In this contribution, computer-aided optimization is presented as the ultimate tool to improve food processing. The state of the art is reviewed, especially focusing in recent developments using modern optimization techniques. Their potential for industrial applications is also discussed in the light of several important examples. Finally, future trends and research needs are outlined.

Improving food processing using modern optimization methods

Fine-grained geographic localization of nodes is essential for an extensive range of distributed sensor applications. To compute geographic coordinates, localization algorithms commonly use pair-wise distance estimates between nodes. In this paper we present a noise tolerant acoustic ranging mechanism for wireless sensors that employs digital signal processing techniques on standard MICA hardware. We describe how noise canceling, digital filtering and peak detection can be applied to meet the severe resource constraints of the platform, yet yielding average range estimation errors below 10cm independently from the actual node-to-node

/pdf/acoustic-ranging-in-resource-constrained-sensor-networks-ucti0qozqj.pdf

Acoustic Ranging in Resource-Constrained Sensor Networks.

Real-world optimization problems are often subject to uncertainties caused by, e.g., missing information in the prob- lem domain or stochastic models. These uncertainties can take different forms in terms of distribution, bounds, and central tendency. In the multiobjective context, some approaches have been proposed to take uncertainties into account within the opti- mization process. Most of them are based on a stochastic exten- sion of Pareto dominance that is combined with standard, non- stochastic diversity preservation mechanisms. Furthermore, it is often assumed that the shape of the underlying probability distribution is known and that for each solution there is a 'true' objective value per dimension which is disturbed by noise. In this paper, we consider a slightly different scenario where the optimization goal is specified in terms of a quality indicator— a real-valued function that induces a total preorder on the set of Pareto set approximations. We propose a general indicator- model that can handle any type of distribution representing the uncertainty, allows different distributions for different so- lutions, and does not assume a 'true' objective vector per solu- tion, but in general regards a solution to be inherently associ- ated with an unknown probability distribution in the objective space. To this end, several variants of an evolutionary algo- rithm for a specific quality indicator, namely the � -indicator, are suggested and empirically investigated. The comparison to ex- isting techniques such as averaging or probabilistic dominance ranking indicates that the proposed approach is especially useful for high-dimensional objective spaces. Moreover, we introduce a general methodology to visualize and analyze Pareto set ap- proximations in the presence of uncertainty which extends the concept of attainment functions.

http://www.info.univ-angers.fr/pub/basseur/papers/IJCIR_BZ06.pdf

Handling Uncertainty in Indicator-Based Multiobjective Optimization

The goal of robust design is to develop stable products that exhibit minimum sensitivity to uncontrollable variations. The main drawback of many quality engineering approaches, including Taguchi's ideology, is that they cannot efficiently handle presence of several often conflicting objectives and constraints that occur in various design environments.

Classical vector optimization and multiobjective genetic algorithms offer numerous techniques for simultaneous optimization of multiple responses, but they have not addressed the central quality control activities of tolerance design and parameter optimization. Due to their ability to search populations of candidate designs in parallel without assumptions of continuity, unimodality or convexity of underlying objectives, genetic algorithms are an especially viable tool for off-line quality control.

In this paper we introduce a new methodology which integrates key concepts from diverse fields of robust design, multiobjective optimization and genetic algorithms. The genetic algorithm developed in this work applies natural genetic operators of reproduction, crossover and mutation to evolve populations of hyper-rectangular design regions while simultaneously reducing the sensitivity of the generated designs to uncontrollable variations. The improvement in quality of successive generations of designs is achieved by conducting orthogonal array experiments as to increase the average signal-to-noise ratio of a pool of candidate designs from one generation to the next.

A Genetic Algorithm for Multiobjective Robust Design

This article proposes an effective methodology for the fluid-dynamic design optimization of the sliding spool of a hydraulic proportional directional valve: the goal is the minimization of the flow force at a prescribed flow rate, so as to reduce the required opening force while keeping the operation features unchanged. A full three-dimensional model of the flow field within the valve is employed to accurately predict the flow force acting on the spool. A theoretical analysis, based on both the axial momentum equation and flow simulations, is conducted to define the design parameters, which need to be properly selected in order to reduce the flow force without significantly affecting the flow rate. A genetic algorithm, coupled with a computational fluid dynamics flow solver, is employed to minimize the flow force acting on the valve spool at the maximum opening. A comparison with a typical single-objective optimization algorithm is performed to evaluate performance and effectiveness of the employed geneti...

Fluid-dynamic design optimization of hydraulic proportional directional valves

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D Quagliarella

Papers

Genetic algorithms and evolution strategy in engineering and computer science : recent advances and industrial applications