1,001 research outputs found
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Ensemble-based genetic algorithm explainer with automized image segmentation: A case study on melanoma detection dataset
Explainable Artificial Intelligence (XAI) makes AI understandable to the human user particularly when the
model is complex and opaque. Local Interpretable Model-agnostic Explanations (LIME) has an image explainer
package that is used to explain deep learning models. The image explainer of LIME needs some parameters to
be manually tuned by the expert in advance, including the number of top features to be seen and the number
of superpixels in the segmented input image. This parameter tuning is a time-consuming task. Hence, with the
aim of developing an image explainer that automizes image segmentation, this paper proposes Ensemblebased Genetic Algorithm Explainer (EGAE) for melanoma cancer detection that automatically detects and
presents the informative sections of the image to the user. EGAE has three phases. First, the sparsity of
chromosomes in GAs is determined heuristically. Then, multiple GAs are executed consecutively. However,
the difference between these GAs are in different number of superpixels in the input image that result in
different chromosome lengths. Finally, the results of GAs are ensembled using consensus and majority votings.
This paper also introduces how Euclidean distance can be used to calculate the distance between the actual
explanation (delineated by experts) and the calculated explanation (computed by the explainer) for accuracy
measurement. Experimental results on a melanoma dataset show that EGAE automatically detects informative
lesions, and it also improves the accuracy of explanation in comparison with LIME efficiently. The python
codes for EGAE, the ground truths delineated by clinicians, and the melanoma detection dataset are available
at https://github.com/KhaosResearch/EGAEThis work has been partially funded by grant PID2020-112540RBC41 (funded by MCIN/AEI/10.13039/501100011033/, Spain), AETHERUMA, Spain (A smart data holistic approach for context-aware data analytics: semantics and context exploitation). Funding for open access charge: Universidad de MĂĄlaga/CBUA. Additionally, we thank Dr. Miguel Ăngel Berciano Guerrero from Unidad de OncologĂa Intercentros, Hospitales Univesitarios Regional Virgen de la Victoria de MĂĄlaga, and Instituto de Investigaciones BiomĂ©dicas (IBIMA), MĂĄlaga, Spain, for his support in images selection and general medical orientation in the particular case of Melanoma
Mooring System Design Optimization Using a Surrogate Assisted Multi-Objective Genetic Algorithm
This is the author accepted manuscript. The final version is available from Taylor & Francis via the DOI in this record.This article presents a novel framework for the multi-objective optimization of o shore re-
newable energy mooring systems using a random forest based surrogate model coupled to
a genetic algorithm. This framework is demonstrated for the optimization of the mooring
system for a
oating o shore wind turbine highlighting how this approach can aid in the
strategic design decision making for real-world problems faced by the o shore renewable
energy sector. This framework utilizes validated numerical models of the mooring system
to train a surrogate model, which leads to a computationally e cient optimization routine,
allowing the search space to be more thoroughly searched. Minimizing both the cost and
cumulative fatigue damage of the mooring system, this framework presents a range of op-
timal solutions characterizing how design changes impact the trade-o between these two
competing objectives.This work is funded by the EPSRC (UK) grant for the SuperGen Marine United Kingdom Centre for Marine Energy Research (UKCMER) [grant number: EP/P008682/1]. The authors would also like to thank Jason Jonkman at NREL who provided the hydrodynamic data for the OC4 semi-submersible and Orcina Ltd. for providing OrcaFlex
Bioinformatics Applications Based On Machine Learning
The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems
Multi-objective Optimization in Traffic Signal Control
Traffic Signal Control systems are one of the most popular Intelligent Transport Systems and they are widely used around the world to regulate traffic flow. Recently, complex optimization techniques have been applied to traffic signal control systems to improve their performance. Traffic simulators are one of the most popular tools to evaluate the performance of a potential solution in traffic signal optimization. For that reason, researchers commonly optimize traffic signal timing by using simulation-based approaches. Although evaluating solutions using microscopic traffic simulators has several advantages, the simulation is very time-consuming.
