An interval space reducing method for constrained problems with particle swarm optimization

Machado-Coelho T. 1 A Machado 1 Luc Jaulin 2, 3 P. Ekel 1 Witold Pedrycz 4 G.L. Soares 1
1 Pontifical Catholic University of Minas Gerais [Belo Horizonte]
2 Lab-STICC_ENSTAB_CID_PRASYS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
3 Pôle STIC_OSM
ENSTA Bretagne
4 University of Alberta
Abstract : In this paper, we propose a method for solving constrained optimization problems using Interval Analysis combined with Particle Swarm Optimization. A Set Inverter Via Interval Analysis algorithm is used to handle constraints in order to reduce constrained optimization to quasi unconstrained one. The algorithm is useful in the detection of empty search spaces, preventing useless executions of the optimization process. To improve computational efficiency, a Space Cleaning algorithm is used to remove solutions that are certainly not optimal. As a result, the search space becomes smaller at each step of the optimization procedure. After completing pre-processing, a modified Particle Swarm Optimization algorithm is applied to the reduced search space to find the global optimum. The efficiency of the proposed approach is demonstrated through comprehensive experimentation involving 100,000 runs on a set of well-known benchmark constrained engineering design problems. The computational efficiency of the new method is quantified by comparing its results with other PSO variants found in the literature.
Keywords : Interval Analysis Evolutionary computation Particle Swarm Optimization Constrained Optimization
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Machado-Coelho T., A Machado, Luc Jaulin, P. Ekel, Witold Pedrycz, et al.. An interval space reducing method for constrained problems with particle swarm optimization. Applied Soft Computing, Elsevier, 2017, 59, pp.405 - 417. ⟨10.1016/j.asoc.2017.05.022⟩. ⟨hal-01698453⟩

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