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Constraint-Handling Techniques for Particle Swarm Optimization Algorithms
Population-based methods can cope with a variety of different problems,
including problems of remarkably higher complexity than those traditional
methods can handle. The main procedure consists of successively updating a
population of candidate solutions, performing a parallel exploration instead of
traditional sequential exploration. While the origins of the PSO method are
linked to bird flock simulations, it is a stochastic optimization method in the
sense that it relies on random coefficients to introduce creativity, and a
bottom-up artificial intelligence-based approach in the sense that its
intelligent behaviour emerges in a higher level than the individuals' rather
than deterministically programmed. As opposed to EAs, the PSO involves no
operator design and few coefficients to be tuned. Since this paper does not
intend to study such tuning, general-purpose settings are taken from previous
studies. The PSO algorithm requires the incorporation of some technique to
handle constraints. A popular one is the penalization method, which turns the
original constrained problem into unconstrained by penalizing infeasible
solutions. Other techniques can be specifically designed for PSO. Since these
strategies present advantages and disadvantages when compared to one another,
there is no obvious best constraint-handling technique (CHT) for all problems.
The aim here is to develop and compare different CHTs suitable for PSOs, which
are incorporated to an algorithm with general-purpose settings. The comparisons
are performed keeping the remaining features of the algorithm the same, while
comparisons to other authors' results are offered as a frame of reference for
the optimizer as a whole. Thus, the penalization, preserving feasibility and
bisection methods are discussed, implemented, and tested on two suites of
benchmark problems. Three neighbourhood sizes are also considered in the
experiments.Comment: Preprint submitted to the 7th ASMO UK Conference on Engineering
Design Optimizatio