Multi-objective Evolutionary Algorithms (MOEAs) are in many ways superior to traditional search methods. They have been widely utilized in traffic signal optimization problems. However, running MOEAs on traffic optimization problems using microscopic traffic simulators to estimate the effectiveness of solutions is time-consuming. Thus, MOEAs which can produce good solutions at a reasonable processing time, especially at an early stage, is required. Anytime behaviour of an algorithm indicates its ability to provide as good a solution as possible at any time during its execution. Therefore, optimization approaches which have good anytime behaviour are desirable in evaluation traffic signal optimization. Moreover, small population sizes are inevitable for scenarios where processing capabilities are limited but require quick response times. In this work, two novel optimization algorithms are introduced that improve anytime behaviour and can work effectively with various population sizes.
NS-LS is a hybrid of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and a local search which has the ability to predict a potential search direction. NS-LS is able to produce good solutions at any running time, therefore having good anytime behaviour. Utilizing a local search can help to accelerate the convergence rate, however, computational cost is not considered in NS-LS. A surrogate-assisted approach based on local search (SA-LS) which is an enhancement of NS-LS is also introduced. SA-LS uses a surrogate model constructed using solutions which already have been evaluated by a traffic simulator in previous generations.
NS-LS and SA-LS are evaluated on the well-known Benchmark test functions: ZDT1 and ZDT2, and two real-world traffic scenarios: Andrea Costa and Pasubio. The proposed algorithms are also compared to NSGA-II and Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D). The results show that NS-LS and SA-LS can effectively optimize traffic signal timings of the studied scenarios. The results also confirm that NS-LS and SA-LS have good anytime behaviour and can work well with different population sizes. Furthermore, SA-LS also showed to produce mostly superior results as compared to NS-LS, NSGA-II, and MOEA/D.Ministry of Education and Training - Vietna
Development of sustainable groundwater management methodologies to control saltwater intrusion into coastal aquifers with application to a tropical Pacific island country
Saltwater intrusion due to the over-exploitation of groundwater in coastal aquifers is a critical challenge facing groundwater-dependent coastal communities throughout the world. Sustainable management of coastal aquifers for maintaining abstracted groundwater quality within permissible salinity limits is regarded as an important groundwater management problem necessitating urgent reliable and optimal management methodologies. This study focuses on the development and evaluation of groundwater salinity prediction tools, coastal aquifer multi-objective management strategies, and adaptive management strategies using new prediction models, coupled simulation-optimization (S/O) models, and monitoring network design, respectively.
Predicting the extent of saltwater intrusion into coastal aquifers in response to existing and changing pumping patterns is a prerequisite of any groundwater management framework. This study investigates the feasibility of using support vector machine regression (SVMR), an innovative artificial intelligence-based machine learning algorithm, to predict salinity at monitoring wells in an illustrative aquifer under variable groundwater pumping conditions. For evaluation purposes, the prediction results of SVMR are compared with well-established genetic programming (GP) based surrogate models. The prediction capabilities of the two learning machines are evaluated using several measures to ensure their practicality and generalisation ability. Also, a sensitivity analysis methodology is proposed for assessing the impact of pumping rates on salt concentrations at monitoring locations. The performance evaluations suggest that the predictive capability of SVMR is superior to that of GP models. The sensitivity analysis identifies a subset of the most influential pumping rates, which is used to construct new SVMR surrogate models with improved predictive capabilities. The improved predictive capability and generalisation ability of SVMR models, together with the ability to improve the accuracy of prediction by refining the dataset used for training, make the use of SVMR models more attractive.
Coupled S/O models are efficient tools that are used for designing multi-objective coastal aquifer management strategies. This study applies a regional-scale coupled S/O methodology with a Pareto front clustering technique to prescribe optimal groundwater withdrawal patterns from the Bonriki aquifer in the Pacific Island of Kiribati. A numerical simulation model is developed, calibrated and validated using field data from the Bonriki aquifer. For computational feasibility, SVMR surrogate models are trained and tested utilizing input-output datasets generated using the flow and transport numerical simulation model. The developed surrogate models were externally coupled with a multi-objective genetic algorithm optimization (MOGA) model, as a substitute for the numerical model. The study area consisted of freshwater pumping wells for extracting groundwater. Pumping from barrier wells installed along the coastlines is also considered as a management option to hydraulically control saltwater intrusion. The objective of the multi-objective management model was to maximise pumping from production wells and minimize pumping from barrier wells (which provide a hydraulic barrier) to ensure that the water quality at different monitoring locations remains within pre-specified limits. The executed multi-objective coupled S/O model generated 700 Pareto-optimal solutions. Analysing a large set of Pareto-optimal solution is a challenging task for the decision-makers. Hence, the k-means clustering technique was utilized to reduce the large Pareto-optimal solution set and help solve the large-scale saltwater intrusion problem in the Bonriki aquifer.
