Single Design Criterion Research Articles

Automatic Design of Robot Swarms under Concurrent Design Criteria: A Study Based on Iterated F‐Race

Automatic design is an appealing approach to realizing robot swarms. In this approach, a designer specifies a mission that the swarm must perform, and an optimization algorithm searches for the control software that enables the robots to perform the given mission. Traditionally, research in automatic design has focused on missions specified by a single design criterion, adopting methods based on single‐objective optimization algorithms. In this study, we investigate whether existing methods can be adapted to address missions specified by concurrent design criteria. We focus on the bi‐criteria case. We conduct experiments with a swarm of e‐puck robots that must perform sequences of two missions: each mission in the sequence is an independent design criterion that the automatic method must handle during the optimization process. We consider modular and neuroevolutionary methods that aggregate concurrent criteria via the weighted sum, hypervolume, or ‐norm. We compare their performance with that of Mandarina, an original automatic modular design method. Mandarina integrates Iterated F‐race as an optimization algorithm to conduct the design process without aggregating the design criteria. Results from realistic simulations and demonstrations with physical robots show that the best results are obtained with modular methods and when the design criteria are not aggregated.

Dual-Polarized Metal-Flare Sliced Notch Antenna Array

This article presents a dual-polarized metal-flare sliced notch antenna (SNA) array comprising metallic Vivaldi flares “sliced” by air gaps. Aside from the slice gaps in the flares (supported with foam sheets in the array build), the SNA is an exact duplicate of a conventional all-metal Vivaldi array, against which the SNA is presented directly in a one-to-one comparison to isolate the effect of slicing on dual-polarized radiator performance. The SNA has very similar capabilities as the Vivaldi in all metrics (match, gain, port isolation, and bandwidth), except that the SNA demonstrates lower cross-polarization (cross-pol) across all scan angles and frequencies. Both arrays are optimally sampled and have excellent matching from 2.6 to 21.2 GHz with zero scan blindness out to wide angles. For this demonstration, uniform slices are introduced in the flares to satisfy a single design criterion of −10 dB cross-pol (relative to copolarized fields) for a 45° scan cone at all frequencies, culminating in a peak SNA cross-pol improvement of 35 dB (relative to the reference Vivaldi array). Infinite array predictions are followed up by measurements on a 256-port prototype aperture. This work demonstrates that the slicing technique can be applied to effectively mitigate high cross-pol in dual-polarized Vivaldi arrays without inhibiting other performance metrics.

Semi-submersible wind turbine hull shape design for a favorable system response behavior

Floating offshore wind turbines are a novel technology, which has reached, with the first wind farm in operation, an advanced state of development. The question of how floating wind systems can be optimized to operate smoothly in harsh wind and wave conditions is the subject of the present work. An integrated optimization was conducted, where the hull shape of a semi-submersible, as well as the wind turbine controller were varied with the goal of finding a cost-efficient design, which does not respond to wind and wave excitations, resulting in small structural fatigue and extreme loads.The optimum design was found to have a remarkably low tower-base fatigue load response and small rotor fore-aft amplitudes. Further investigations showed that the reason for the good dynamic behavior is a particularly favorable response to first-order wave loads: The floating wind turbine rotates in pitch-direction about a point close to the rotor hub and the rotor fore-aft motion is almost unaffected by the wave excitation. As a result, the power production and the blade loads are not influenced by the waves. A comparable effect was so far known for Tension Leg Platforms but not for semi-submersible wind turbines. The methodology builds on a low-order simulation model, coupled to a parametric panel code model, a detailed viscous drag model and an individually tuned blade pitch controller. The results are confirmed by the higher-fidelity model FAST. A new indicator to express the optimal behavior through a single design criterion has been developed.

Flexible waveform-constrained optimization design method for cognitive radar

The problem of waveform optimization design for cognitive radar (CR) in the presence of extended target with unknown target impulse response (TIR) is investigated. On the premise of ensuring the TIR estimation precision, a flexible waveform-constrained optimization design method taking both target detection and range resolution into account is proposed. In this method, both the estimate of TIR and transmitted waveform can be updated according to the environment information fed back by the receiver. Moreover, rather than optimizing waveforms for a single design criterion, the framework can synthesize waveforms that provide a trade-off between competing design criteria. The trade-off is determined by the parameter settings, which can be adjusted according to the requirement of radar performance in each cycle of CR. Simulation results demonstrate that CR with the proposed waveform performs better than a traditional radar system with a fixed waveform and offers more flexibility and practicability.

