Particle swarm optimization : adaptation of tribes to the multiobjective optimization
type: Dataset
description: Meta-heuristics are recognized to be successful to deal with multiobjective optimization problems but still with limited success in engineering fields. In an environment where only the performance counts, the stochastic aspect of meta-heuristics again seems to be a difficult obstacle to cross for the decision-makers. It is, thus, important that the researchers of the community concern a quite particular effort to ease the handling of those algorithms. The more the algorithms will be easily accessible for the novices, the more the use of these algorithms can spread. Among the possible improvements, reducing the number of parameters is considered as the most challenging one. In fact, the performance of meta-heuristics is strongly dependent on their parameters values. TRIBES presents an attempt to remedy this problem. In fact, it is a particle swarm optimization (PSO) algorithm that works in an autonomous way. It was proposed by Maurice Clerc. Indeed, like every other meta-heuristic, PSO requires many parameters to be fitted every time a new problem is considered. The major contribution of TRIBES is to avoid the effort of fitting them. We propose, in this thesis, an adaptation of TRIBES to the multiobjective optimization. Our aim is to conceive a competitive PSO algorithm free of parameters. We consider the main mechanisms of TRIBES to which are added new mechanisms intended to handle multiobjective problems. After the experimentations, we noticed that Multiobjective-TRIBESis not competitive compared to other multiobjective algorithms representative of the state of art. It can be explained by the premature stagnation of the swarm. To remedy these problems, we proposed the hybridization between Multiobjective-TRIBES and local search algorithms such as simulated annealing and tabu search. The idea behind the hybridization was to improve the capacity of exploitation of Multiobjective-TRIBES. Our algorithms were finally applied to sizing analogical circuits' problems
identifier: NNT: 2013PEST1099
issued: 2026-05-05T13:49:05.359671
language: fr
modified: 2026-05-05T13:49:05.359676
contact point: Organization
  fn: CCSD
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: multiobjective-optimization
keyword: optimisation-continue
keyword: optimisation-multiobjectif
keyword: optimisation-par-essaim-particulaire
keyword: particle-swarm-optimization
keyword: recherche-tabou
keyword: recuit-simule
keyword: simulated-annealing
keyword: tabu-search
keyword: theses
keyword: tribes
landing page: https://theses.hal.science/tel-00981558

Particle swarm optimization : adaptation of tribes to the multiobjective optimization
type: Distribution
format: HTML
issued: 2026-05-05T13:49:05.361168
modified: 2026-05-05T13:49:05.345168
access URL: https://theses.hal.science/tel-00981558

test_moissonnage_selune
type: Agent