Applying adaptive swarm intelligence technology with structuration in web-based collaborative learning

One of the key challenges in the promotion of web-based learning is the development of effective collaborative learning environments. We posit that the structuration process strongly influences the effectiveness of technology used in web-based collaborative learning activities. In this paper, we propose an ant swarm collaborative learning (ASCL) environment based on a swarm intelligence system (SIS) that structures opportunities for effective collaboration and learning in a dynamic way. The results of our experiments indicate that: (1) the self-organizing behavior of SIS is positively associated with system appropriation; (2) the multi-agent-based mechanism of SIS is positively associated with system appropriation; (3) the cohesive capability of SIS is positively associated with system appropriation; and (4) the learner’s tendency toward system appropriation is positively associated with learning effectiveness. Our findings also show that learners in an ASCL environment outperform their counterparts in a general web-based learning (GWL) environment. We conclude that different types of technological support can influence the achievement in a web-based learning environment.     

An adaptive,secure and imperceptive image watermarking using swarm intelligence,Arnold transform,SVD and DWT

Multimedia Tools and Applications - Digital image watermarking is one of the important method of copyright protection and rightful ownership of the digital image and video. In this paper, the...     

Analyzing collective behavior from blogs using swarm intelligence

With the rapid growth of the availability and popularity of interpersonal and behavior-rich resources such as blogs and other social media avenues, emerging opportunities and challenges arise as people now can, and do, actively use computational intelligence to seek out and understand the opinions of others. The study of collective behavior of individuals has implications to business intelligence, predictive analytics, customer relationship management, and examining online collective action as manifested by various flash mobs, the Arab Spring (2011) and other such events. In this article, we introduce a nature-inspired theory to model collective behavior from the observed data on blogs using swarm intelligence, where the goal is to accurately model and predict the future behavior of a large population after observing their interactions during a training phase. Specifically, an ant colony optimization model is trained with behavioral trend from the blog data and is tested over real-world blogs. Promising results were obtained in trend prediction using ant colony based pheromone classier and CHI statistical measure. We provide empirical guidelines for selecting suitable parameters for the model, conclude with interesting observations, and envision future research directions.     

An effective clinical decision support system using swarm intelligence

The Journal of Supercomputing - As healthcare organizations collect a large volume of data on a daily basis, there is an absolute necessity to extract valuable information from them, owing to the...     

Business intelligence using machine learning algorithms

Multimedia Tools and Applications - Business intelligence, as one of the branches of information technology, is increasingly considered by managers in today’s business world. In order to make...     

基于AQPSO的数据聚类

提出了一种新的聚类算法——适应性的基于量子行为的微粒群优化算法的数据聚类(AQPSO)。AQPSO在全局搜索能力和局部搜索能力上优于PSO和QPSO算法,它的适应性方法比较接近于高水平智能群体的社会有机体的学习过程,并且能保证种群不断地进化。聚类过程都是根据数据向量之间的Euclidean(欧几里得的)距离。PSO和QPSO的不同在于聚类中心的进化上。QPSO和AQPSO的不同在于参数的选择上。实验中用到4个数据集比较聚类的效果,结果证明了AQPSO聚类方法优于PSO和QPSO聚类方法。     

Robots, insects and swarm intelligence

The aim of this paper is to consider the relationships between robots and insects. To this end, an overview is provided of the two main areas in which insects have been implicated in robotics research. First, robots have been used to provide working models of mechanisms underlying insect behaviour. Second, there are developments in robotics that have been inspired by our understanding of insect behaviour; in particular the approach of swarm robotics. In the final section of the paper, the possibility of achieving “strong swarm intelligence” is discussed. Two possible interpretations of strong swarm intelligence are raised: (1) the emergence of a group mind from a natural, or robot swarm, and (2) that behaviours could emerge from a swarm of artificial robots in the same way as they emerge from a biological swarm. Both interpretations are dismissed as being unachievable in principle. It is concluded that bio-robotic modelling and biological inspiration have made important contributions to both insect and robot research, but insects and robots remain separated by the divide between the living and the purely mechanical.     

Enhancing particle swarm optimization using generalized opposition-based learning

Particle swarm optimization (PSO) has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence when solving complex problems. This paper presents an enhanced PSO algorithm called GOPSO, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome this problem. GOBL can provide a faster convergence, and the Cauchy mutation with a long tail helps trapped particles escape from local optima. The proposed approach uses a similar scheme as opposition-based differential evolution (ODE) with opposition-based population initialization and generation jumping using GOBL. Experiments are conducted on a comprehensive set of benchmark functions, including rotated multimodal problems and shifted large-scale problems. The results show that GOPSO obtains promising performance on a majority of the test problems.     

Optimal design of a scaled-up PRO system using swarm intelligence approach

In this study,the pressure-retarded osmosis (PRO) process is optimized using Harris hawks optimization (HHO)-based maximum power point tracking (MPPT) technolog...     

Deep learning with adaptive learning rate using laplacian score

An attempt has been made to improve the performance of Deep Learning with Multilayer Perceptron (MLP). Tuning the learning rate or finding an optimum learning rate in MLP is a major challenge. Depending on the value of the learning rate, classification accuracy can vary drastically. This issue has been taken as a challenge in this paper. In this paper, a new approach has been proposed to combine adaptive learning rate in conjunction with the concept of Laplacian score for varying the weights. Learning rate is taken as a function of parameter which itself is updated on the basis of error gradient by forming mini-batches. Laplacian score of the neuron is further used for updating the incoming weights. This removes the bottleneck involved in finding the optimum value for the learning rate in Deep Learning by using MLP. It is observed on benchmark datasets that this approach leads to increase in classification accuracy as compared to the existing benchmark levels achieved by the well known methods of deep learning.     

Artificial intelligence and economic modelling

Abstract: Artificial intelligence has emerged from the toy problem world and is being applied to real domains in a more general way, the existence of several large application systems supporting the belief that a generation of smarter and more general systems will be developed. However, a new problem, sometimes referred to as the fusion problem, has been identified, which acts to restrict the development of such systems. This paper explains the nature of the problem, and by examining a proposed expert system in economics (ESE), discussing three approaches to a prototype ESE and the problems associated with them, draws some conclusions with regard to data fusion and expert co-operation.     

A shape-based adaptive segmentation of time-series using particle swarm optimization

The increasing size of large databases has motivated many researchers to develop methods to reduce the dimensionality of data so that their further analysis can be easier and faster. There are many techniques for time-series’ dimensionality reduction; however, majority of them need an input by the user such as the number of segments. In this paper, the segmentation problem is analyzed from the optimization point of view. A new approach for time-series’ segmentation based on Particle Swarm Optimization (PSO) is proposed which is highly adaptive to time-series’ shape and shape-based characteristics. The proposed approach, called Adaptive Particle Swarm Optimization Segmentation (APSOS), is tested on various datasets to demonstrate its effectiveness and efficiency. Experiments are conducted to show that APSOS is independent of user input parameters and the results indicate that the proposed approach outperforms common methods used for the time-series segmentation.     

Multi-objective shape optimization of transonic airfoil sections using swarm intelligence and surrogate models

In this article, the optimization problem of designing transonic airfoil sections is solved using a framework based on a multi-objective optimizer and surrogate models for the objective functions and constraints. The computed Pareto-optimal set includes solutions that provide a trade-off between maximizing the lift-to-drag ratio during cruise and minimizing the trailing edge noise during the aircraft’s approach to landing. The optimization problem was solved using a recently developed multi-objective optimizer, which is based on swarm intelligence. Additional computational intelligence tools, e.g., artificial neural networks, were utilized to create surrogate models of the objective functions and constraints. The results demonstrate the effectiveness and efficiency of the proposed optimization framework when applied to simulation-based engineering design optimization problems.     

Dynamic economic load dispatch using hybrid swarm intelligence based harmony search algorithm

This paper presents the hybrid harmony search algorithm with swarm intelligence (HHS) to solve the dynamic economic load dispatch problem. Harmony Search (HS) is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work is an attempt to hybridize the HS algorithm with the powerful population based algorithm PSO for a better convergence of the proposed algorithm. The main aim of dynamic economic load dispatch problem is to find out the optimal generation schedule of the generators corresponding to the most economical operating point of the system over the considered timing horizon. The proposed algorithm also takes care of different constraints like power balance, ramp rate limits and generation limits by using penalty function method. Simulations were performed over various standard test systems with 5 units, 10 units and 30 units and a comparative study is carried out with other recently reported results. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.     

Designing an algorithm for swarm behavior using the concept of Umwelt

In this study, we propose a methodology for designing a swarm behavior. The difficulty in designing the swarm behavior is a gap between the object of evaluation and that of design. The former is the performance of a group, but the latter is the action of each individual. We utilize the concept “Umwelt” in ethology for bridging the gap. The advantage of this concept is that all actions necessary for the swarm behavior can he derived from the purpose of each individual. Using this concept, the swarm behavior can he built into the action algorithm of the individuals. In order to evaluate the proposed method, we construct the swarm algorithm for a search and collection task. Using a computer simulation, we confirmed that the swarm successfully achieved the task with flexibility and parallelism, and also robustness in part. These results support the effectiveness of the proposed methodology.     

Probabilistic modelling, inference and learning using logical theories

This paper provides a study of probabilistic modelling, inference and learning in a logic-based setting. We show how probability densities, being functions, can be represented and reasoned with naturally and directly in higher-order logic, an expressive formalism not unlike the (informal) everyday language of mathematics. We give efficient inference algorithms and illustrate the general approach with a diverse collection of applications. Some learning issues are also considered.     

应用佳点集的混合反向学习人工鱼群算法

为了改善人工鱼群算法求解精度较低、容易过早收敛的弱点,提出了一种应用佳点集和反向学习的人工鱼群算法.改进算法在迭代中对当前种群中部分优质个体执行一般动态反向学习,生成它们的反向种群,引导种群向包含全局最优的解空间逼近,以提高算法的平衡和探索能力.当种群的拥挤程度超过阈值λ时,利用佳点集机制对大部分个体重新初始化,以帮助算法脱离局部最优的约束.在六个Benchmark函数上的实验表明,该算法收敛速度快、求解精度高,适合求解函数优化问题.     

Development of efficient identification scheme for nonlinear dynamic systems using swarm intelligence techniques

This paper outlines the basic concept and principles of two simple and powerful swarm intelligence tools: the particle swarm optimization (PSO) and the Bacterial Foraging Optimization (BFO). The adaptive identification of an unknown plant has been formulated as an optimization problem and then solved using the PSO and BFO techniques. Using this new approach efficient identification of complex nonlinear dynamic plants have been carried out through simulation study.