Recently symmetric multi processing (SMP) has become available at a reasonable cost. In this paper, we propose several types of parallel ACO algorithms with SMP for solving the quadratic assignment problem (QAP). These models include the master-slave models and the island models. We evaluated each parallel algorithm with a condition that the run time for each parallel algorithm and the base seq...

The quadratic assignment problem (QAP) is among the hardest combinatorial optimization problems. Some instances of size n = 30 have remained unsolved for decades. The solution of these problems requires both improvements in mathematical programming algorithms and the utilization of powerful computational platforms. In this article we describe a novel approach to solve QAPs using a state-of-the-...

This paper presents two approaches that address the problems of the local character of the search and imprecise state representation of reinforcement learning (RL) algorithms for solving combinatorial optimization problems. The first, Bayesian, approach aims to capture solution parameter interdependencies. The second approach combines local information as encoded by typical RL schemes and globa...

Many heuristics, such as simulated annealing, genetic algorithms, greedy randomized adaptive search procedures are stochastic. In this paper, we propose a deterministic heuristic algorithm, which is applied to the quadratic assignment problem. We refer this algorithm to as intensive search algorithm (or briefly intensive search). We tested our algorithm on the various instances from the library...

We report on the development of algorithms for solving the Quadratic 3-dimensional Assignment Problem (Q3AP). The application is a hybrid ARQ scheme for enriching diversity among multiple packet transmissions by optimizing the mapping of transmission symbols to data. Our current exact algorithm, based on a reformulation linearization technique, solves Q3AP instances of size 13 or smaller. Our f...

Previous papers have proposed an algorithm called the edge histogram sampling algorithm (EHBSA) that models the relative relation between two nodes (edge) of permutation strings of a population within the PMBGA framework for permutation domains. This paper proposes another histogram based model we call the node histogram sampling algorithm (NHBSA). The NHBSA models node frequencies at each abso...

We consider transformations of the (metric) Quadratic Assignment Problem (QAP), that exploit the metric structure of a given instance. We show in particular, how the structural properties of rectangular grids can be used to improve a given lower bound. Our work is motivated by previous research of G.S. Palubetskes, and it extends a bounding approach proposed by J. Chakrapani and J. Skorin-Kapov...

The Quadratic Assignment Problem (QAP) is one of the classical combinatorial optimization problems and is known for its diverse applications. In this paper, we suggest a genetic algorithm for the QAP and report its computational behavior. The genetic algorithm incorporates many greedy principles in its design and, hence, we refer to it as a greedy genetic algorithm. The ideas we incorporate in ...

Tourism eco-efficiency analysis is an effective tool to solve the problem of sustainable tourism development. The evaluation index system was constructed in study, and undesirable output super-slacks-based measure model used estimate 26 cities Yangtze River Delta. Then, modified gravity based on values each city construct a spatial correlation network. structural characteristics association net...

Recent developments in fitness landscape analysis include the study of Local Optima Networks (LON) and applications of the Elementary Landscapes theory. This paper represents a first step at combining these two tools to explore their ability to forecast the performance of search algorithms. We base our analysis on the Quadratic Assignment Problem (QAP) and conduct a large statistical study over...