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Search Results: 1 - 10 of 11996 matches for " Ben Niu "
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Bacterial Colony Optimization
Ben Niu,Hong Wang
Discrete Dynamics in Nature and Society , 2012, DOI: 10.1155/2012/698057
Abstract: This paper investigates the behaviors at different developmental stages in Escherichia coli (E. coli) lifecycle and developing a new biologically inspired optimization algorithm named bacterial colony optimization (BCO). BCO is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration. A newly created chemotaxis strategy combined with communication mechanism is developed to simplify the bacterial optimization, which is spread over the whole optimization process. However, the other behaviors such as elimination, reproduction, and migration are implemented only when the given conditions are satisfied. Two types of interactive communication schemas: individuals exchange schema and group exchange schema are designed to improve the optimization efficiency. In the simulation studies, a set of 12 benchmark functions belonging to three classes (unimodal, multimodal, and rotated problems) are performed, and the performances of the proposed algorithms are compared with five recent evolutionary algorithms to demonstrate the superiority of BCO. 1. Introduction Swarm intelligence is the emergent collective intelligent behaviors from a large number of autonomous individuals. It provides an alternative way to design novel intelligent algorithms to solve complex real-world problems. Different from conventional computing paradigms [1–3], such algorithms have no constraints of central control, and the searching result of the group will not be affected by individual failures. What is more, swarm intelligent algorithms maintain a population of potential solutions to a problem instead of only one solution. Nowadays, most of swarm intelligent optimization algorithms are inspired by the behavior of animals with higher complexity. Particle swarm optimization (PSO) [4, 5] was gleaned ideas from swarm behavior of bird flocking or fish schooling. Ant colony optimization (ACO) was motivated from the foraging behavior of ants [6, 7]. Artificial fish swarm algorithm (AFSA) was originated in the swarming behavior of fish [8], and artificial bee colony algorithm (ABCA) [9, 10] was stimulated by social specialization behavior of bees. However, the states of the abovementioned animals are more complex, and their behaviors are difficult to describe qualitatively. As prokaryote, bacteria behave in a simple pattern which can be easily described. Inspired by the foraging behavior of Escherichia coli (E. coli) in human intestines, Passion proposed an
A Novel PSO Model Based on Simulating Human Social Communication Behavior
Yanmin Liu,Ben Niu
Discrete Dynamics in Nature and Society , 2012, DOI: 10.1155/2012/791373
Abstract: In order to solve the complicated multimodal problems, this paper presents a variant of particle swarm optimizer (PSO) based on the simulation of the human social communication behavior (HSCPSO). In HSCPSO, each particle initially joins a default number of social circles (SC) that consist of some particles, and its learning exemplars include three parts, namely, its own best experience, the experience of the best performing particle in all SCs, and the experiences of the particles of all SCs it is a member of. The learning strategy takes full advantage of the excellent information of each particle to improve the diversity of the swarm to discourage premature convergence. To weight the effects of the particles on the SCs, the worst performing particles will join more SCs to learn from other particles and the best performing particles will leave SCs to reduce their strong influence on other members. Additionally, to insure the effectiveness of solving multimodal problems, the novel parallel hybrid mutation is proposed to improve the particle’s ability to escape from the local optima. Experiments were conducted on a set of classical benchmark functions, and the results demonstrate the good performance of HSCPSO in escaping from the local optima and solving the complex multimodal problems compared with the other PSO variants. 1. Introduction Particle swarm optimization (PSO), originally introduced by Kennedy and Eberhart [1], has proven to be a powerful competitor to other evolutionary algorithms (e.g., genetic algorithms) [2]. In PSO, these individuals, instead of being manipulated by the evolution operator such as crossover and mutation, are “evolved” by the cooperation and competition among the individuals through generations. Each individual in the swarm is called a particle (a point) with a velocity that is dynamically adjusted in the search process according to its own flying experience and the best experience of the swarm. When solving the unconstraint optimization problem, PSO has empirically turned out to perform well on many optimization problems. However, when it comes to solving complex multimodal problems, PSO may easily get trapped in a local optimum. In order to overcome this defect and improve PSO performance, some researchers proposed several methods [3–20]. In this paper, we present an improved PSO based on human social communication. This strategy ensures the swarm’s diversity against the premature convergence, especially when solving the complex multimodal problems. This paper is organized as follows. Section 2 presents an overview of
Nonresonant Hopf-Hopf bifurcation and a chaotic attractor in neutral functional differential equations
Ben Niu,Weihua Jiang
Mathematics , 2014, DOI: 10.1016/j.jmaa.2012.08.051
Abstract: Nonresonant Hopf-Hopf singularity in neutral functional differential equation (NFDE) is considered. An algorithm for calculating the third-order normal form is established by using the formal adjoint theory, center manifold theorem and the traditional normal form method for RFDE. Van der Pol's equation with extended delay feedback is studied as an example. The unfoldings near the Hopf-Hopf bifurcation point is given by applying this algorithm. Periodic solutions, quasi-periodic solutions are found via theoretical bifurcation diagram and numerical illustrations. The Hopf-Hopf bifurcation diagram indicates the possible existence of a chaotic attractor, which is confirmed by a sequence of simulations.
