18,827 research outputs found
Tracking moving optima using Kalman-based predictions
The dynamic optimization problem concerns finding an optimum in a changing environment. In the field of evolutionary algorithms, this implies dealing with a timechanging fitness landscape. In this paper we compare different techniques for integrating motion information into an evolutionary algorithm, in the case it has to follow a time-changing optimum, under the assumption that the changes follow a nonrandom law. Such a law can be estimated in order to improve the optimum tracking capabilities of the algorithm. In particular, we will focus on first order dynamical laws to track moving objects. A vision-based tracking robotic application is used as testbed for experimental comparison
Optimisation of Mobile Communication Networks - OMCO NET
The mini conference âOptimisation of Mobile Communication Networksâ focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University.
The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference
From Social Simulation to Integrative System Design
As the recent financial crisis showed, today there is a strong need to gain
"ecological perspective" of all relevant interactions in
socio-economic-techno-environmental systems. For this, we suggested to set-up a
network of Centers for integrative systems design, which shall be able to run
all potentially relevant scenarios, identify causality chains, explore feedback
and cascading effects for a number of model variants, and determine the
reliability of their implications (given the validity of the underlying
models). They will be able to detect possible negative side effect of policy
decisions, before they occur. The Centers belonging to this network of
Integrative Systems Design Centers would be focused on a particular field, but
they would be part of an attempt to eventually cover all relevant areas of
society and economy and integrate them within a "Living Earth Simulator". The
results of all research activities of such Centers would be turned into
informative input for political Decision Arenas. For example, Crisis
Observatories (for financial instabilities, shortages of resources,
environmental change, conflict, spreading of diseases, etc.) would be connected
with such Decision Arenas for the purpose of visualization, in order to make
complex interdependencies understandable to scientists, decision-makers, and
the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c
Freeze-drying modeling and monitoring using a new neuro-evolutive technique
This paper is focused on the design of a black-box model for the process of freeze-drying of pharmaceuticals. A new methodology based on a self-adaptive differential evolution scheme is combined with a back-propagation algorithm, as local search method, for the simultaneous structural and parametric optimization of the model represented by a neural network. Using the model of the freeze-drying process, both the temperature and the residual ice content in the product vs. time can be determine off-line, given the values of the operating conditions (the temperature of the heating shelf and the pressure in the drying chamber). This makes possible to understand if the maximum temperature allowed by the product is trespassed and when the sublimation drying is complete, thus providing a valuable tool for recipe design and optimization. Besides, the black box model can be applied to monitor the freeze-drying process: in this case, the measurement of product temperature is used as input variable of the neural network in order to provide in-line estimation of the state of the product (temperature and residual amount of ice). Various examples are presented and discussed, thus pointing out the strength of the too
Adaptive Investment Strategies For Periodic Environments
In this paper, we present an adaptive investment strategy for environments
with periodic returns on investment. In our approach, we consider an investment
model where the agent decides at every time step the proportion of wealth to
invest in a risky asset, keeping the rest of the budget in a risk-free asset.
Every investment is evaluated in the market via a stylized return on investment
function (RoI), which is modeled by a stochastic process with unknown
periodicities and levels of noise. For comparison reasons, we present two
reference strategies which represent the case of agents with zero-knowledge and
complete-knowledge of the dynamics of the returns. We consider also an
investment strategy based on technical analysis to forecast the next return by
fitting a trend line to previous received returns. To account for the
performance of the different strategies, we perform some computer experiments
to calculate the average budget that can be obtained with them over a certain
number of time steps. To assure for fair comparisons, we first tune the
parameters of each strategy. Afterwards, we compare the performance of these
strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22
pages, 9 figure
Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems
Dynamic multi-objective optimization problems (DMOPs) are widely accepted to
be more challenging than stationary problems due to the time-dependent nature
of the objective functions and/or constraints. Evaluation of purpose-built
algorithms for DMOPs is often performed on narrow selections of dynamic
instances with differing change magnitude and frequency or a limited selection
of problems. In this paper, we focus on the reproducibility of simulation
experiments for parameters of DMOPs. Our framework is based on an extension of
PlatEMO, allowing for the reproduction of results and performance measurements
across a range of dynamic settings and problems. A baseline schema for dynamic
algorithm evaluation is introduced, which provides a mechanism to interrogate
performance and optimization behaviours of well-known evolutionary algorithms
that were not designed specifically for DMOPs. Importantly, by determining the
maximum capability of non-dynamic multi-objective evolutionary algorithms, we
can establish the minimum capability required of purpose-built dynamic
algorithms to be useful. The simplest modifications to manage dynamic changes
introduce diversity. Allowing non-dynamic algorithms to incorporate
mutated/random solutions after change events determines the improvement
possible with minor algorithm modifications. Future expansion to include
current dynamic algorithms will enable reproduction of their results and
verification of their abilities and performance across DMOP benchmark space.Comment: Accepted for publication in "Proceedings of the Genetic and
Evolutionary Computation Conference (GECCO) 2022
Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network
In this study, an artificial neural network (ANN) based on particle swarm
optimization (PSO) was developed for the time series prediction. The hybrid
ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the
short-term . The performance prediction was evaluated and compared with
another studies available in the literature. Also, we presented properties of
the dynamical system via the study of chaotic behaviour obtained from the
predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with
a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in
order to obtain a new estimator of the predictions, which also allowed us to
compute uncertainties of predictions for noisy Mackey--Glass chaotic time
series. Thus, we studied the impact of noise for several cases with a white
noise level () from 0.01 to 0.1.Comment: 11 pages, 8 figure
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