Yunlong Zhu

MCPSO: A multi-swarm cooperative particle swarm optimizer

This paper presents a new optimization algorithm - MCPSO, multi-swarm cooperative particle swarm optimizer, inspired by the phenomenon of symbiosis in natural ecosystems. MCPSO is based on a master-slave model, in which a population consists of one master swarm and several slave swarms. The slave swarms execute a single PSO or its variants independently to maintain the diversity of particles, while the master swarm evolves based on its own knowledge and also the knowledge of the slave swarms. According to the co-evolutionary relationship between master swarm and slave swarms, two versions of MCPSO are proposed, namely the competitive version of MCPSO (COM-MCPSO) and the collaborative version of MCPSO (COL-MCPSO), where the master swarm enhances its particles based on an antagonistic scenario or a synergistic scenario, respectively. In the simulation studies, several benchmark functions are performed, and the performances of the proposed algorithms are compared with the standard PSO (SPSO) and its variants to demonstrate the superiority of MCPSO.

A new approach for data clustering using hybrid artificial bee colony algorithm

Data clustering is a popular data analysis technique needed in many fields. Recent years, some swarm intelligence-based approaches for clustering were proposed and achieved encouraging results. This paper presents a Hybrid Artificial Bee Colony (HABC) algorithm for data clustering. The incentive mechanism of HABC is enhancing the information exchange (social learning) between bees by introducing the crossover operator of Genetic Algorithm (GA) to ABC. With a test on ten benchmark functions, the proposed HABC algorithm is proved to have significant improvement over canonical ABC and several other comparison algorithms. The HABC algorithm is then employed for data clustering. Six real datasets selected from the UCI machine learning repository are used. The results show that the HABC algorithm achieved better results than other algorithms and is a competitive approach for data clustering.

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.

A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

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.

Multi-population cooperative particle swarm optimization

Inspired by the phenomenon of symbiosis in natural ecosystem, a master-slave mode is incorporated into Particle Swarm Optimization (PSO), and a Multi-population Cooperative Optimization (MCPSO) is thus presented. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute PSO (or its variants) independently to maintain the diversity of particles, while the master swarm enhances its particles based on its own knowledge and also the knowledge of the particles in the slave swarms. In the simulation part, several benchmark functions are performed, and the performance of the proposed algorithm is compared to the standard PSO (SPSO) to demonstrate its efficiency.

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.

A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing

Inspired by the phenomenon of symbiosis in natural ecosystems a multi-swarm cooperative particle swarm optimizer (MCPSO) is proposed as a new fuzzy modeling strategy for identification and control of non-linear dynamical systems. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute particle swarm optimization (PSO) or its variants independently to maintain the diversity of particles, while the particles in the master swarm enhance themselves based on their own knowledge and also the knowledge of the particles in the slave swarms. With four benchmark functions, MCPSO is proved to have better performance than PSO and its variants. MCPSO is then used to automatically design the fuzzy identifier and fuzzy controller for non-linear dynamical systems. The proposed algorithm (MCPSO) is shown to outperform PSO and some other methods in identifying and controlling dynamical systems.