Tumor cell growth models involve high-dimensional parameter spaces that require computationally tractable methods to solve. To address a proposed tumor growth dynamics mathematical model, an instance of the particle swarm optimization method was implemented to speed up the search process in the multi-dimensional parameter space to find optimal parameter values that fit experimental data from mi...

Objective function evaluation in continuous optimization tasks is often the operation that dominates the algorithm’s cost. In particular in the case of black-box functions, i.e. when no analytical description is available, and the function is evaluated empirically. In such a situation, utilizing information from a surrogate model of the objective function is a well known technique to accelerate...

The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimizat...

Nature has always been a great source of inspiration for the development of computational approaches for optimization. Two major groups representing this class of biologically inspired algorithms are Swarm Intelligence and Evolutionary Computation. Such algorithms are called metaheuristics and are recognized to be efficient approaches for solving complex problems. Both Swarm Intelligence and Ev...

The process of identifying the optimal parameters for an optimization algorithm or a machine learning one is a costly combinatorial problem because it involves the search of a large, possibly infinite, space of candidate parameter sets. Our work compares grid search with a simple genetic algorithm when used to find the optimal parameter setting for an ID3 like learner operating on given dataset...

Ultrasonic vibration assisted single point incremental forming (UVaSPIF) is based on localized plastic deformation in a sheet metal blank. It consists to deform gradually and locally the sheet metal using vibrating hemispherical-head tool controlled by a CNC milling machine. The ultrasonic excitation of forming tool reduces the vertical component of forming force. In addition, application of ul...

It can be observed from the experimental data of different processes that different process parameter combinations can lead to the same performance indicators, but during the optimization of process parameters, using current techniques, only one of these combinations can be found when a given objective function is specified. The combination of process parameters obtained after optimization may ...

274 Optimization of process parameter for surface finishing in abrasive flow finishing process Ravi Gupta (M.E) Asst. Prof.(Lovely Professional University) [email protected] , Abstract-Abrasive flow finishing is a non-conventional finishing process that is used for finishing of work piece which are difficult to finish by conventional method i.e. by honing, lapping and other machining operati...

We deal with the optimization of process parameters in industrial continuous casting of steel. The process requires fine-tuning of numerous parameters with respect to the metallurgical cooling criteria to achieve the highest possible quality of the cast steel. We tackle the problem with various optimization methods: local optimization, conjugate gradient, downhill simplex and several types of e...