Layered-resolved autofluorescence imaging of photo-receptors using two-photon excitation  [PDF]
Ling-Ling Zhao, Jun-Le Qu, Dan-Ni Chen, Han-Ben Niu
Journal of Biomedical Science and Engineering (JBiSE) , 2009, DOI: 10.4236/jbise.2009.25052
Abstract: In this paper, we present our investigation on the morphological and autofluorescence char-acteristics of the cones and rods using two- photon excitation with a femtosecond Ti: sap-phire laser. The results show that the micro-structures of the photoreceptor layers can be visualized at submicron level without any stain- ing or slicing. The morphology and spatial dis-tribution of the cones and rods can be resolved by autofluorescence imaging. The autofluores-cence in the photoreceptor outer segments is much stronger than other layers, but suscepti-ble to light-induced damage.
Visual Hand Pose Estimation Based on Hierarchical Temporal Memory in Virtual Reality Cockpit Simulator
Zhou Lai,Gu Hongbin,Niu Ben
Information Technology Journal , 2011,
Abstract: Hand pose estimation is foundation of Human-Computer Interface (HCI) in virtual reality cockpit simulator but it is a challenging problem due to the variation of posture appearance, especially only from single camera. This study proposes a novel visual hand pose estimation method based on Hierarchical Temporal Memory (HTM) which is a biologically inspired model consisting of a hierarchically connected network of nodes. A database containing synthetic images generated by graphics software Pose8 and real images captured by camera is built to train the HTM network. The trained HTM network is used to classify the hand gestures and estimate the wrist parameters of input images. Subsequently, the classification result of HTM is utilized to identify hand motion sequence which is predefined and the finger parameters are acquired by searching the concrete position of input images in the sequence. Experimental results show that the proposed method possesses the characteristic of accurate rendering of the virtual hand applied in HCI and the ability to reconstruct hand postures in a virtual reality cockpit simulator.
Research on a Novel Soft-Switching Buck Converter
Yuanbin Li,Peng Ge,Ben Niu
Research Journal of Applied Sciences, Engineering and Technology , 2013,
Abstract: Based on classical zero voltage transition buck pwm converter, an ideal buck converter with pwm-controlled soft-switching circuit is proposed. The proposed auxiliary circuit allows the main switch to operate with zero-voltage switching. Besides, all of the semiconductor devices operate under soft-switching conditions. Thus, losses were reduced. It was analyzed in detail to demonstrate the operating principle of the novel circuit. Finally, simulation results are given analysis and the simulation results are provided to verify the performance of the proposed buck Converter.
An approach to normal forms of Kuramoto model with distributed delays and the effect of minimal delay
Ben Niu,Yuxiao Guo,Weihua Jiang
Mathematics , 2014, DOI: 10.1016/j.physleta.2015.06.028
Abstract: Heterogeneous delays with positive lower bound (gap) are taken into consideration in Kuramoto oscillators. We first establish a perturbation technique, by which universal normal forms and detailed dynamical behavior of this model can be obtained easily. Theoretically, a hysteresis loop is found near the subcritically bifurcated coherent state on the Ott-Antonsen's manifold. For Gamma distributed delay with fixed variance and mean, we find large gap destroys the loop and significantly increases in the number of coexisted coherent attractors. This result is also explained in the viewpoint of excess kurtosis.
