<p>Multifactorial Optimization (MFO) and Evolutionary Transfer
Optimization (ETO) are new optimization challenging paradigms for which the
multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting
despite limitations. MOPSO has been widely used in static/dynamic
multi-objective optimization problems, while its potentials for multi-task
optimization are not completely unveiled. This paper proposes a new Distributed
Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task
optimization. This new system has a distributed architecture on a set of
sub-swarms that are dynamically constructed based on the number of optimization
tasks affected by each particle skill factor. DMFPSO is designed to deal with
the issues of handling convergence and diversity concepts separately. DMFPSO
uses Beta function to provide two optimized profiles with a dynamic switching
behaviour. The first profile, Beta-1, is used for the exploration which aims to
explore the search space toward potential solutions, while the second Beta-2
function is used for convergence enhancement. This new system is tested on 36
benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective
Optimization Competition. Comparatives with the state-of-the-art methods are
done using the Inverted General Distance (IGD) and Mean Inverted General
Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best
results on most tested problems.</p>
Performance-aware placement and chaining scheme for virtualized network functions: a particle swarm optimization approach
