WSEAS Transactions on Computer Research
Print ISSN: 1991-8755, E-ISSN: 2415-1521
Volume 12, 2024
Sensitivity Analysis of a Parallel Particle Swarm Optimization Clustering Algorithm for Multi-objective Optimization
Author:
Abstract: Parallel bio-inspired algorithms have been successful in solving multi-objective optimisation problems.
In this work, we discuss a parallel particle swarm algorithm with added clustering for solving multi-objective
optimisation problems. The aim of this work is to perform sensitivity analysis of the parallel particle swarm
algorithm. We need to see how the added parallelism improves the overall execution time. Also, looked at
the effect of different strategies for population initialisation (such as mutating current set of leaders, random
population and lookup in archive for nearest points using geometric calculation). The results show that using
different migration frequencies for scattering reduced the overall overlap between processors. Results regarding
how clustering and gathering affect performance metrics are also reported.
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Keywords: Particle Swarm Algorithms, Parallel Algorithms, Migration, Clustering, Multi-objective
Optimisation, Sensitivity Analysis
Pages: 359-366
DOI: 10.37394/232018.2024.12.35