Abstract
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.