Parallel hybrid chicken swarm optimization for solving the quadratic assignment problem

In this research, we intend to suggest a new method based on a parallel hybrid chicken swarm optimization (PHCSO) by integrating the constructive procedure of GRASP and an effective modified version of Tabu search. In this vein, the goal of this adaptation is straightforward about the fact of preventing the stagnation of the research. Furthermore, the proposed contribution looks at providing an optimal trade-off between the two key components of bio-inspired metaheuristics: local intensification and global diversification, which affect the efficiency of our proposed algorithm and the choice of the dependent parameters. Moreover, the pragmatic results of exhaustive experiments were promising while applying our algorithm on diverse QAPLIB instances . Finally, we briefly highlight perspectives for further research

Meta-heuristic combining prior online and offline information for the quadratic assignment problem

The construction of promising solutions for NP-hard combinatorial optimization problems (COPs) in meta-heuristics is usually based on three types of information, namely a priori information, a posteriori information learned from visited solutions during the search procedure, and online information collected in the solution construction process. Prior information reflects our domain knowledge about the COPs. Extensive domain knowledge can surely make the search effective, yet it is not always available. Posterior information could guide the meta-heuristics to globally explore promising search areas, but it lacks local guidance capability. On the contrary, online information can capture local structures, and its application can help exploit the search space. In this paper, we studied the effects of using this information on metaheuristic's algorithmic performances for the COPs. The study was illustrated by a set of heuristic algorithms developed for the quadratic assignment problem. We first proposed an improved scheme to extract online local information, then developed a unified framework under which all types of information can be combined readily. Finally, we studied the benefits of the three types of information to meta-heuristics. Conclusions were drawn from the comprehensive study, which can be used as principles to guide the design of effective meta-heuristic in the future

Optimización metaheurística para la planificación de redes WDM

Las implementaciones actuales de las redes de telecomunicaciones no permiten soportar el incremento en la demanda de ancho de banda producido por el crecimiento del tráfico de datos en las últimas décadas. La aparición de la fibra óptica y el desarrollo de la tecnología de multiplexación por división de longitudes de onda (WDM) permite incrementar la capacidad de redes de telecomunicaciones existentes mientras se minimizan costes. En este trabajo se planifican redes ópticas WDM mediante la resolución de los problemas de Provisión y Conducción en redes WDM (Provisioning and Routing Problem) y de Supervivencia (Survivability Problem). El Problema de Conducción y Provisión consiste en incrementar a mínimo coste la capacidad de una red existente de tal forma que se satisfaga un conjunto de requerimientos de demanda. El problema de supervivencia consiste en garantizar el flujo del tráfico a través de una red en caso de fallo de alguno de los elementos de la misma. Además se resuelve el Problema de Provisión y Conducción en redes WDM con incertidumbre en las demandas. Para estos problemas se proponen modelos de programación lineal entera. Las metaheurísticas proporcionan un medio para resolver problemas de optimización complejos, como los que surgen al planificar redes de telecomunicaciones, obteniendo soluciones de alta calidad en un tiempo computacional razonable. Las metaheurísticas son estrategias que guían y modifican otras heurísticas para obtener soluciones más allá de las generadas usualmente en la búsqueda de optimalidad local. No garantizan que la mejor solución encontrada, cuando se satisfacen los criterios de parada, sea una solución óptima global del problema. Sin embargo, la experimentación de implementaciones metaheurísticas muestra que las estrategias de búsqueda embebidas en tales procedimientos son capaces de encontrar soluciones de alta calidad a problemas difíciles en industria, negocios y ciencia. Para la solución del problema de Provisión y Conducción en Redes WDM, se desarrolla un algoritmo metaheurístico híbrido que combina principalmente ideas de las metaheurísticas Búsqueda Dispersa (Scatter Search) y Búsqueda Mutiarranque (Multistart). Además añade una componente tabú en uno de los procedimiento del algoritmo. Se utiliza el modelo de programación lineal entera propuesto por otros autores y se propone un modelo de programación lineal entera alternativo que proporciona cotas superiores al problema, pero incluye un menor número de variables y restricciones, pudiendo ser resuelto de forma óptima para tamaños de red mayores. Los resultados obtenidos por el algoritmo metaheurístico diseñado se comparan con los obtenidos por un procedimiento basado en permutaciones de las demandas propuesto anteriormente por otros autores, y con los dos modelos de programación lineal entera usados. Se propone modelos de programación lineal entera para sobrevivir la red en caso de fallos en un único enlace. Se proponen modelos para los esquemas de protección de enlace compartido, de camino compartido con enlaces disjuntos, y de camino compartido sin enlaces disjuntos. Se propone un método de resolución metaheurístico que obtiene mejores costes globales que al resolver el problema en dos fases, es decir, al resolver el problema de servicio y a continuación el de supervivencia. Se proponen además modelos de programación entera para resolver el problema de provisión en redes WDM con incertidumbres en las demandas

Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem

Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost

Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem

The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.Publicad