The S/O-based management models have delivered optimal saltwater intrusion management strategies. However, at times, uncertainties in the numerical simulation model due to uncertain aquifer parameters are not incorporated into the management models. The present study explicitly incorporates aquifer parameter uncertainty into a multi-objective management model for the optimal design of groundwater pumping strategies from the unconfined Bonriki aquifer. To achieve computational efficiency and feasibility of the management model, the calibrated numerical simulation model in the S/O model was is replaced with ensembles of SVMR surrogate models. Each SVMR standalone surrogate model in the ensemble is constructed using datasets from different numerical simulation models with different hydraulic conductivity and porosity values. These ensemble SVMR models were coupled to the MOGA model to solve the Bonriki aquifer management problem for ensuring sustainable withdrawal rates that maintain specified salinity limits. The executed optimization model presented a Pareto-front with 600 non-dominated optimal trade-off pumping solutions. The reliability of the management model, established after validation of the optimal solution results, suggests that the implemented constraints of the optimization problem were satisfied; i.e., the salinities at monitoring locations remained within the pre-specified limits.
The correct implementation of a prescribed optimal management strategy based on the coupled S/O model is always a concern for decision-makers. The management strategy actually implemented in the field sometimes deviates from the recommended optimal strategy, resulting in field-level deviations. Monitoring such field-level deviations during actual implementation of the recommended optimal management strategy and sequentially updating the strategy using feedback information is an important step towards adaptive management of coastal groundwater resources. In this study, a three-phase adaptive management framework for a coastal aquifer subjected to saltwater intrusion is applied and evaluated for a regional-scale coastal aquifer study area. The methodology adopted includes three sequential components. First, an optimal management strategy (consisting of groundwater extraction from production and barrier wells) is derived and implemented for the optimal management of the aquifer. The implemented management strategy is obtained by solving a homogeneous ensemble-based coupled S/O model. Second, a regional-scale optimal monitoring network is designed for the aquifer system, which considers possible user noncompliance of a recommended management strategy and uncertainty in aquifer parameter estimates. A new monitoring network design is formulated to ensure that candidate monitoring wells are placed at high risk (highly contaminated) locations. In addition, a k-means clustering methodology is utilized to select candidate monitoring wells in areas representative of the entire model domain. Finally, feedback information in the form of salinity measurements at monitoring wells is used to sequentially modify pumping strategies for future time periods in the management horizon. The developed adaptive management framework is evaluated by applying it to the Bonriki aquifer system. Overall, the results of this study suggest that the implemented adaptive management strategy has the potential to address practical implementation issues arising due to user noncompliance, as well as deviations between predicted and actual consequences of implementing a management strategy, and uncertainty in aquifer parameters.
The use of ensemble prediction models is known to be more accurate standalone prediction models. The present study develops and utilises homogeneous and heterogeneous ensemble models based on several standalone evolutionary algorithms, including artificial neural networks (ANN), GP, SVMR and Gaussian process regression (GPR). These models are used to predict groundwater salinity in the Bonriki aquifer. Standalone and ensemble prediction models are trained and validated using identical pumping and salinity concentration datasets generated by solving numerical 3D transient density-dependent coastal aquifer flow and transport numerical simulation models. After validation, the ensemble models are used to predict salinity concentration at selected monitoring wells in the modelled aquifer under variable groundwater pumping conditions. The predictive capabilities of the developed ensemble models are quantified using standard statistical procedures. The performance evaluation results suggest that the predictive capabilities of the standalone prediction models (ANN, GP, SVMR and GPR) are comparable to those of the groundwater variable-density flow and salt transport numerical simulation model. However, GPR standalone models had better predictive capabilities than the other standalone models. Also, SVMR and GPR standalone models were more efficient (in terms of computational training time) than other standalone models. In terms of ensemble models, the performance of the homogeneous GPR ensemble model was found to be superior to that of the other homogeneous and heterogeneous ensemble models.
