RFID network planning using a multi-swarm optimizer

Cooperative artificial bee colony algorithm for multi-objective RFID network planning

“…This results in an unacceptable level of misreads. The main feature of our approach is that the interference is not solved by traditional ways, such as frequency assignment (Engels and Sarma, 2002) and reader scheduling (Chen et al, 2011), but in a more precautionary way. This objective function is formulated as follows:…”

“…This gives rise to some challenging issues in the deployment of an RFID network, such as optimal tag coverage, quality of service (QoS), and cost efficiency. Therefore, our previous pioneering work pointed out that the RFID network planning (RNP) problem in an RFID system is a key issue that has to meet many requirements of the RFID system in order to operate the large-scale network of RFID readers in an optimal fashion (Chen et al, 2011). In general, we defined that the RNP aims to optimize a set of objectives (coverage, load balance, economic efficiency, interference between readers, etc.)…”

“…Obviously, optimization of RNP is essentially a typical multiobjective problem (MOP), which is a high-dimensional NP-hard problem with a large number of variables and uncertain parameters (Coello Coello et al, 2002;Chen et al, 2011). However, the methods used in the previous studies to solve the multi-objective RNP (MORNP) are always weighted coefficient approaches used to transform multiple objectives into a single objective (Guan et al, 2006;Chen et al, 2011;Seok et al, 2010;Gong et al, 2012;Karaboga and Akay, 2009).…”

“…However, the methods used in the previous studies to solve the multi-objective RNP (MORNP) are always weighted coefficient approaches used to transform multiple objectives into a single objective (Guan et al, 2006;Chen et al, 2011;Seok et al, 2010;Gong et al, 2012;Karaboga and Akay, 2009). Most of them are based on evolutionary algorithms (EAs) and swarm intelligence (SI) optimization techniques, such as genetic algorithms (GA) (Guan et al, 2006; Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/jnca Yang et al, 2009), evolutionary strategy (ES) (Bhattacharya and Roy, 2010), differential evolution (DE) (Seok et al, 2010), and particle swarm optimization (PSO) (Chen et al, 2011;Bhattacharya and Roy, 2010;Gong et al, 2012). Notice that these works considered only one object in RFID network planning or a single objective function linearly composed of several planning objectives, and none of them can generate the tradeoffs between objectives.…”

Bacterial colony foraging optimization

“…In the light of the analogy between bacterial quorum sensing and swarming pattern of PSO model [32], a novel principle of cell-to-cell communication for bacterial foraging algorithm is introduced.…”