Zhenjie Wang

A novel hybrid algorithm of gravitational search algorithm with genetic algorithm for multi-level thresholding

Graphical abstractDisplay Omitted The multi-level thresholding is a popular method for image segmentation. However, the method is computationally expensive and suffers from premature convergence when level increases. To solve the two problems, this paper presents an advanced version of gravitational search algorithm (GSA), namely hybrid algorithm of GSA with genetic algorithm (GA) (GSA-GA) for multi-level thresholding. In GSA-GA, when premature convergence occurred, the roulette selection and discrete mutation operators of GA are introduced to diversify the population and escape from premature convergence. The introduction of these operators therefore promotes GSA-GA to perform faster and more accurate multi-level image thresholding. In this paper, two common criteria (1) entropy and (2) between-class variance were utilized as fitness functions. Experiments have been performed on six test images using various numbers of thresholds. The experimental results were compared with standard GSA and three state-of-art GSA variants. Comparison results showed that the GSA-GA produced superior or comparative segmentation accuracy in both entropy and between-class variance criteria. Moreover, the statistical significance test demonstrated that GSA-GA significantly reduce the computational complexity for all of the tested images.

A Hybrid Genetic Algorithm and Gravitational Search Algorithm for Global Optimization

The laws of gravity and mass interactions inspire the gravitational search algorithm (GSA), which nds optimal regions of complex search spaces through the interaction of individuals in a population of particles. Although GSA has proven effective in both science and engineering, it is still easy to suffer from premature convergence especially facing complex problems. In this paper, we pro- posed a new hybrid algorithm by integrating genetic algorithm (GA) and GSA (GA-GSA) to avoid premature convergence and to improve the search ability of GSA. In GA-GSA, crossover and mutation operators are introduced from GA to GSA for jumping out of the local optima. To demonstrate the search ability of the proposed GA-GSA, 23 complex benchmark test functions were employed, including unimodal and multimodal high-dimensional test functions as well as multimodal test functions with xed dimensions. Wilcoxon signed-rank tests were also utilized to execute statistical analysis of the results obtained by PSO, GSA, and GA-GSA. Experimental results demonstrated that the proposed algorithm is both efficient and effective.

Combinational Build-Up Index (CBI) for Effective Impervious Surface Mapping in Urban Areas

The distribution of urban impervious surface is a significant indicator of the degree of urbanization, as well as a major indicator of environmental quality. Hence, taking advantage of remotely sensed imagery to map impervious surface has become an important topic. Spectral indices have been developed due to its convenience to apply, among which feature extraction approach has shown superiority in reliability and applicability. However, impervious surface is often confused with bare soil when the current existing indices are used as well as their sensor-specific limitations. In this study, a new index, combinational build-up index (CBI), is proposed to extract impervious surface. The new index combines the first component of a principal component analysis (PC1), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI), representing high albedo, low albedo, and vegetation, respectively, to reduce the original bands into three thematic-oriented features. The new index was tested using various remote sensing images at different spectral and spatial resolutions. Qualitative and quantitative assessments of the accuracy and separability of CBI, together with the comparison with other existing indices, were performed. The result of this study indicates that the proposed method is able to serve as an effective impervious index and can be applied widely.

Locally informed gravitational search algorithm

Gravitational search algorithm (GSA) has been successfully applied to many scientific and engineering applications in the past few years. In the original GSA and most of its variants, every agent learns from all the agents stored in the same elite group, namely Kbest. This type of learning strategy is in nature a fully-informed learning strategy, in which every agent has exactly the same global neighborhood topology structure. Obviously, the learning strategy overlooks the impact of environmental heterogeneity on individual behavior, which easily resulting in premature convergence and high runtime consuming. To tackle these problems, we take individual heterogeneity into account and propose a locally informed GSA (LIGSA) in this paper. To be specific, in LIGSA, each agent learns from its unique neighborhood formed by k local neighbors and the historically global best agent rather than from just the single Kbest elite group. Learning from the k local neighbors promotes LIGSA fully and quickly explores the search space as well as effectively prevents premature convergence while the guidance of global best agent can accelerate the convergence speed of LIGSA. The proposed LIGSA has been extensively evaluated on 30 CEC2014 benchmark functions with different dimensions. Experimental results reveal that LIGSA remarkably outperforms the compared algorithms in solution quality and convergence speed in general.

DMMOGSA: Diversity-enhanced and memory-based multi-objective gravitational search algorithm

Multi-objective optimization (MOO) is an important research topic in both science and engineering. This paper proposes a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA). We combine the memory of the best states of individual particles and their population in their evolution paths and the gravitational rules to construct a new search strategy. Under this strategy, the position and mass states of each particle are updated based on the memory associated with it and the current states of all particles in the current population in terms of their gravitational forces on it. A novel diversity-enhancement mechanism is also employed to control the velocity of each particle for traveling to a new position. Experiments were conducted on 12 well-known benchmark functions, and for each function the results of DMMOGSA were compared with those of SPEA2, NSGA-II and MOPSO. Our results show that DMMOGSA can reduce the effect of premature convergence and achieve more reliable performance on most of the tested cases.

