12,846 research outputs found
Towards 'smart lasers': self-optimisation of an ultrafast pulse source using a genetic algorithm
Short-pulse fibre lasers are a complex dynamical system possessing a broad
space of operating states that can be accessed through control of cavity
parameters. Determination of target regimes is a multi-parameter global
optimisation problem. Here, we report the implementation of a genetic algorithm
to intelligently locate optimum parameters for stable single-pulse mode-locking
in a Figure-8 fibre laser, and fully automate the system turn-on procedure.
Stable ultrashort pulses are repeatably achieved by employing a compound
fitness function that monitors both temporal and spectral output properties of
the laser. Our method of encoding photonics expertise into an algorithm and
applying machine-learning principles paves the way to self-optimising `smart'
optical technologies
Unitary Linear Dispersion Code Design and Optimisation for MIMO Communication Systems
Linear Dispersion Codes (LDCs) have recently attracted numerous research interests. Thanks to their efficient spreading of data across both the time and spatial domains, LDCs are capable of achieving a desired Diversity-Multiplexing Trade-off (DMT) in Multiple Input Multiple Output (MIMO) broadband wireless access systems. This paper proposes a novel LDC design method, which relies on the unitary matrix theory combined with a Genetic Algorithm (GA) aided optimisation procedure. The proposed design provides a flexible framework, where new LDCs attaining higher data rates and better error resilience than a number of classic MIMO schemes can be generated. Index Terms Diversity-multiplexing trade-off, genetic algorithm, multiple-input multiple-output, linear dispersion code
A multi-objective genetic algorithm for the design of pressure swing adsorption
Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA
processes would be beneficial for the development of the technology, but their development is
a difficult task due to the complexity of the simulation of PSA cycles and the computational
effort needed to detect the performance at cyclic steady state.
We present a preliminary investigation of the performance of a custom multi-objective genetic
algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of
air for N2 production. The simulation requires a detailed diffusion model, which involves coupled
nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA
to handle this complex problem has been assessed by comparison with direct search methods.
An analysis of the effect of MOGA parameters on the performance is also presented
Optimal phenotypic plasticity in a stochastic environment minimizes the cost/benefit ratio
This paper addresses the question of optimal phenotypic plasticity as a
response to environmental fluctuations while optimizing the cost/benefit ratio,
where the cost is energetic expense of plasticity, and benefit is fitness. The
dispersion matrix \Sigma of the genes' response (H = ln|\Sigma|) is used: (i)
in a numerical model as a metric of the phenotypic variance reduction in the
course of fitness optimization, then (ii) in an analytical model, in order to
optimize parameters under the constraint of limited energy availability.
Results lead to speculate that such optimized organisms should maximize their
exergy and thus the direct/indirect work they exert on the habitat. It is shown
that the optimal cost/benefit ratio belongs to an interval in which differences
between individuals should not substantially modify their fitness.
Consequently, even in the case of an ideal population, close to the optimal
plasticity, a certain level of genetic diversity should be long conserved, and
a part, still to be determined, of intra-populations genetic diversity probably
stem from environment fluctuations. Species confronted to monotonous factors
should be less plastic than vicariant species experiencing heterogeneous
environments. Analogies with the MaxEnt algorithm of E.T. Jaynes (1957) are
discussed, leading to the conjecture that this method may be applied even in
case of multivariate but non multinormal distributions of the responses
Constraint handling strategies in Genetic Algorithms application to optimal batch plant design
Optimal batch plant design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various
products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators,
their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient
one
An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer
The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has
resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have
been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed.
However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy
reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in
a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed.
A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem
in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility
of the model and the developed algorithm
Locating and quantifying gas emission sources using remotely obtained concentration data
We describe a method for detecting, locating and quantifying sources of gas
emissions to the atmosphere using remotely obtained gas concentration data; the
method is applicable to gases of environmental concern. We demonstrate its
performance using methane data collected from aircraft. Atmospheric point
concentration measurements are modelled as the sum of a spatially and
temporally smooth atmospheric background concentration, augmented by
concentrations due to local sources. We model source emission rates with a
Gaussian mixture model and use a Markov random field to represent the
atmospheric background concentration component of the measurements. A Gaussian
plume atmospheric eddy dispersion model represents gas dispersion between
sources and measurement locations. Initial point estimates of background
concentrations and source emission rates are obtained using mixed L2-L1
optimisation over a discretised grid of potential source locations. Subsequent
reversible jump Markov chain Monte Carlo inference provides estimated values
and uncertainties for the number, emission rates and locations of sources
unconstrained by a grid. Source area, atmospheric background concentrations and
other model parameters are also estimated. We investigate the performance of
the approach first using a synthetic problem, then apply the method to real
data collected from an aircraft flying over: a 1600 km^2 area containing two
landfills, then a 225 km^2 area containing a gas flare stack
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