211 research outputs found
TFAW: wavelet-based signal reconstruction to reduce photometric noise in time-domain surveys
There have been many efforts to correct systematic effects in astronomical
light curves to improve the detection and characterization of planetary
transits and astrophysical variability. Algorithms like the Trend Filtering
Algorithm (TFA) use simultaneously-observed stars to remove systematic effects,
and binning is used to reduce high-frequency random noise. We present TFAW, a
wavelet-based modified version of TFA. TFAW aims to increase the periodic
signal detection and to return a detrended and denoised signal without
modifying its intrinsic characteristics. We modify TFA's frequency analysis
step adding a Stationary Wavelet Transform filter to perform an initial noise
and outlier removal and increase the detection of variable signals. A wavelet
filter is added to TFA's signal reconstruction to perform an adaptive
characterization of the noise- and trend-free signal and the noise contribution
at each iteration while preserving astrophysical signals. We carried out tests
over simulated sinusoidal and transit-like signals to assess the effectiveness
of the method and applied TFAW to real light curves from TFRM. We also studied
TFAW's application to simulated multiperiodic signals, improving their
characterization. TFAW improves the signal detection rate by increasing the
signal detection efficiency (SDE) up to a factor ~2.5x for low SNR light
curves. For simulated transits, the transit detection rate improves by a factor
~2-5x in the low-SNR regime compared to TFA. TFAW signal approximation performs
up to a factor ~2x better than bin averaging for planetary transits. The
standard deviations of simulated and real TFAW light curves are ~40x better
than TFA. TFAW yields better MCMC posterior distributions and returns lower
uncertainties, less biased transit parameters and narrower (~10x) credibility
intervals for simulated transits. We present a newly-discovered variable star
from TFRM.Comment: Accepted for publication by A&A. 13 pages, 16 figures and 5 table
On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
Multitasking optimization is a recently introduced paradigm, focused on the
simultaneous solving of multiple optimization problem instances (tasks). The
goal of multitasking environments is to dynamically exploit existing
complementarities and synergies among tasks, helping each other through the
transfer of genetic material. More concretely, Evolutionary Multitasking (EM)
regards to the resolution of multitasking scenarios using concepts inherited
from Evolutionary Computation. EM approaches such as the well-known
Multifactorial Evolutionary Algorithm (MFEA) are lately gaining a notable
research momentum when facing with multiple optimization problems. This work is
focused on the application of the recently proposed Multifactorial Cellular
Genetic Algorithm (MFCGA) to the well-known Capacitated Vehicle Routing Problem
(CVRP). In overall, 11 different multitasking setups have been built using 12
datasets. The contribution of this research is twofold. On the one hand, it is
the first application of the MFCGA to the Vehicle Routing Problem family of
problems. On the other hand, equally interesting is the second contribution,
which is focused on the quantitative analysis of the positive genetic
transferability among the problem instances. To do that, we provide an
empirical demonstration of the synergies arisen between the different
optimization tasks.Comment: 8 pages, 1 figure, paper accepted for presentation in the 23rd IEEE
International Conference on Intelligent Transportation Systems 2020 (IEEE
ITSC 2020
Quantitative Analysis and Performance Study of Ant Colony Optimization Models Applied to Multi-Mode Resource Constraint Project Scheduling Problem
Constraint Satisfaction Problems (CSP) belongs to this kind of traditional NP-hard problems with a high impact in both, research and industrial domains. However, due to the complexity that CSP problems exhibit, researchers are forced to use heuristic algorithms for solving the problems in a reasonable time. One of the most famous heuristic al- gorithms is Ant Colony Optimization (ACO) algorithm. The possible utilization of ACO algorithms to solve CSP problems requires the de- sign of a decision graph where the ACO is executed. Nevertheless, the classical approaches build a graph where the nodes represent the vari- able/value pairs and the edges connect those nodes whose variables are different. In order to solve this problem, a novel ACO model have been recently designed. The goal of this paper is to analyze the performance of this novelty algorithm when solving Multi-Mode Resource-Constraint Satisfaction Problems. Experimental results reveals that the new ACO model provides competitive results whereas the number of pheromones created in the system is drastically reduced
A local search method for graph clustering heuristics based on partitional distribution learning
The community structure of complex networks reveals hidden relationships in the organization of their constituent nodes. Indeed, many practical problems stemming from different fields of knowledge such as Biology, Sociology, Chemistry and Computer Science can be modeled as a graph. Therefore, graph analysis and community detection have become a key component for understanding the inherent relational characteristics underlying different systems and processes. In this regard, distinct unsupervised quality metrics such as conductance, coverage and modularity, have upsurged in order to evaluate the clustering arrangements based on structural and topological characteristics of the cluster space. In this regard graph clustering can be formulated as an optimization problem based on the maximization of one of such metrics, for which a number of nature-inspired heuristic solvers has been proposed in the literature. This paper elaborates on a novel local search method that allows boosting the convergence of such heuristics by estimating and sampling the cluster arrangement distribution from the set of intermediate produced solutions of the algorithm at hand. Simulation results reveal a generalized better performance compared towards other community detection algorithms in synthetic and real datasets
Comparative study of pheromone control heuristics in ACO algorithms for solving RCPSP problems
Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization
In recent years, Multifactorial Optimization (MFO) has gained a notable
momentum in the research community. MFO is known for its inherent capability to
efficiently address multiple optimization tasks at the same time, while
transferring information among such tasks to improve their convergence speed.
