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

A novel artificial bee colony algorithm for shortest path problems with fuzzy arc weights

“…A large number of heuristic algorithms have been proposed in the literature to solve combinatorial optimization problems (Bernardino et al, 2013;Ma et al, 2014). Some of these algorithms are designed to solve the SP problems with different types of fuzzy arc weights.…”

A hybrid artificial bee colony optimizer by combining with life-cycle, Powell’s search and crossover

“…A classical RFID system contains a certain number of tags and readers communicating with each other through wireless RF signals [33][34][35]. The RFID tag stores the unique identification information by integrating a small integrated circuit while the RFID readers communicate with the tags by reading/writing the information stored on them.…”

“…11. In this experiment, these methods that have been adopted for RFID optimizations in the literature [33,34,36] O algorithm proposed by us in [33] as an effective RFID optimization approach is set to be default of their original literature: the number of swarms and swarm size can be set by n = 10, m = 5. The constriction factor is used with v = 0.729, and then the learning rates c1 = c2 = c3 = 1.3667 [6].…”

Introducing a Novel Hybrid Artificial Intelligence Algorithm to Optimize Network of Industrial Applications in Modern Manufacturing

“…However, RNP is a very challenging problem, and the solution has to meet many requirements of the RFID system [8,9]. In general, RNP aims to optimize a set of objectives (coverage, load balance, economic efficiency and interference between antennas, etc.)…”

“…Nowadays, with the developments in computer technology, and software engineering, the conventional trial and error approach has been replaced with modern computational techniques that provide important criteria such as the coverage of objects, collision of antennas, and number of antennas [15]. Computational evolutionary techniques such as Artificial Neural Networks [16], Fuzzy Logic [17], Genetic Algorithms (GA) [10,11,18], particle swarm optimization (PSO) [13,19,20], differential evolution (DE) [9], and hierarchical artificial bee colony algorithm [8] are points of interest for many scientists working with the RNP problem. In this respect, Han and Jie [21] proposed a novel optimization algorithm, namely, the multicommunity GA-PSO, for solving the problem of complicated RNP.…”