Characterization of supercharged cellulase activity and stability in ionic liquids

Ionic liquids (ILs) have many potential benefits in biochemical processes, including improved substrate or product solubility, increased enzyme selectivity, and higher yield. Varying ion substituents allow ILs to be tuned or optimized to achieve a specific goal. Unfortunately, optimization based on a single design criterion can have undesirable side effects on other process components. For example, hydrophilic ILs capable of efficiently dissolving biomass often inhibit enzymatic activity during hydrolysis. A panel of nine different aqueous ILs was selected for this study to systematically assess which factors contribute to the loss of enzyme activity. The activity of endoglucanase E1 from Acidothermus cellulolyticus steadily decreased in higher concentrations of ILs, especially in the presence of the common cellulose dissolving solvent 1-ethyl-3-methylimidazolium acetate. The impact of most other ILs could be rationalized via the Hofmeister series. Enzyme behavior was further probed by rationally modifying the surface charge of E1. Variants were computationally designed to have positively or negatively charged surfaces and assessed for activity in ILs. Surprisingly, positive supercharging maintained wild type activity levels in ILs, while negative supercharging drastically reduced activity. Discrepancy between stability and activity measurements for some ILs indicated active site inhibition or other unique inactivation mechanisms might be crucial components to consider in future studies.

Multicriteria global optimization for biocircuit design.

BackgroundOne of the challenges in Synthetic Biology is to design circuits with increasing levels of complexity. While circuits in Biology are complex and subject to natural tradeoffs, most synthetic circuits are simple in terms of the number of regulatory regions, and have been designed to meet a single design criterion.ResultsIn this contribution we introduce a multiobjective formulation for the design of biocircuits. We set up the basis for an advanced optimization tool for the modular and systematic design of biocircuits capable of handling high levels of complexity and multiple design criteria. Our methodology combines the efficiency of global Mixed Integer Nonlinear Programming solvers with multiobjective optimization techniques. Through a number of examples we show the capability of the method to generate non intuitive designs with a desired functionality setting up a priori the desired level of complexity.ConclusionsThe methodology presented here can be used for biocircuit design and also to explore and identify different design principles for synthetic gene circuits. The presence of more than one competing objective provides a realistic design setting where every solution represents an optimal trade-off between different criteria.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0113-3) contains supplementary material, which is available to authorized users.

Codon Optimization OnLine (COOL): a web-based multi-objective optimization platform for synthetic gene design

Codon optimization has been widely used for designing synthetic genes to improve their expression in heterologous host organisms. However, most of the existing codon optimization tools consider a single design criterion and/or implement a rather rigid user interface to yield only one optimal sequence, which may not be the best solution. Hence, we have developed Codon Optimization OnLine (COOL), which is the first web tool that provides the multi-objective codon optimization functionality to aid systematic synthetic gene design. COOL supports a simple and flexible interface for customizing various codon optimization parameters such as codon adaptation index, individual codon usage and codon pairing. In addition, users can visualize and compare the optimal synthetic sequences with respect to various fitness measures. User-defined DNA sequences can also be compared against the COOL optimized sequences to show the extent by which the user's sequences can be further improved. COOL is free to academic and non-commercial users and licensed to others for a fee by the National University of Singapore. Accessible at http://bioinfo.bti.a-star.edu.sg/COOL/ CONTACT: cheld@nus.edu.sg Supplementary data are available at Bioinformatics online.

Rotorcraft Engine Cycle Optimization at Mission Level

This work investigates the potential to reduce fuel consumption associated with civil rotorcraft operations at mission level, through optimization of the engine design point cycle parameters. An integrated simulation framework, comprising models applicable to rotorcraft flight dynamics, rotor blade aeroelasticity, and gas turbine performance, has been deployed. A comprehensive and computationally efficient optimization strategy, utilizing a novel particle-swarm method, has been structured. The developed methodology has been applied on a twin-engine light and a twin-engine medium rotorcraft configuration. The potential reduction in fuel consumption has been evaluated in the context of designated missions, representative of modern rotorcraft operations. Optimal engine design point cycle parameters, in terms of total mission fuel consumption, have been obtained. Pareto front models have been structured, describing the optimum interrelationship between maximum shaft power and mission fuel consumption. The acquired results suggest that, with respect to technological limitations, mission fuel economy can be improved with the deployment of design specifications leading to increased thermal efficiency, while simultaneously catering for sufficient performance to satisfy airworthiness certification requirements. The developed methodology enables the identification of optimum engine design specifications using a single design criterion; the respective trade-off between fuel economy and payload–range capacity, through maximum contingency shaft power, that the designer is prepared to accept.

