Simulated annealing algorithms for continuous global optimization: convergence conditions
Journal of Optimization Theory and Applications
New Sequential and Parallel Derivative-Free Algorithms for Unconstrained Minimization
SIAM Journal on Optimization
Pattern Search Algorithms for Bound Constrained Minimization
SIAM Journal on Optimization
A Combined Global & Local Search (CGLS) Approach to Global Optimization
Journal of Global Optimization
On the deployment of picocellular wireless infrastructure
IEEE Wireless Communications
Optimal location of transmitters for micro-cellular radio communication system design
IEEE Journal on Selected Areas in Communications
Optimising mobile base station placement using an enhanced Multi-Objective Genetic Algorithm
International Journal of Business Intelligence and Data Mining
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In this paper, we study the application of non-monotone derivative-free optimization algorithms to wireless local area networks (WLAN) planning, which can be modeled as an unconstrained minimization problem. We wish to determine the access point (AP) positions that maximize coverage in order to provide connectivity to static and mobile users. As the objective function of the optimization model is not everywhere differentiable, previous research has discarded gradient methods and employed heuristics such as neighborhood search (NS) and simulated annealing (SA). In this paper, we show that the model fulfills the conditions required by recently proposed non-monotone derivative-free (DF) algorithms. Unlike SA, DF has guaranteed convergence. The numerical tests reveal that a tailored DF implementation (termed "zone search") outperforms NS and SA.