Simulated annealing: theory and applications
Simulated annealing: theory and applications
Tabu Search
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Manual and automatic design for UMTS networks
Mobile Networks and Applications
Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem
Mobile Networks and Applications
Radio Network Planning and Optimisation for UMTS
Radio Network Planning and Optimisation for UMTS
Parametric tabu-search for mixed integer programs
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Solving the uncapacitated facility location problem using tabu search
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Planning UMTS base station location: optimization models with power control and algorithms
IEEE Transactions on Wireless Communications
Optimisation of WCDMA radio networks with consideration of link-level performance factors
International Journal of Mobile Network Design and Innovation
Comparison of different meta-heuristics to solve the global planning problem of UMTS networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
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The planning and optimization of WCDMA (wideband code-division multiple access)radio network issues remain vital, and are carried out using static snapshot-based simulation. To improve the accuracy of the static simulation, link-level performance factors, such as the impact of power control, pilot power and soft handover, have to be taken into account. These factors have not been investigated together in the previous works. In this paper, we give a brief introduction to our programming models that take these characteristics into account, and present optimisation strategies based on three major Metaheuristics; genetic algorithms (GA), simulated annealing (SA) and tabu search (TS). Extensive experimental results are provided and the performance of different heuristic algorithms is compared.