSO-II was integrated with the previously developed agent-based model of a goods exchange through the objective functions. As a result, the Pareto fronts have been obtained for the agent-based model of a goods exchange in different configurations of the initial distribution of agents.</p>
</abstract>

This work presents an improved parallel biobjective hybrid real-coded genetic algorithm (MORCGA-MOPSO-II). The approach is based on the combined use of the parallel Multi-Objective Real-Coded Genetic Algorithm (MORCGA) and the Multi-Objective Particle Swarm Optimization (MOPSO). At the same time, clustering-based selection techniques are used to form subpopulations of parent individuals. Using well-known clustering algorithms (e.g., k-Means, hierarchical clustering, c-means, and DBSCAN) in combination with the proposed clustering-based mutation (the CL-mutation) directed toward the obtained cluster centers allows for improving the quality of the Pareto fronts’ approximations. The results of the MORCGA-MOPSO-II were compared with other well-known multi-objective evolutionary algorithms (e.g., SPEA2, NSGA-II, FCGA, MOSPO, etc.). Moreover, the MORCGA-MOPSO-II was integrated with the previously developed agent-based model of a goods exchange through the objective functions. As a result, the Pareto fronts have been obtained for the agent-based model of a goods exchange in different configurations of the initial distribution of agents.

An Improved Parallel Biobjective Hybrid Real-Coded Genetic Algorithm with Clustering-Based Selection

MIREA – Russian Technological University

Cybernetics and Information Technologies

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

This work presents an improved parallel biobjective hybrid real-coded genetic algorithm (MORCGA-MOPSO-II). The approach is based on the combined use of the...

An Improved Parallel Biobjective Hybrid Real-Coded Genetic Algorithm with Clustering-Based Selection

Pubblicato online: 27 giu 2024

Pagine: 32 - 49

Ricevuto: 06 feb 2024

Accettato: 05 mar 2024

DOI: https://doi.org/10.2478/cait-2024-0014

Parole chiave
Clustering algorithms, Multi-objective optimization, Real-coded genetic algorithms, Particle swarm optimization, Multiagent socioeconomic systems, Agent-based modeling

© 2024 Andranik S. Akopov, published by Sciendo

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

An Improved Parallel Biobjective Hybrid Real-Coded Genetic Algorithm with Clustering-Based Selection

Pubblicato online: 27 giu 2024

Pagine: 32 - 49

Ricevuto: 06 feb 2024

Accettato: 05 mar 2024

DOI: https://doi.org/10.2478/cait-2024-0014

Parole chiaveClustering algorithms, Multi-objective optimization, Real-coded genetic algorithms, Particle swarm optimization, Multiagent socioeconomic systems, Agent-based modeling

© 2024 Andranik S. Akopov, published by Sciendo

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

Parole chiave
Clustering algorithms, Multi-objective optimization, Real-coded genetic algorithms, Particle swarm optimization, Multiagent socioeconomic systems, Agent-based modeling