A hybrid multi objective cellular spotted hyena optimizer for wellbore trajectory optimization

Cost and safety are critical factors in the oil and gas industry for optimizing wellbore trajectory, which is a constrained and nonlinear optimization problem. In this work, the wellbore trajectory is optimized using the true measured depth, well profile energy, and torque. Numerous metaheuristic algorithms were employed to optimize these objectives by tuning 17 constrained variables, with notable drawbacks including decreased exploitation/exploration capability, local optima trapping, non-uniform distribution of non-dominated solutions, and inability to track isolated minima. The purpose of this work is to propose a modified multi-objective cellular spotted hyena algorithm (MOCSHOPSO) for optimizing true measured depth, well profile energy, and torque. To overcome the aforementioned difficulties, the modification incorporates cellular automata (CA) and particle swarm optimization (PSO). By adding CA, the SHO\u27s exploration phase is enhanced, and the SHO\u27s hunting mechanisms are modified with PSO\u27s velocity update property. Several geophysical and operational constraints have been utilized during trajectory optimization and data has been collected from the Gulf of Suez oil field. The proposed algorithm was compared with the standard methods (MOCPSO, MOSHO, MOCGWO) and observed significant improvements in terms of better distribution of non-dominated solutions, better-searching capability, a minimum number of isolated minima, and better Pareto optimal front. These significant improvements were validated by analysing the algorithms in terms of some statistical analysis, such as IGD, MS, SP, and ER. The proposed algorithm has obtained the lowest values in IGD, SP and ER, on the other side highest values in MS. Finally, an adaptive neighbourhood mechanism has been proposed which showed better performance than the fixed neighbourhood topology such as L5, L9, C9, C13, C21, and C25. Hopefully, this newly proposed modified algorithm will pave the way for better wellbore trajectory optimization

THE BEES’ ALGORITHM FOR DESIGN OPTIMIZATION OF A GRIPPER MECHANISM

In this paper, a gripper mechanism is optimized by using bees’ algorithm (BA) to compare with Non-dominated Sorting Genetic Algorithm version II (NSGA-II). The procedure of BA is proposed. The superiority of BA is illustrated by using results in figures and tables. A sensitivity analysis using correlation test is executed. The effectiveness coefficients of design variable for the objectives are provided. Consequently, the effectual design variables and the genuine searching method of BA are clearly evaluated and discussed. The BA provides dispersed and the least crowded Pareto Front population for solution in the shortest duration. Therefore, the best solutions are selected based on curve fitting. The closest solutions to the fitted curve are selected as the best in the region

An improved grey wolf with whale algorithm for optimization functions

The Grey Wolf Optimization (GWO) is a nature-inspired, meta-heuristic search optimization algorithm. It follows the social hierarchical structure of a wolf pack and their ability to hunt in packs. Since its inception in 2014, GWO is able to successfully solve several optimization problems and has shown better convergence than the Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), and Evolutionary Programming (EP). Despite providing successful solutions to optimization problems, GWO has an inherent problem of poor exploration capability. The position-update equation in GWO mostly relies on the information provided by the previous solutions to generate new candidate solutions which result in poor exploration activity. Therefore, to overcome the problem of poor exploration in the GWO the exploration part of the Whale optimization algorithm (WOA) is integrated in it. The resultant Grey Wolf Whale Optimization Algorithm (GWWOA) offers better exploration ability and is able to solve the optimization problems to find the most optimal solution in search space. The performance of the proposed algorithm is tested and evaluated on five benchmarked unimodal and five multimodal functions. The simulation results show that the proposed GWWOA is able to find a fine balance between exploration and exploitation capabilities during convergence to global minima as compared to the standard GWO and WOA algorithms

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Multi-Objective Optimization in Metabolomics/Computational Intelligence

The development of reliable computational models for detecting non-linear patterns encased in throughput datasets and characterizing them into phenotypic classes has been of particular interest and comprises dynamic studies in metabolomics and other disciplines that are encompassed within the omics science. Some of the clinical conditions that have been associated with these studies include metabotypes in cancer, in ammatory bowel disease (IBD), asthma, diabetes, traumatic brain injury (TBI), metabolic syndrome, and Parkinson's disease, just to mention a few. The traction in this domain is attributable to the advancements in the procedures involved in 1H NMR-linked datasets acquisition, which have fuelled the generation of a wide abundance of datasets. Throughput datasets generated by modern 1H NMR spectrometers are often characterized with features that are uninformative, redundant and inherently correlated. This renders it di cult for conventional multivariate analysis techniques to e ciently capture important signals and patterns. Therefore, the work covered in this research thesis provides novel alternative techniques to address the limitations of current analytical pipelines. This work delineates 13 variants of population-based nature inspired metaheuristic optimization algorithms which were further developed in this thesis as wrapper-based feature selection optimizers. The optimizers were then evaluated and benchmarked against each other through numerical experiments. Large-scale 1H NMR-linked datasets emerging from three disease studies were employed for the evaluations. The rst is a study in patients diagnosed with Malan syndrome; an autosomal dominant inherited disorder marked by a distinctive facial appearance, learning disabilities, and gigantism culminating in tall stature and macrocephaly, also referred to as cerebral gigantism. Another study involved Niemann-Pick Type C1 (NP-C1), a rare progressive neurodegenerative condition marked by intracellular accrual of cholesterol and complex lipids including sphingolipids and phospholipids in the endosomal/lysosomal system. The third study involved sore throat investigation in human (also known as `pharyngitis'); an acute infection of the upper respiratory tract that a ects the respiratory mucosa of the throat. In all three cases, samples from pathologically-con rmed cohorts with corresponding controls were acquired, and metabolomics investigations were performed using 1H NMR technique. Thereafter, computational optimizations were conducted on all three high-dimensional datasets that were generated from the disease studies outlined, so that key biomarkers and most e cient optimizers were identi ed in each study. The clinical and biochemical signi cance of the results arising from this work were discussed and highlighted

