Kunyuan Hu

Original research paper: Multi-colony bacteria foraging optimization with cell-to-cell communication for RFID network planning

In order to obtain accurate and reliable network planning in the Radio Frequency Identification (RFID) communication system, the locations of readers and the associated values for each of the reader parameters have to be determined. All these choices must optimize a set of objectives, such as tag coverage, economic efficiency, load balance, and interference level between readers. This paper proposes a novel optimization algorithm, namely the multi-colony bacteria foraging optimization (MC-BFO), to solve complex RFID network planning problem. The main idea of MC-BFO is to extend the single population bacterial foraging algorithm to the interacting multi-colony model by relating the chemotactic behavior of single bacterial cell to the cell-to-cell communication of bacterial community. With this multi-colony cooperative approach, a suitable diversity in the whole bacterial community can be maintained. At the same time, the cell-to-cell communication mechanism significantly speeds up the bacterial community to converge to the global optimum. Then a mathematical model for planning RFID networks is developed based on the proposed MC-BFO. The performance of MC-BFO is compared to both GA and PSO on RFID network planning problem, demonstrating its superiority.

RFID network planning using a multi-swarm optimizer

In this paper, we develop an optimization model for planning the positions of readers in the RFID network based on a novel Multi-swarm Particle Swarm Optimizer called PS2O. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. This algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. Simulation results show that the proposed PS2O algorithm proves to be superior for planning RFID networks than the standard PSO and other two evolutionary algorithms, namely Genetic Algorithm (GA) and Evolution Strategy (ES), in terms of optimization accuracy and computation robustness.

Cooperative Bacterial Foraging Optimization

Bacterial Foraging Optimization (BFO) is a novel optimization algorithm based on the social foraging behavior of E. coli bacteria. This paper presents a variation on the original BFO algorithm, namely, the Cooperative Bacterial Foraging Optimization (CBFO), which significantly improve the original BFO in solving complex optimization problems. This significant improvement is achieved by applying two cooperative approaches to the original BFO, namely, the serial heterogeneous cooperation on the implicit space decomposition level and the serial heterogeneous cooperation on the hybrid space decomposition level. The experiments compare the performance of two CBFO variants with the original BFO, the standard PSO and a real-coded GA on four widely used benchmark functions. The new method shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

Adaptive Bacterial Foraging Optimization

Bacterial Foraging Optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization (ABFO), employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization (PSO) and a real-coded genetic algorithm (GA) on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.

Self-adaptation in Bacterial Foraging Optimization algorithm

Bacterial foraging optimization (BFO) is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques like genetic algorithm (GA) and particle swarm optimization (PSO). This paper first analyzes how the run-length unit parameter controls the exploration and exploitation ability of BFO, and then presents a variation on the original BFO algorithm, called the self-adaptive bacterial foraging optimization (SA-BFO), employing the adaptive search strategy to significantly improve the performance of the original algorithm. This is achieved by enabling SA-BFO to adjust the run-length unit parameter dynamically during evolution to balance the exploration/exploitation tradeoff. Application of SA-BFO on several benchmark functions shows a marked improvement in performance over the original BFO.

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

Radio frequency identification (RFID) is rapidly growing into an important technology for object identification and tracking applications. This gives rise to the most challenging RFID network planning (RNP) problem in the large-scale RFID deployment environment. RNP has been proven to be an NP-hard problem that involves many objectives and constraints. The application of evolutionary and swarm intelligence algorithms for solving multi-objective RNP (MORNP) has gained significant attention in the literature, while these proposed methods always transform multi-objective RNP into single-objective problem by the weighted coefficient approach. In this work, we propose a cooperative multi-objective artificial colony algorithm called CMOABC to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP. The experiment presents an exhaustive comparison of the proposed CMOABC and two successful multi-objective techniques, namely the recently developed multi-objective artificial bee colony algorithm (MOABC) and nondominated sorting genetic algorithm II (NSGA-II), on instances of different nature, namely the two-objective and three-objective MORNP in the large-scale RFID scenario. Simulation results show that CMOABC proves to be superior for planning RFID networks compared to NSGA-II and MOABC in terms of optimization accuracy and computation robustness.

Mining frequent trajectory pattern based on vague space partition

Frequent trajectory pattern mining is an important spatiotemporal data mining problem with broad applications. However, it is also a difficult problem due to the approximate nature of spatial trajectory locations. Most of the previously developed frequent trajectory pattern mining methods explore a crisp space partition approach 8,10] to alleviate the spatial approximation concern. However, this approach may cause the sharp boundary problem that spatially close trajectory locations may fall into different partitioned regions, and eventually result in failure of finding meaningful trajectory patterns. In this paper, we propose a flexible vague space partition approach to solve the sharp boundary problem. In this approach, the spatial plane is divided into a set of vague grid cells, and trajectory locations are transformed into neighboring vague grid cells by a distance-based membership function. Based on two classical sequential mining algorithms, the PrefixSpan and GSP algorithms, we propose two efficient trajectory pattern mining algorithms, called VTPM-PrefixSpan and VTPM-GSP, to mine the transformed trajectory sequences with time interval constraints. A comprehensive performance study on both synthetic and real datasets shows that the VTPM-PrefixSpan algorithm outperforms the VTPM-GSP algorithm in both effectiveness and scalability.

Discrete and continuous optimization based on multi-swarm coevolution

This paper presents a novel Multi-swarm Particle Swarm Optimizer called PS2O, which is inspired by the coevolution of symbiotic species in natural ecosystems. The main idea of PS2O is to extend the single population PSO to the interacting multi-swarms model by constructing hierarchical interaction topology and enhanced dynamical update equations. With the hierarchical interaction topology, a suitable diversity in the whole population can be maintained. At the same time, the enhanced dynamical update rule significantly speeds up the multi-swarm to converge to the global optimum. The PS2O algorithm, which is conceptually simple and easy to implement, has considerable potential for solving complex optimization problems. With a set of 17 mathematical benchmark functions (including both continuous and discrete cases), PS2O is proved to have significantly better performance than four other successful variants of PSO.

Hierarchical Swarm Model: A New Approach to Optimization

This paper presents a novel optimization model called hierarchical swarm optimization (HSO), which simulates the natural hierarchical complex system from where more complex intelligence can emerge for complex problems solving. This proposed model is intended to suggest ways that the performance of HSO-based algorithms on complex optimization problems can be significantly improved. This performance improvement is obtained by constructing the HSO hierarchies, which means that an agent in a higher level swarm can be composed of swarms of other agents from lower level and different swarms of different levels evolve on different spatiotemporal scale. A novel optimization algorithm (named PS2O), based on the HSO model, is instantiated and tested to illustrate the ideas of HSO model clearly. Experiments were conducted on a set of 17 benchmark optimization problems including both continuous and discrete cases. The results demonstrate remarkable performance of the PS2O algorithm on all chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms.

Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.