This paper presents an approach that uses reinforcement learning (RL) algorithms to solve combinatorial optimization problems. In particular, the approach combines both local and global search characteristics: local information as encoded by typical RL schemes and global information as contained in a population of search agents. The effectiveness of the approach is demonstrated on both the Asym...

This paper is a major step towards a pioneering software framework for the reusable design and implementation of parallel metaheuristics on Graphics Processing Units (GPU). The objective is to revisit the ParadisEO framework to allow its utilization on GPU accelerators. The focus is on local search metaheuristics and the parallel exploration of their neighborhood. The challenge is to make the G...

It is the purpose of this paper to propose and discuss certain modifications of the ideas concerning GromovHausdorff distances in order to tackle the problems of shape matching and comparison. These reformulations render these distances more amenable to practical computations without sacrificing theoretical underpinnings. A second goal of this paper is to establish links to several other practi...

In this work we present a simple, but effective method of enhancing and exploiting diversity from multiple packet transmissions in systems that employ nonbinary linear modulations such as PSK and QAM. This diversity improvement results from redesigning the symbol mapping for each packet transmission. Using a general framework for evaluating the upper bound of bit error rate (BER) with multiple ...

In this paper we present a simple, but effective method of creating and exploiting diversity from packet retransmissions in systems that employ nonbinary modulations such as PSK and QAM. This diversity results from differing the symbol mapping for each packet retransmission. By developing a general framework for evaluating the bit error rate (BER) upper hound with multiple transmissions, a crit...

In this paper, we present a new local search algorithm for solving the Quadratic Assignment Problem based on the Kernighan.Lin heuristic for the. Graph Partitioning Problem. We also prove that finding a local optimum for the Quadratic Assignment Problem, with the neighborhood.structure defined in the algorithm, is PLi·complete. The greatest advantages of the algorithm are its simplicity and sPf...

There exist a number of problem classes for which obtaining the exact solution becomes exponentially expensive with increasing problem size. The quadratic assignment problem (QAP) or the travelling salesman problem (TSP) are just two examples of such NP-hard problems. In practice, approximate algorithms are employed to obtain a suboptimal solution, where one must face a trade-off between comput...

The Operating Theater (OT) is known to be a fluctuating production system. The unpredictability of populations’ needs has an impact on the required human and material resources and makes the Operating Theater a dynamic environment. Thus, the use of dynamic models is getting more realistic to solve OT Layout Problem (OTLP). While solving Dynamic OTLP, Real-life Operating Theater (OT) sizes are l...

Semideenite programming (SDP) relaxations for the quadratic assignment problem (QAP) are derived using the dual of the (homogenized) Lagrangian dual of appropriate equivalent representations of QAP. These relaxations result in the interesting, special, case where only the dual problem of the SDP relaxation has strict interior, i.e. the Slater constraint qualii-cation always fails for the primal...

This paper proposes a mathematical formulation for the facility layout problem (FLP) based on the generalized quadratic assignment problem (GQAP). The GQAP is a generalization of the NP-hard quadratic assignment problem (QAP) that allows multiple facilities to be assigned to a single location as long as the capacity of the location allows. As a case study, we adapt the GRASP with path-relinking...