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

IntroductionAtopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians’ subjective judgment, which may be missed or misdiagnosed sometimes.MethodsThis paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population’s diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.ResultsTo prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.DiscussionThe 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD

Codon usage adaptation in prokaryotic genomes

La tesi esta basada en l'adaptació de l'ús de codons a genomes procariotes, especialment l'adaptació de l'ús de codons a una alta expressió. Hi ha un grup de genomes procariotes, els quals estan sota una selecció traduccional, que tenen un grup de gens amb un ús de codons esbiaixat de la resta de gens del genoma i adaptats a l'abundància dels tRNA. Hem desenvolupat un nou algoritme per a avaluar si un genoma esta sota selecció traduccional i predir els gens altament expressat de tots els genomes sota selecció traduccional. Aquestes prediccions són públiques a la base de dades HEG-DB (http://genomes.urv.cat/HEG-DB), la qual s'ha publicat a la revista Nucleic Acids Research. Les prediccions de gens altament expressats s'han fet servir com a filtre en les prediccions de gens adquirits per transferència horitzontal, ja que els gens altament expressats molts cops son predits com a falsos positius en la predicció de gens adquirits. Amb les dades de la predicció de gens altament expressats, també hem desenvolupat una nova eina Bioinformàtica, anomenada OPTIMIZER (http://genomes.urv.cat/OPTIMIZER) i publicada al Nucleic Acids Research, per tal d'optimitzar l'ús de codons d'un gen per a incrementar la seva expressió en experiments d'expressió heteròloga de proteïnes. També hem estudiat un cas particular d'adaptació de l'ús de codons. El cas de l' 'amelioration', que és l'adaptació de l'ús de codons que pateix un gen inserit en un genoma hoste. Aquest cas l'hem estudiat amb els gens mitocondrials que varen saltar al genoma nuclear i varen haver d'adaptar el seu us de codons mitocondrial a l'ús de codons del genoma nuclear. Per tal d'estudiar l''amelioration', hem desenvolupat un nou índex anomenat CAI esperat (eCAI) i una nova eina Bioinformàtica anomenada CAIcal (http://genomes.urv.cat/CAIcal), que està en procés de revisió a la revista BMC Bioinformatics. Analitzant l'anàlisi de l'ús de codons dels genomes completament sequenciats vàrem realitzar una troballa que s'aparta una mica del tema central de la tesi. Vàrem veure que els genomes que estan adaptats a la (hiper)termofília tenen un patró de l'ús de codons i d'aminoàcids diferent a la resta de genomes (mesòfils). Aquest fet ens ha permès descobrir casos de guany i pèrdua (recents i antics) de la capacitat d'adaptació termofílica en genomes procariotes. Aquests resultats han donat lloc a una publicació a la revista Trends in Genetics. Durant la tesi he realitzat una estada de 4 mesos (Febrer - Juny, 2006) en el laboratori de bioinformàtica del departament de biologia de la universitat nacional d'Irlanda a Maynooth sota la supervisió del Dr James McInerney on vaig desenvolupar un nou programa per a la comparació d'arbres filogenètics anomenat TOPD/FMTS (http://genomes.urv.cat/topd) el qual està publicat a la revista Bioinformatics.This thesis is based in codon usage adaptation in prokaryotic genomes, especially the codon usage adaptation to a high expression. In genomes under translational selection, the group of highly expressed genes has a codon usage adapted to the most abundant tRNA species. We have developed a new iterative algorithm which predicts a group of highly expressed genes in genomes under translational selection by using the Codon Adaptation Index and the group of ribosomal protein genes as a seed. We have developed a new genomic database, called HEG-DB, to store genes that are predicted as highly expressed in prokaryotic complete genomes under strong translational selection. The database is freely available at http://genomes.urv.cat/HEG-DB and it has been published in Nucleic Acids Research. The predicted highly expressed genes are used as an initial filter to reduce the number of false positives of the Horizontal Gene Transfer Database, due to highly expressed genes are usually false positive in predictions of acquired genes. We have developed a new web sever, called OPTIMIZER (http://genomes.urv.cat/OPTIMIZER), which has been published in Nucleic Acids Research, to optimize the codon usage of DNA or RNA sequences. This new web server can be used to predict and optimize the level expression of a gene in heterologous gene expression or to express new genes that confer new metabolic capabilities in a given species. We have also analyzed an especial case of codon usage adaptation, which is called 'amelioration'. The 'amelioration' is the adaptation of foreign genes to a new genome. This is the case of mitochondrial genes encoded in the human nuclear genome and originally encoded in the proto-mitochondria. To test the 'amelioration' process we have developed an expected value of CAI (eCAI) to find out whether the differences in the CAI are statistically significant or whether they are the product of biased nucleotide and/or amino acid composition and a new bioinformatics tool called CAIcal (http://genomes.urv.cat/CAIcal). We have also analyzed the evolution of thermophilic adaptation in prokaryotes and we suggest that the amino acid composition signature in thermophilic organisms is a consequence of or an adaptation to living at high temperatures, not its cause. Our findings suggest that there have been several cases where the capacity for thermophilic adaptation has been gained or lost throughout the evolution of prokaryotes. These results have been published in Trends in Genetics. During my thesis I have worked for four months in the Bioinformatics Laboratory of the Biology Department at the National University of Ireland under the supervision of Dr James O. McInerney where I developed a new software program to compare phylogenetic trees called TOPD/FMTS (http://genomes.urv.cat/topd), that has been published in Bioinformatics