On the other hand, the quantum leap made by Deep Q Learning (DQL) in the
Machine Learning field has allowed facing Reinforcement Learning (RL) problems
of unprecedented complexity. Unfortunately, complex DQL models usually find it
difficult to converge to optimal policies due to the lack of exploration or
sparse rewards. In order to overcome these drawbacks, pre-trained models are
widely harnessed via Transfer Learning, extrapolating knowledge acquired in a
source task to the target task. Besides, meta-heuristic optimization has been
shown to reduce the lack of exploration of DQL models. This work proposes a MFO
framework capable of simultaneously evolving several DQL models towards solving
interrelated RL tasks. Specifically, our proposed framework blends together the
benefits of meta-heuristic optimization, Transfer Learning and DQL to automate
the process of knowledge transfer and policy learning of distributed RL agents.
A thorough experimentation is presented and discussed so as to assess the
performance of the framework, its comparison to the traditional methodology for
Transfer Learning in terms of convergence, speed and policy quality , and the
intertask relationships found and exploited over the search process.Comment: 8 pages, 5 figures, submitted to IEEE Conference on Evolutionary
Computation 2020 (IEEE CEC
Multi-objective heuristics applied to robot task planning for inspection plants
Robotics are generally subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this regard the so-called cost of a mission must be considered as an additional criterion when designing optimal task schedules within the mission at hand. Such a cost can be conceived as the impact of the mission on the robotic resources themselves, which range from the consumption of battery to other negative effects such as mechanic erosion. This manuscript focuses on this issue by presenting experimental results obtained over realistic scenarios of two heuristic solvers (MOHS and NSGA-II) aimed at efficiently scheduling tasks in robotic swarms that collaborate together to accomplish a mission. The heuristic techniques resort to a Random-Keys encoding strategy to represent the allocation of robots to tasks whereas the relative execution order of such tasks within the schedule of certain robots is computed based on the Traveling Salesman Problem (TSP). Experimental results in three different deployment scenarios reveal the goodness of the proposed technique based on the Multi-objective Harmony Search algorithm (MOHS) in terms of Hypervolume (HV) and Coverage Rate (CR) performance indicators
dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems
The emerging research paradigm coined as multitasking optimization aims to
solve multiple optimization tasks concurrently by means of a single search
process. For this purpose, the exploitation of complementarities among the
tasks to be solved is crucial, which is often achieved via the transfer of
genetic material, thereby forging the Transfer Optimization field. In this
context, Evolutionary Multitasking addresses this paradigm by resorting to
concepts from Evolutionary Computation. Within this specific branch, approaches
such as the Multifactorial Evolutionary Algorithm (MFEA) has lately gained a
notable momentum when tackling multiple optimization tasks. This work
contributes to this trend by proposing the first adaptation of the recently
introduced Multifactorial Evolutionary Algorithm II (MFEA-II) to
permutation-based discrete optimization environments. For modeling this
adaptation, some concepts cannot be directly applied to discrete search spaces,
such as parent-centric interactions. In this paper we entirely reformulate such
concepts, making them suited to deal with permutation-based search spaces
without loosing the inherent benefits of MFEA-II. The performance of the
proposed solver has been assessed over 5 different multitasking setups,
composed by 8 datasets of the well-known Traveling Salesman (TSP) and
Capacitated Vehicle Routing Problems (CVRP). The obtained results and their
comparison to those by the discrete version of the MFEA confirm the good
performance of the developed dMFEA-II, and concur with the insights drawn in
previous studies for continuous optimization.Comment: 7 pages, 0 figures, Camera-ready version of the paper accepted for
presentation in The Genetic and Evolutionary Computation Conference 2020
(GECCO 2020
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