Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system

Recommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-based collaborative filtering. In this paper, we follow this technique by proposing a new algorithm which is called hybrid crow search and uniform crossover algorithm (HCSUC) to find a set of feasible clusters of similar users to enhance the recommendation process. Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima. The top-N recommendations are presented for the corresponding user according to the most feasible cluster’s members. The performance of the HCSUC algorithm is evaluated using the Jester dataset. A set of experiments have been conducted to validate the solution quality and accuracy of the HCSUC algorithm against the standard particle swarm optimization (PSO), African buffalo optimization (ABO), and the crow search algorithm (CSA). In addition, the proposed algorithm and the other meta-heuristic algorithms are compared against the collaborative filtering recommendation technique (CF). The results indicate that the HCSUC algorithm has obtained superior results in terms of mean absolute error, root means square errors and in minimization of the objective function.

Diseño y optimización de redes de distribución de agua utilizando algoritmos genéticos

El diseño óptimo de una red de distribución de agua consiste en minimizar el costo de la red cumpliendo ciertas restricciones: presión mínima, caudal, velocidad del agua etc. Los algoritmos genéticos (AGs.) han demostrado ser métodos eficaces en cuanto a buena aproximación, calidad y tiempo de cómputo, al resolver este tipo problemas de optimización que, por sus características, pertenece a un problema NP-Duro. El diseño de una red de distribución de agua se rige por ecuaciones lineales y no lineales que se resuelven por el método de Newton-Raphson. Para resolver este problema se han utilizado los siguientes AGs: Crossover elitism population, Half uniform crossover combination, Cataclysm mutation (CHC) y el Algoritmo Genético canónico, aplicados a redes clásicas de distribución de aguas como la red de Alperovits y Shamir y la red de Nueva York. Se obtuvieron resultados satisfactorios y de gran calidad, en ambos algoritmos propuestos.