Multi-disciplinary design optimization (MDO) is the process whereby the often conflicting requirements of the different disciplines to the engineering design process attempts to converge upon a description that represents an acceptable compromise in the design space. We present a simple demonstrator of a flexible workflow framework for engineering design optimization using an e-Science tool. Th...

Swarm Intelligence is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. The Bee Colony Optimization (BCO) metaheuristic has been introduced fairly recently as a new direction in the field of Swarm Intelligence. Artificial bees represent agents, which collaboratively solve complex combinatorial optimization problem. The chapter present...

Panagiotis Vogiatzis Computational Modeling, Analysis and Design Optimization Research Laboratory Department of Mechanical Engineering State University of New York at Stony Brook Stony Brook, NY, 11794 Email: [email protected] Shikui Chen Computational Modeling, Analysis and Design Optimization Research Laboratory Department of Mechanical Engineering State University of New Yo...

COMPUTATIONAL ENGINEERING AND DESIGN CENTER SCHOOL OF ENGINEERING SCIENCES Doctor of Philosophy Artificial Intelligence Technologies in Complex Engineering Design by Yew Soon Ong Engineering design optimization is an emerging technology whose application both tends to shorten design-cycle time and finds new designs that are not only feasible, but also nearer to optimum, based on specified desig...

This paper presents technical insights gained from developing a multidisciplinary design and analysis (MDA) environment, which proficiently integrates, coordinates and controls disciplinary software packages, data sources, and human factors. The MDA environment cost-effectively boosters global optimization of product design problems by means of integrating and coordinating engineering resources...

The design of an optimal control strategy for a hybrid system is a matter of growing interest in computational engineering. The solution of optimization problems in most engineering disciplines often requires efficient parallel optimization algorithms to solve these kinds of problems in reasonable time. Instead of introducing parallelism to selected components of an existing sequential algorith...

Traffic Engineering (TE) is the process of controlling how traffic flows through a network in order to facilitate efficient and reliable network operations while simultaneously optimizing network resource utilization and traffic performance. TE improves the management of data traffic within a network and provides the better utilization of network resources. Many research works considers intra a...

Traditional mathematical algorithms are incapable of solving real time engineering design problems because of its rigid procedure mainly due to discrete or random data and multi-objective functions in a problem. An optimization algorithm is a procedure which is executed iteratively by comparing various solutions till the optimum or a satisfactory solution is found. There are two population base...

) | ( j k Y prob pkj = = = θ The jth column of P gives the probability distribution associated with the parameter value j = θ . We consider the problem of estimatingθ , based on an observed sample of Y. The m values of θ are called the hypotheses, and finding the best value of θ is hypothesis testing. A randomized detector of θ is a random variable , with a distribution dependent on the observe...