Maowei He

Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. 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 operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

A Butterfly-Shaped Wideband Microstrip Patch Antenna for Wireless Communication

A novel butterfly-shaped patch antenna for wireless communication is introduced in this paper. The antenna is designed for wideband wireless communications and radio-frequency identification (RFID) systems. Two symmetrical quasi-circular arms and two symmetrical round holes are incorporated into the patch of a microstrip antenna to expand its bandwidth. The diameter and position of the circular slots are optimized to achieve a wide bandwidth. The validity of the design concept is demonstrated by means of a prototype having a bandwidth of about 40.1%. The return loss of the butterfly-shaped antenna is greater than 10 dB between 4.15 and 6.36 GHz. The antenna can serve simultaneously most of the modern wireless communication standards.

Artificial Bee Colony Optimizer Based on Bee Life-Cycle for Stationary and Dynamic Optimization

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms.

A Novel Plant Root Foraging Algorithm for Image Segmentation Problems

This paper presents a new type of biologically-inspired global optimization methodology for image segmentation based on plant root foraging behavior, namely, artificial root foraging algorithm (ARFO). The essential motive of ARFO is to imitate the significant characteristics of plant root foraging behavior including branching, regrowing, and tropisms for constructing a heuristic algorithm for multidimensional and multimodal problems. A mathematical model is firstly designed to abstract various plant root foraging patterns. Then, the basic process of ARFO algorithm derived in the model is described in details. When tested against ten benchmark functions, ARFO shows the superiority to other state-of-the-art algorithms on several benchmark functions. Further, we employed the ARFO algorithm to deal with multilevel threshold image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the suitability of the proposed method for solving such problem.

Hierarchical Artificial Bee Colony Optimizer with Divide-and-Conquer and Crossover for Multilevel Threshold Image Segmentation

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the original ABC method in parallel to part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results for comparing HABC with several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.

Drop-on-Demand Inkjet Printhead Performance Enhancement by Dynamic Lumped Element Modeling for Printable Electronics Fabrication

The major challenge in printable electronics fabrication is the print resolution and accuracy. In this paper, the dynamic lumped element model (DLEM) is proposed to directly simulate an inkjet-printed nanosilver droplet formation process and used for predictively controlling jetting characteristics. The static lumped element model (LEM) previously developed by the authors is extended to dynamic model with time-varying equivalent circuits to characterize nonlinear behaviors of piezoelectric printhead. The model is then used to investigate how performance of the piezoelectric ceramic actuator influences jetting characteristics of nanosilver ink. Finally, the proposed DLEM is applied to predict the printing quality using nanosilver ink. Experimental results show that, compared to other analytic models, the proposed DLEM has a simpler structure with the sufficient simulation and prediction accuracy.

Droplet property optimization in printable electronics fabrication using root system growth algorithm

Abstract For printable electronics fabrication, the printing quality delivered by a Drop-on-Demand (DoD) Piezoelectric Inkjet (PIJ) Printhead is limited due to the residual vibration problem in the ink channel. The residual vibration after jetting the ink drop influences the droplet velocity and volume consistency, and limits the maximum jetting frequency of the printhead. In order to meet the challenging requirements of printable electronics fabrication, this work proposes a novel Optimization framework for optimal parameter setting of the high-frequency driving waveform. A novel Root System Growth Algorithm (RSGA), based on principles from plant root growth and foraging behaviors, is chosen as the core optimization algorithm of the framework. The proposed RSGA is benchmarked against other four state-of-the-art bio-inspired algorithms using CEC2005 test function suite. Then several groups of experiment results for various targets are presented to demonstrate the universality of the proposed optimization-based search system for the improvement of printing quality. Simulation results show that the RSGA has an outstanding performance in searching the driving waveform parameters for specified droplet properties.

Multispecies Coevolution Particle Swarm Optimization Based on Previous Search History

A hybrid coevolution particle swarm optimization algorithm with dynamic multispecies strategy based on -means clustering and nonrevisit strategy based on Binary Space Partitioning fitness tree (called MCPSO-PSH) is proposed. Previous search history memorized into the Binary Space Partitioning fitness tree can effectively restrain the individuals’ revisit phenomenon. The whole population is partitioned into several subspecies and cooperative coevolution is realized by an information communication mechanism between subspecies, which can enhance the global search ability of particles and avoid premature convergence to local optimum. To demonstrate the power of the method, comparisons between the proposed algorithm and state-of-the-art algorithms are grouped into two categories: 10 basic benchmark functions (10-dimensional and 30-dimensional), 10 CEC2005 benchmark functions (30-dimensional), and a real-world problem (multilevel image segmentation problems). Experimental results show that MCPSO-PSH displays a competitive performance compared to the other swarm-based or evolutionary algorithms in terms of solution accuracy and statistical tests.

An on-line detection method for outliers of dynamic unstable measurement data

Aiming at the characteristics of the vibration data collected by the regulation system during the unstable regulation process and the deficiency of the traditional wavelet anomaly detection method, an on-line anomaly detection method combining the autoregressive and the wavelet analysis is proposed to detect the abnormal data of the regulation system. By introducing the improved robust AR model, this method can overcome the problem that the time and frequency of traditional anomaly detection using wavelet analysis method cannot be well balanced, and ensure the rationality of abnormal value detection of process data. Considering the general parameters of the regulation system is time-varying and has strong dynamic characteristics, the method proposed in this paper has the ability of online detection and real-time updating of parameters to ensure that the control parameters of time-varying control system; In order to avoid the problem that the traditional anomaly detection method needs to set the detection threshold in advance, HMM is introduced to analyze the wavelet coefficients and update the HMM parameters online, which can ensure that the HMM can well reflect the actual distribution of process data anomalies. It is proved that the method of anomaly data detection proposed in this paper is more suitable for the unstable regulation process data and has certain practicability through experiment and application.

Adaptive Bacterial Foraging Algorithm and Its Application in Mobile Robot Path Planning

This work considered the utilization of biomimicry of bacterial foraging strategy to develop an adaptive control strategy for mobile robot, and proposed a bacterial foraging approach for robot path planning. In the proposed model, robot that mimics the behavior of bacteria is able to determine an optimal collision-free path between a start and a target point in the environment surrounded by obstacles. In the simulation studies, a test scenario of static environment with different number obstacles is adopted to evaluate the performance of the proposed method. Simulation results show that the robot which reflects the bacterial foraging behavior can adapt to complex environments in the planned trajectories with both satisfactory accuracy and stability.