A New Parallel Cooperative Landscape Smoothing Algorithm and Its Applications on TSP and UBQP
arxiv(2024)
摘要
Combinatorial optimization problem (COP) is difficult to solve because of the
massive local optimal solutions in his solution space. Various methods have
been put forward to smooth the solution space of COPs, including homotopic
convex (HC) transformation for the traveling salesman problem (TSP). This paper
first extends the HC transformation approach to the unconstrained binary
quadratic programming (UBQP). We theoretically prove the effectiveness of the
proposed HC transformation method on smoothing the landscape of the UBQP.
Subsequently, we introduce an iterative algorithmic framework incorporating HC
transformation, referred as landscape smoothing iterated local search (LSILS).
Our experimental analyses, conducted on various UBQP instances show the
effectiveness of LSILS. Furthermore, this paper proposes a parallel cooperative
variant of LSILS, denoted as PC-LSILS and apply it to both the UBQP and the
TSP. Our experimental findings highlight that PC-LSILS improves the smoothing
performance of the HC transformation, and further improves the overall
performance of the algorithm.
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