A novel Grouping Coral Reefs Optimization algorithm for optimal mobile network deployment problems under electromagnetic pollution and capacity control criteria. (15th August 2016)

• Record Type: Journal Article
• Authors: Salcedo-Sanz, Sancho; García-Díaz, Pilar; Del Ser, Javier; Bilbao, Miren Nekane; Portilla-Figueras, José Antonio
• Is Part Of: Expert systems with applications. Volume 55(2016)
• Page Start: 388
• Page End: 402
• Publication Date: 2016-08-15
• Subjects: Coral Reefs Optimization -- Mobile network deployment -- Grouping-based heuristics -- Electromagnetic pollution minimization -- BTS capacity; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.02.032
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new grouping Coral Reefs Optimization algorithm is presented. A problem of mobile network deployment with pollution control is tackled. A real case-study in Alcalá de Henares (Madrid) is discussed. Comparison with state of the art algorithms shows excellent performance of the proposed algorithm. Abstract: This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain.