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Adham Atyabi

Bio: Adham Atyabi is an academic researcher from Seattle Children's Research Institute. The author has contributed to research in topics: Computer science & Particle swarm optimization. The author has an hindex of 15, co-authored 46 publications receiving 863 citations. Previous affiliations of Adham Atyabi include Multimedia University & Yale University.


Papers
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Journal ArticleDOI
18 May 2015-PLOS ONE
TL;DR: In this paper, the authors provide an in-depth survey of well-known swarm optimization algorithms and compare them with each other comprehensively through experiments conducted using thirty wellknown benchmark functions and a number of statistical tests are then carried out to determine the significant performances.
Abstract: Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.

382 citations

01 Jan 2015
TL;DR: The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.
Abstract: Many swarm optimization algorithms have been introduced since the early 60's, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.

117 citations

Journal ArticleDOI
TL;DR: The novel robot arm physical design presented in this article successfully decouples its end-effector positioning from its stiffness, and combines the light weight, high payload to weight ratio and robustness of pneumatic actuation with the adaptability and versatility of variable stiffness.
Abstract: Soft robot arms possess unique capabilities when it comes to adaptability, flexibility, and dexterity. In addition, soft systems that are pneumatically actuated can claim high power-to-weight ratio. One of the main drawbacks of pneumatically actuated soft arms is that their stiffness cannot be varied independently from their end-effector position in space. The novel robot arm physical design presented in this article successfully decouples its end-effector positioning from its stiffness. An experimental characterization of this ability is coupled with a mathematical analysis. The arm combines the light weight, high payload to weight ratio and robustness of pneumatic actuation with the adaptability and versatility of variable stiffness. Light weight is a vital component of the inherent safety approach to physical human-robot interaction. To characterize the arm, a neural network analysis of the curvature of the arm for different input pressures is performed. The curvature-pressure relationship is also characterized experimentally.

97 citations

Journal ArticleDOI
TL;DR: Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.

92 citations

Journal ArticleDOI
25 Sep 2015-PLOS ONE
TL;DR: In this paper, the authors studied the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories by learning a support vector machine and exploiting various feature fusion schemes.
Abstract: Multimedia analysis benefits from understanding the emotional content of a scene in a variety of tasks such as video genre classification and content-based image retrieval. Recently, there has been an increasing interest in applying human bio-signals, particularly eye movements, to recognize the emotional gist of a scene such as its valence. In order to determine the emotional category of images using eye movements, the existing methods often learn a classifier using several features that are extracted from eye movements. Although it has been shown that eye movement is potentially useful for recognition of scene valence, the contribution of each feature is not well-studied. To address the issue, we study the contribution of features extracted from eye movements in the classification of images into pleasant, neutral, and unpleasant categories. We assess ten features and their fusion. The features are histogram of saccade orientation, histogram of saccade slope, histogram of saccade length, histogram of saccade duration, histogram of saccade velocity, histogram of fixation duration, fixation histogram, top-ten salient coordinates, and saliency map. We utilize machine learning approach to analyze the performance of features by learning a support vector machine and exploiting various feature fusion schemes. The experiments reveal that ‘saliency map’, ‘fixation histogram’, ‘histogram of fixation duration’, and ‘histogram of saccade slope’ are the most contributing features. The selected features signify the influence of fixation information and angular behavior of eye movements in the recognition of the valence of images.

54 citations


Cited by
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01 Nov 2011
TL;DR: The Communication program emphasizes theory, research, and application to examine the ways humans communicate, verbally and non-verbally, across a variety of levels and contexts, to understand ourselves, the authors' media, their relationships, their culture and how these things connect.
Abstract: The Communication program emphasizes theory, research, and application to examine the ways humans communicate, verbally and non-verbally, across a variety of levels and contexts. This is particularly important as communication shapes our ideas and values, gives rise to our politics, consumption and socialization, and helps to define our identities and realities. Its power and potential is inestimable. From the briefest of text messages to the grandest of public declarations, we indeed live within communication and invite you to join us in appreciating its increasing importance in contemporary society. From Twitter and reality television to family relationships and workplace dynamics, communication is about understanding ourselves, our media, our relationships, our culture and how these things connect.

822 citations

Journal ArticleDOI
TL;DR: A novel metaheuristic algorithm named Henry gas solubility optimization (HGSO), which mimics the behavior governed by Henry’s law to solve challenging optimization problems, provides competitive and superior results compared to other algorithms when solving challenging optimize problems.

602 citations

Journal ArticleDOI
TL;DR: The comparison results on the benchmark functions suggest that MRFO is far superior to its competitors, and the real-world engineering applications show the merits of this algorithm in tackling challenging problems in terms of computational cost and solution precision.

519 citations

Journal ArticleDOI
TL;DR: It has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches and are used to improve the performance of the classical approaches as a hybrid algorithm.

450 citations

Journal ArticleDOI
01 Dec 2016
TL;DR: The comprehensive review of Krill Herd Algorithm as applied to different domain is presented, which covers the applications, modifications, and hybridizations of the KH algorithms.
Abstract: Graphical abstractDisplay Omitted HighlightsThe comprehensive review of Krill Herd Algorithm as applied to different domain is presented.The review covers the applications, modifications and hybridizations of the KH algorithms.It provides future research directions across different areas. Krill Herd (KH) algorithm is a class of nature-inspired algorithm, which simulates the herding behavior of krill individuals. It has been successfully utilized to tackle many optimization problems in different domains and found to be very efficient. As a result, the studies has expanded significantly in the last 3 years. This paper presents the extensive (not exhaustive) review of KH algorithm in the area of applications, modifications, and hybridizations across these fields. The description of how KH algorithm was used in the approaches for solving these kinds of problems and further research directions are also discussed.

449 citations