An Integrated Approach for the Multidisciplinary Design of Optimum Rotorcraft Operations

This work focuses on the development and application of a generic methodology targeting the design of optimum rotorcraft operations in terms of fuel burn, gaseous emissions, and ground noise impact. An integrated tool capable of estimating the performance and emitted noise of any defined rotorcraft configuration within any designated mission has been deployed. A comprehensive and cost-effective optimization strategy has been structured. The methodology has been applied to two generic, baseline missions representative of current rotorcraft operations. Optimally designed operations for fuel burn, gaseous emissions, and ground noise impact have been obtained. A comparative evaluation has been waged between the acquired optimum designs. The respective trade-off arising from the incorporation of flight paths optimized for different objectives has been quantified. Pareto front derived models for fuel burn and emitted noise have been structured for each mission. The Pareto models have been subsequently deployed for the design of operations optimized in a multidisciplinary manner. The results have shown that the proposed methodology is promising with regards to achieving simultaneous reduction in fuel burn, gaseous emissions, and ground noise impact for any defined mission. The obtainable reductions are found to be dependent on the designated mission. Finally, the potential to design optimum operations in a multidisciplinary fashion using only a single design criterion is demonstrated.

Sequential experimental design based on multiobjective optimization procedures

Model-based sequential experimental designs are frequently applied for discrimination of rival models and/or estimation of precise model parameters. Although the development and use of a single design criterion to perform the simultaneous model discrimination and precise parameter estimation seem appealing, published material indicates that previous attempts to develop such a single design criterion have not been successful. Despite that, this problem has rarely been analyzed with the help of multiobjective optimization procedures. In this work, a multiobjective optimization method based on the particle swarm optimization procedure is used to build the Pareto fronts in experimental design problems where distinct design criteria used for discrimination of rival models and/or estimation of precise model parameters are considered simultaneously. It is shown through the rigorous analysis of the Pareto sets that both design objectives are frequently conflicting, which means that optimum discrimination of rival models and estimation of precise model parameters cannot be performed simultaneously in many cases. However, it is also shown that the use of the posterior covariance matrix of estimated model parameters for model discrimination makes the design of experiments for the simultaneous optimum model discrimination and estimation of model parameters possible in many experimental design problems.

Design and optimization of manufacturing facilities layouts

The present paper deals with the design of manufacturing facilities layout with the consideration of downtime of facilities and space utilization using genetic algorithm (GA). The existing approaches to solve problems of unequal area facility layout in manufacturing environments can provide optimal solutions with limitations such as single design criterion, manual revision, and approximated shape. Also, importance to practical factors such as downtime of facilities owing to planned maintenance or failure of machines, and floor space utilization are addressed to a lesser degree. A mathematical model and a heuristic solution procedure for designing manufacturing facilities layouts that address the above-mentioned shortcomings are proposed. The proposed model minimizes the cost associated with material handling during normal and breakdown periods with due consideration of compactness of the layout. A heuristic placement procedure, which is capable of changing the orientation of facilities, is proposed to place the machines in the floor area, in order to obtain maximum space utilization. The proposed design approach is evaluated by solving different layout design problems taken from the literature and by solving an industrial case study. The results of the proposed GA optimizer are compared with results obtained from greedy search and simulated annealing methods. The results show that the approach performs better in most of the cases.

Solving unequal area facility layout problems using genetic algorithm

This paper presents an approach for solving multi-row, unequal area, manufacturing facility layout problems using genetic algorithm. The existing approach for solving problems of unequal area facility layout in manufacturing environments can provide optimal solutions with limitations like single design criterion, manual revision and approximated shape. Also, practical factors like cost involved in additional travel during facilities breakdown and floor space utilisation are not considered. The proposed model minimises the cost associated with material handling during normal and breakdown periods with due consideration of compactness of the layout. The reliability of the facilities are accounted for the calculation of additional material transfer between the facilities during a breakdown period. A novel placement procedure which is capable of changing the orientation of facilities is used to place the facilities in the given building area. The mathematical formulation, the details of solution and its methodology are presented with a case study.