Characteristics of equatorial plasma bubble zonal drift velocity and tilt based on Hong Kong GPS CORS network: From 2001 to 2012

Hong Kong (22.3°N, 114.2°E, dip: 30.5°N; geomagnetic 15.7°N, 173.4°W, declination: 2.7°W) is a low-latitude area, and the Hong Kong Continuously Operating Reference Station (CORS) network has been developed and maintained by Lands Department of Hong Kong government since 2001. Based on the collected GPS observations of a whole solar cycle from 2001 to 2012, a method is proposed to estimate the zonal drift velocity as well as the tilt of the observed plasma bubbles, and the estimated results are statistically analyzed. It is found that although the plasma bubbles are basically vertical within the equatorial plane, the tilt can be as big as more than 60° eastward or westward sometimes. And, the tilt and the zonal drift velocity are correlated. When the velocity is large, the tilt is also large generally. Another finding is that large velocity and tilt generally occur in spring and autumn and in solar active years.

Geometry-free and non-geometry-free testing quantities for cycle slip detection and correction in case of strong atmospheric variations with static observations

Abstract Cycle slip detection and correction is an important part of GPS data processing. In the past research, geometry-free and non-geometry-free testing quantities have been proposed. Both of them can be used for static cases. Each of them has their own advantages and disadvantages. This research is aiming to compare these two kinds of testing quantities for static cycle slip detection and correction in different situations, such as in low and high elevation angles, especially in cases of strong atmospheric variations. The performance of geometry-free and non-geometry-free testing quantities is compared with observations of different situations. The numerical results show that the effect of the rapid change of relative humidity on cycle slip detection and correction with these two kinds of testing quantity is not obvious. However, the results clearly show that in the case of a low elevation angle (<10°), non-geometry-free is obviously a better choice. In the case of ionospheric scintillation, geometry-free testing quantity can no longer be used. With non-geometry-free testing quantity, cycle slips can be detected and corrected successfully, if the number of continuous cycle slips is small for example, less than 5.

Grayscale Image Segmentation Using Multilevel Thresholding and Nature-Inspired Algorithms

Multilevel image thresholding plays a crucial role in analyzing and interpreting the digital images. Previous studies revealed that classical exhaustive search techniques are time consuming as the number of thresholds increased. To solve the problem, many nature-inspired algorithms (NAs) which can produce high-quality solutions in reasonable time have been utilized for multilevel thresholding. This chapter discusses three typical kinds of NAs and their hybridizations in solving multilevel image thresholding. Accordingly, a novel hybrid algorithm of gravitational search algorithm (GSA) with genetic algorithm (GA), named GSA-GA, is proposed to explore optimal threshold values efficiently. The chosen objective functions in this chapter are Kapur’s entropy and Otsu criteria. This chapter conducted experiments on two well-known test images and two real satellite images using various numbers of thresholds to evaluate the performance of different NAs.

Kinematic cycle slip detection and correction for carrier phase based navigation applications in urban environment in case of ultra high rate GNSS observations

Abstract In recent years, GNSS has been more and more widely used for all kinds of applications, including those in urban environment. Carrier phase observations from GNSS are necessary for precise navigation applications. However, before that, cycle slip has to be detected and corrected. For static GPS observations, as the distance between the receiver and the GPS satellites is smooth, the testing quantities formed with carrier phase observations between satellites can be used for cycle slip detection and correction. These testing quantities have two advantages. First, as only carrier phase observations are used, they are not affected by big noise of code observations. Second, the wavelength of the testing quantities is about 20 cm, long enough to be insensitive to carrier phase noise and multipath. However, generally these testing quantities cannot be used for kinematic observations with a sample interval of 1 s as the moving of the receiver and the distance between the receiver and the satellites is not smooth. Kinematic cycle slip detection and correction has been a challenge for many years. Currently, two methods are popularly used: geometry-free and time relative. Both of these two methods are sensitive to observation noise and multipath of carrier phase and code, especially the latter one. For carrier phase based applications in urban environment, this weakness will become more outstanding. For kinematic ultra high rate observations, the changes of the speed and acceleration of the receiver can be neglected in very short time such as 1 s or less if there is no abrupt movement. In this case, the distance between the receiver and the satellites can be regarded as smooth and the testing quantities formed between satellites can be used for cycle slip detection and correction. Based on this, in this paper, a new kinematic cycle slip detection and correction method is proposed, aiming at navigation applications in urban environment with ultra high rate GPS observations. The new method has three features: first, it is based on the use of ultra high rate observations (20 Hz); second, the speed and acceleration change of the vehicle is neglected; third, code measurement is not involved. The new method is tested with practical ultra high rate GPS observations in urban environment and compared with ionospheric residual method and time relative method. The numerical results show that the new method performs obviously better than the others with all cycle slips detected and determined reliably.

Partial GNSS ambiguity resolution in coordinate domain

Traditionally, if full ambiguity resolution is not successful, partial ambiguity resolution (PAR) will be tried. However, identifying which subset of ambiguities to fix is not easy and is still an open problem. Since the actual purpose of most applications is positioning, rather than fixing all or part of the ambiguities, in this research, we are trying to bypass the problem of identifying which subset of ambiguities to fix and provide a partial solution in the coordinate domain for the bias-free case. The basic idea is that with a user-defined failure rate, we can find a group of ambiguity candidates and each will provide one position. The partial solution is constructed based on these positions together with an indicator to show its maximum positioning error with user-defined reliability. In order to meet various user requirements, different kinds of partial solutions in coordinate domain are proposed. Different from the traditional PAR methods, the new method still works with all the ambiguities (i.e. the complete vector), but works with the different possible values that the complete ambiguity vector may take. The validness and applicability of the proposed partial solution are demonstrated-based practical BeiDou triple-frequency observations. Numerical results show that some partial solutions can be more accurate, while others can meet higher reliability or integrity requirement.