An Adaptive Bacterial Foraging Optimization Algorithm with Lifecycle and Social Learning
Xiaohui Yan,Yunlong Zhu,Hao Zhang,Hanning Chen,Ben Niu
Discrete Dynamics in Nature and Society , 2012, DOI: 10.1155/2012/409478
Abstract: Bacterial Foraging Algorithm (BFO) is a recently proposed swarm intelligence algorithm inspired by the foraging and chemotactic phenomenon of bacteria. However, its optimization ability is not so good compared with other classic algorithms as it has several shortages. This paper presents an improved BFO Algorithm. In the new algorithm, a lifecycle model of bacteria is founded. The bacteria could split, die, or migrate dynamically in the foraging processes, and population size varies as the algorithm runs. Social learning is also introduced so that the bacteria will tumble towards better directions in the chemotactic steps. Besides, adaptive step lengths are employed in chemotaxis. The new algorithm is named BFOLS and it is tested on a set of benchmark functions with dimensions of 2 and 20. Canonical BFO, PSO, and GA algorithms are employed for comparison. Experiment results and statistic analysis show that the BFOLS algorithm offers significant improvements than original BFO algorithm. Particulary with dimension of 20, it has the best performance among the four algorithms. 1. Introduction Swarm intelligence is an innovative optimization technique inspired by the social behaviors of animal swarms in nature. Though the individuals have only simple behaviors and are without centralized control, complex collectiveintelligence could emerge on the level of swarm by their interaction and cooperation. Recent years, several swarm intelligence algorithms have been proposed, such as Ant Colony Optimization (ACO) [1], Particle Swarm Optimization (PSO) [2], Artificial Bee Colony (ABC) ?[3], and Bacterial Foraging Optimization (BFO) BFO algorithm is first proposed by Passino ?[4] in 2002. It is inspired by the foraging and chemotactic behaviors of bacteria, especially the Escherichia coli (E. coli). By smooth running and tumbling, The E. coli can move to the nutrient area and escape from poison area in the environment. The chemotactic is the most attractive behavior of bacteria. It has been studied by many researchers [5, 6]. By simulating the problem as the foraging environment, BFO algorithm and its variants are used for many numerical optimization [7, 8] or engineering optimization problems, such as distributed optimization [9], job shop scheduling [10], image processing? [11], and stock market prediction ?[12]. However, the original BFO has some shortages: dispersal, reproduction, and elimination each happens; after a certain amount of chemotaxis operations. The appropriate time and method for dispersal and reproduction are important. Otherwise, the stability of
Vehicle Routing Problem with Time Windows and Simultaneous Delivery and Pick-Up Service Based on MCPSO
Xiaobing Gan,Yan Wang,Shuhai Li,Ben Niu
Mathematical Problems in Engineering , 2012, DOI: 10.1155/2012/104279
Abstract: This paper considers two additional factors of the widely researched vehicle routing problem with time windows (VRPTW). The two factors, which are very common characteristics in realworld, are uncertain number of vehicles and simultaneous delivery and pick-up service. Using minimization of the total transport costs as the objective of the extension VRPTW, a mathematic model is constructed. To solve the problem, an efficient multiswarm cooperative particle swarm optimization (MCPSO) algorithm is applied. And a new encoding method is proposed for the extension VRPTW. Finally, comparing with genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, the MCPSO algorithm performs best for solving this problem.
Development and Application of Integrated Optical Sensors for Intense E-Field Measurement
Rong Zeng,Bo Wang,Ben Niu,Zhanqing Yu
Sensors , 2012, DOI: 10.3390/s120811406
Abstract: The measurement of intense E-fields is a fundamental need in various research areas. Integrated optical E-field sensors (IOESs) have important advantages and are potentially suitable for intense E-field detection. This paper comprehensively reviews the development and applications of several types of IOESs over the last 30 years, including the Mach-Zehnder interferometer (MZI), coupler interferometer (CI) and common path interferometer (CPI). The features of the different types of IOESs are compared, showing that the MZI has higher sensitivity, the CI has a controllable optical bias, and the CPI has better temperature stability. More specifically, the improvement work of applying IOESs to intense E-field measurement is illustrated. Finally, typical uses of IOESs in the measurement of intense E-fields are demonstrated, including application areas such as E-fields with different frequency ranges in high-voltage engineering, simulated nuclear electromagnetic pulse in high-power electromagnetic pulses, and ion-accelerating field in high-energy physics.
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