Circuit design using evolutionary algorithms

Beielstein, Thomas; Dienstuhl, Jan; Feist, C.; Pompl, M.

doi:10.1109/cec.2002.1004534

Cited by 7 publications

(2 citation statements)

References 16 publications

(4 reference statements)

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“…A description of the experimental design (DoE) methods we used is omitted here. [Kle87,LK00] give excellent introductions into design of experiments, the applicability of DoE to evolutionary algorithms is shown in [Bei03].…”

Section: Statistical Experimental Designmentioning

confidence: 99%

Optimal Elevator Group Control by Evolution Strategies

Beielstein

Ewald

Markon³

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. Efficient elevator group control is important for the operation of large buildings. Recent developments in this field include the use of fuzzy logic and neural networks. This paper summarizes the development of an evolution strategy (ES) that is capable of optimizing the neuro-controller of an elevator group controller. It extends the results that were based on a simplified elevator group controller simulator. A threshold selection technique is presented as a method to cope with noisy fitness function values during the optimization run. Experimental design techniques are used to analyze first experimental results.

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Section: Statistical Experimental Designmentioning

confidence: 99%

Optimal Elevator Group Control by Evolution Strategies

Beielstein

Ewald

Markon³

2003

Genetic and Evolutionary Computation — GECCO 2003

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“…This group is represented by the meta-heuristic algorithms [7]. These include, e.g., particle swarm optimisation [8], genetic or evolutionary algorithms [9], [10], [11], [12], [13], [14], and direct-search algorithms [15]. A multi-objective optimisationbased approach to the TA problem is typically considered with application of the NSGAII algorithm [16], [17], In the latter group of algorithms, if certain requirements are met, the algorithms tends to converge to a global optimum.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Positioning Capability Assessment Based on Optimal Thrust Allocation

Piekło¹,

Witkowska

Zubowicz

2023

Polish Maritime Research

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The article presents an efficient method of optimal thrust allocation over the actuators in a dynamically positioned ship, according to the DNV-ST-0111 standard, Level 1. The optimisation task is approximated to a convex problem with linear constraints and mathematically formulated as quadratic programming. The case study is being used to illustrate the use of the proposed approach in assessing the DP capability of a rescue ship. The quadratic programming-based approach applied for dynamic positioning capability assessment allows for fast calculations to qualitatively compare different ship designs. In comparison with the DNV tool, it gives 100% successful validation for a ship with azimuth thrusters and a pessimistic solution for a ship equipped with propellers with rudders. Therefore, it can be safely applied at an early design stage.

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