Employing data-driven predictive models as replacements for complex groundwater flow and transport models enables the prediction of future scenarios and also helps save computational time, effort and requirements when developing optimal coastal aquifer management strategies based on coupled S/O models. In this study, a new data-driven model, namely Group method for data handling (GMDH) approach is developed and utilized to predict salinity concentration in a coastal aquifer and, simultaneously, determine the most influential input predictor variables (pumping rates) that had the most impact onto the outcomes (salinity at monitoring locations). To confirm the importance of variables, three tests are conducted, in which new GMDH models are constructed using subsets of the original datasets. In TEST 1, new GMDH models are constructed using a set of most influential variables only. In TEST 2, a subset of 20 variables (10 most and 10 least influential variables) are used to develop new GMDH models. In TEST 3, a subset of the least influential variables is used to develop GMDH models. A performance evaluation demonstrates that the GMDH models developed using the entire dataset have reasonable predictive accuracy and efficiency. A comparison of the performance evaluations of the three tests highlights the importance of appropriately selecting input pumping rates when developing predictive models. These results suggest that incorporating the least influential variables decreases model accuracy; thus, only considering the most influential variables in salinity prediction models is beneficial and appropriate.
This study also investigated the efficiency and viability of using artificial freshwater recharge (AFR) to increase fresh groundwater pumping rates from production wells. First, the effect of AFR on the inland encroachment of saline water is quantified for existing scenarios. Specifically, groundwater head and salinity differences at monitoring locations before and after artificial recharge are presented. Second, a multi-objective management model incorporating groundwater pumping and AFR is implemented to control groundwater salinization in an illustrative coastal aquifer system. A coupled SVMR-MOGA model is developed for prescribing optimal management strategies that incorporate AFR and groundwater pumping wells. The Pareto-optimal front obtained from the SVMR-MOGA optimization model presents a set of optimal solutions for the sustainable management of the coastal aquifer. The pumping strategies obtained as Pareto-optimal solutions with and without freshwater recharge shows that saltwater intrusion is sensitive to AFR. Also, the hydraulic head lenses created by AFR can be used as one practical option to control saltwater intrusion. The developed 3D saltwater intrusion model, the predictive capabilities of the developed SVMR models, and the feasibility of using the proposed coupled multi-objective SVMR-MOGA optimization model make the proposed methodology potentially suitable for solving large-scale regional saltwater intrusion management problems.
Overall, the development and evaluation of various groundwater numerical simulation models, predictive models, multi-objective management strategies and adaptive methodologies will provide decision-makers with tools for the sustainable management of coastal aquifers. It is envisioned that the outcomes of this research will provide useful information to groundwater managers and stakeholders, and offer potential resolutions to policy-makers regarding the sustainable management of groundwater resources. The real-life case study of the Bonriki aquifer presented in this study provides the scientific community with a broader understanding of groundwater resource issues in coastal aquifers and establishes the practical utility of the developed management strategies
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
The ongoing advancements in network architecture design have led to
remarkable achievements in deep learning across various challenging computer
vision tasks. Meanwhile, the development of neural architecture search (NAS)
has provided promising approaches to automating the design of network
architectures for lower prediction error. Recently, the emerging application
scenarios of deep learning have raised higher demands for network architectures
considering multiple design criteria: number of parameters/floating-point
operations, and inference latency, among others. From an optimization point of
view, the NAS tasks involving multiple design criteria are intrinsically
multiobjective optimization problems; hence, it is reasonable to adopt
evolutionary multiobjective optimization (EMO) algorithms for tackling them.
Nonetheless, there is still a clear gap confining the related research along
this pathway: on the one hand, there is a lack of a general problem formulation
of NAS tasks from an optimization point of view; on the other hand, there are
challenges in conducting benchmark assessments of EMO algorithms on NAS tasks.
To bridge the gap: (i) we formulate NAS tasks into general multi-objective
optimization problems and analyze the complex characteristics from an
optimization point of view; (ii) we present an end-to-end pipeline, dubbed
, to generate benchmark test problems for EMO algorithms to
run efficiently -- without the requirement of GPUs or Pytorch/Tensorflow; (iii)
we instantiate two test suites comprehensively covering two datasets, seven
search spaces, and three hardware devices, involving up to eight objectives.
Based on the above, we validate the proposed test suites using six
representative EMO algorithms and provide some empirical analyses. The code of
is available from
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