A New Parallel Cooperative Landscape Smoothing Algorithm and Its Applications on TSP and UBQP
This work addresses combinatorial optimization challenges for researchers and practitioners, but it is incremental as it builds on existing transformation methods.
The paper tackled combinatorial optimization problems by extending a homotopic convex transformation to unconstrained binary quadratic programming (UBQP), proving unimodality theoretically and empirically verifying smoothing effects, and introduced a parallel cooperative variant (PC-LSILS) that improved performance on UBQP and TSP instances.
Combinatorial optimization problem (COP) is difficult to solve because of the massive number of 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 extends the HC transformation approach to unconstrained binary quadratic programming (UBQP) by proposing a method to construct a unimodal toy UBQP of any size. We theoretically prove the unimodality of the constructed toy UBQP. After that, we apply this unimodal toy UBQP to smooth the original UBQP by using the HC transformation framework and empirically verify the smoothing effects. 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.