As exact algorithms are unfeasible to solve real optimization problems, due their computational complexity, meta-heuristics usually used them. However, choosing a meta-heuristic particular problem is non-trivial task, and often requires time-consuming trial error process. Hyper-heuristics, which heuristics choose heuristics, have been proposed as means both simplify improve algorithm selection ...

Meta-heuristics are algorithms which are applied to solve problems when conventional algorithms can not find good solutions in reasonable time; evolutionary algorithms are perhaps the most well-known examples of meta-heuristics. As there are many possible meta-heuristics, finding the most suitable meta-heuristic for a given problem is not a trivial task. In order to make this choice, one can de...

Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic methods and tabu search (TS), simulated annealing, and pa...

assigning facilities to locations is one of the important problems, which significantly is influence in transportation cost reduction. in this study, we solve quadratic assignment problem (qap), using a meta-heuristic algorithm with deterministic tasks and equality in facilities and location number. it should be noted that any facility must be assign to only one location. in this paper, first o...

Meta-heuristics are commonly applied to solve various global optimization problems. In order make the meta-heuristics performing a search, balancing their exploration and ability is still an open avenue. This manuscript proposes novel Opposition-based learning scheme, called “PCOBL” (Partial Centroid Learning), inspired by partial centroid. PCOBL aims improve performance through maintaining eff...

Abstract Variational quantum algorithms, a class of heuristics, are promising candidates for the demonstration useful computation. Finding best way to amplify performance these methods on hardware is an important task. Here, we evaluate optimization heuristics with existing techniques called “meta-learners.” We compare meta-learner evolutionary strategies, L-BFGS-B and Nelder-Mead approaches, t...