MOEA/D with Tabu Search for multiobjective permutation flow shop scheduling problems

Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) decomposes a multiobjective optimisation problem into a number of single-objective problems and optimises them in a collaborative manner. This paper investigates how to use Tabu Search (TS), a well-studied single objective heuristic to enhance MOEA/D performance. In our proposed approach, the TS is applied to these subproblems with the aim to escape from local optimal solutions. The experimental studies have shown that MOEA/D with TS outperforms the classical MOEA/D on multiobjective permutation flow shop scheduling problems. It also have demonstrated that use of problem specific knowledge can significantly improve the algorithm performance

Vehicle Routing Optimization for Non-Profit Organization Systems

The distributor management system has long been a challenge for many organizations and companies. Overall, successful distribution involves several moving entities and methods, requiring a resilient distribution management strategy powered by data analysis. For nonprofit organizations, the distribution system requires efficient distribution and management. This includes minimizing time, distance, and cost. As a consequence, service quality and financial efficiency can be achieved. This paper proposes a methodology to tackle the vehicle routing problems (VRP) faced by nonprofit organizations. The methodology consists of four subsequent approaches—greedy, intraroute, interroute, and tabu search—to improve the functionality and performance of nonprofit organizations. The methodology was validated by applying it to a real nonprofit organization. Furthermore, the proposed system was compared to another state-of-the-art system; the achieved results were satisfactory and suggest that this methodology is capable of handling the VRP accordingly, improving the functionality and performance of nonprofit organizations

Molecular Basis of MADS-Box Protein-Protein Interactions

Scientific summary (English) In this study we aimed to understand the complex protein network interactions of one of the largest plant transcription factors families, the MADS-box genes. Through high throughput screening via yeast two/three hybrid and electro mobility shift assays, we were able to establish a good understanding of the interaction specificity in extant protein networks. To obtain this understanding, we have used resurrected ancestral MADS-box proteins at different evolutionary time points, around key whole genome duplication events. This highlighted the role of hub proteins in complex protein interaction networks. The evolutionary data resulted in a better understanding of key protein-protein interactions at the molecular level, where the effect of a mutation/s along the evolutionary trajectory could be traced back. By a number of mutagenesis assays, we were able to confirm the effect of these mutations, when introduced into extant proteins in the network. For the hub protein SEP3, we were able to illustrate that this hub protein has gained specificity during its evolution through proline accumulation, which was associated with a reduction in flexibility in the extant protein compared to its ancestors. This approach of testing protein-protein interaction in ancestral networks was quite novel. When combined with the reciprocal swapping experiment of certain protein members between networks of different ages, it proved to be a very useful and powerful technique to understand network evolution. This approach can be applied to any protein network providing that there are enough sequenced genes of its members to reconstruct their ancestral proteins. We augmented our wet lab results with several in silico simulations to illustrate the folding dynamics at the molecular level of the dimerization domain of the hub protein SEP3. This enabled us to make more solid conclusions regarding the effect of protein flexibility on protein affinity and specificity and illustrated how flexibility affects the conformational space and the folding energy landscape. We were able to illustrate that these protein-protein interactions follow an induced fit model rather than key-lock model, the dimerization process is dynamic, and mutations that affect the energy landscape between different conformers can contribute to the interaction specificity and affinity equally as mutation on the interaction interface itself. By studying a second case of protein evolution in the MADS-box protein network, the SHORT VEGETATIVE PHASE (SVP) protein, we were able to recognize a different molecular mechanism that was selected during evolution. A new subdomain through insertion has enabled SVP to gain more interaction in the protein network. This shows how complex and diverse are the protein-protein interactions even in a family of close paralogs such as the MADS-box proteins. This combination of wet-lab/dry-lab techniques will become more necessary and feasible in the future. In addition to large-scale high throughput mutational scans and protein-protein interaction assays. Enabling us to achieve a holistic understanding from the molecular to phenotype level.Acknowledgments........................................ix List of Abbreviations..................................x Thesis overview........................................xi Chapter 1: Introduction 1.1 Chapter 1: objectives..............................1 1.2 Proteins structure and function: overview..........1 1.3 Protein-Protein interactions: overview.............4 1.4 Protein-protein interaction assays: overview.......7 1.5 MADS-domain proteins family: overview..............9 1.6 MIKC domains role in proteins-protein interactions.10 1.7 Main MADS-domain proteins interactions in plants: overview...............................................13 1.7.1 Flowering quartet model..........................14 Chapter 2: Evolution of protein interactions in MADS-domain protein network 2.1 Chapter 2: objectives..............................19 2.2 Ancestral proteins reconstruction: overview........19 2.3 The role SEP3 as a hub protein in MADS-domain proteins networks......................................23 2.4 SEP3 evolved from promiscuity to specificity.......26 2.5 SEP3 lost conformational flexibility by acquiring proline residues in the I-domain.......................28 2.6 Large conformational changes between bound and free SEP3 K-domains.........................................32 2.7 SEP3 K domain loop conformational dynamics fine tune its hub property.......................................34 2.8 Not all MADS-domain proteins are equal (evolution of SVP).....................................38 2.9 Evolutionary molecular strategies: conclusion......40 2.10 Chapter 2: materials and methods..................42 Chapter 3: Predicting protein 3D structure from amino acids sequence 3.1 Chapter 3: objectives..............................49 3.2 Predicting protein 3D structure from amino acids sequence: Overview.....................................49 3.2.1 Iterative fragment assembly......................50 3.2.2 ab initio modelling..............................52 3.2.3 Protein 3D Structure prediction using evolutionary couplings..............................................52 3.3 Mutagenesis-high-throughput Y2H assay to reveal protein 3D structure...................................58 3.3.1 Choosing and validating the selection system.....59 3.4 in silico simulation preparatory experiment........60 3.4.1 Residue-residue contact map and size compatibility index..................................................63 3.4.2 Residue-residue physicochemical properties compatibility index....................................64 3.4.3 Direct coupling analysis of in silico selected libraries..............................................68 3.5 Deep Mutational scan experimental procedures.......69 3.5.1 Library design and preparation...................70 3.5.2 Mutagenesis via error-prone PCR..................70 3.5.3 Transformation into electrocompetent E.coli cells..................................................71 3.5.4 High-efficiency transformation of plasmid library into yeast.............................................72 3.5.5 Selection and NGS sequencing.....................74 3.5.6 Nested PCR with illumina next-generation sequencing compatible primers..........................75 3.5.7 Next generation sequencing results...............78 3.5.8 Selected library illumina reads processing.......82 3.6 in vivo expression of SEP3 and SVP proteins........90 3.7 Reconstructing protein 3D structure from amino acids sequence: conclusion...................................91 Chapter 4: Evolution of Jasmonate-Auxin receptors specificity (an in silico study) 4.1 Chapter 4: objectives..............................93 4.2 Plant hormones (Auxin and Jasmonate) co-receptors: overview...............................................93 4.3 Reconstructed ancestral genes JAZ-AUX receptors to understand evolution of specificity....................98 4.4 Auxin (IAA) and Jasmonate (JA-Ile and COR) in silico docking................................................101 4.5 Future prospect and conclusion.....................109 Chapter 5: Thesis conclusion 5.1.1 Dynamic molecular evolution: recapitulation......113 5.1.2 Dynamic molecular evolution: prospects...........114 5.2.1 Protein structure prediction: recapitulation.....115 5.2.2 Protein structure prediction: prospects..........116 Appendices.............................................120 References.............................................150status: publishe

Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia

The world’s technological and economic advancements have led to a sharp increase in the demand for electrical energy. Saudi Arabia is experiencing rapid economic and demographic growth, which is resulting in higher energy needs. The limits of fossil fuel reserves and their disruption to the environment have motivated the pursuit of alternative energy options such as wind energy. In order to regulate the power system to maintain safe and dependable operation, projections of current and daily power generation are crucial. Thus, this work focuses on wind power prediction and the statistical analysis of wind characteristics using wind data from a meteorological station in Makkah, Saudi Arabia. The data were collected over four years from January 2015 to July 2018. More than twelve thousand data points were collected and analyzed. Layout and wake effect studies were carried out. Furthermore, the near wake length downstream from the rotor disc between 1 and 5 rotor diameters (1D to 5D) was taken into account. Five robust machine learning algorithms were implemented to estimate the potential wind power production from a wind farm in Makkah, Saudi Arabia. The relationship between the wind speed and power produced for each season was carefully studied. Due to the variability in the wind speeds, the power production fluctuated much more in the winter. The higher the wind speed, the more significant the difference in energy production between the five farm layouts, and vice versa, whereas at a low wind speed, there was no significant difference in the power production in all of the near wake lengths of the 1D to 5D rotor diameters downstream from the rotor disc. Among the utilized prediction models, the decision tree regression was found to have the best accuracy values in all four utilized evaluation metrics, with 0.994 in R-squared, 0.025 in MAE, 0.273 in MSE, and 0.522 in RMSE. The obtained results were satisfactory and provide support for the construction of several wind farms, producing hundreds of megawatts, in Saudi Arabia, particularly in the Makkah Region

Vehicle Routing Optimization for Non-Profit Organization Systems

The distributor management system has long been a challenge for many organizations and companies. Overall, successful distribution involves several moving entities and methods, requiring a resilient distribution management strategy powered by data analysis. For nonprofit organizations, the distribution system requires efficient distribution and management. This includes minimizing time, distance, and cost. As a consequence, service quality and financial efficiency can be achieved. This paper proposes a methodology to tackle the vehicle routing problems (VRP) faced by nonprofit organizations. The methodology consists of four subsequent approaches—greedy, intraroute, interroute, and tabu search—to improve the functionality and performance of nonprofit organizations. The methodology was validated by applying it to a real nonprofit organization. Furthermore, the proposed system was compared to another state-of-the-art system; the achieved results were satisfactory and suggest that this methodology is capable of handling the VRP accordingly, improving the functionality and performance of nonprofit organizations

Hybridisation of decomposition and GRASP for combinatorial multiobjective optimisation

This paper proposes an idea of using heuristic local search procedures specific for single-objective optimisation in multiobjectie evolutionary algorithms (MOEAs). In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) hybridised with a multi-start single-objective metaheuristic called greedy randomised adaptive search procedure (GRASP). In our method a multiobjetive optimisation problem (MOP) is decomposed into a number of single-objecive subproblems and optimised in parallel by using neighbourhood information. The proposed GRASP alternates between subproblems to help them escape local Pareto optimal solutions. Experimental results have demonstrated that MOEA/D with GRASP outperforms the classical MOEA/D algorithm on the multiobjective 0-1 knapsack problem that is commonly used in the literature. It has also demonstrated that the use of greedy genetic crossover can significantly improve the algorithm performance

OpToGen — A genetic algorithm based framework for optimal topology generation for linear networks

International audienc

Multiobjective evolutionary algorithms NSGA-II and NSGA-III for software product lines testing optimization

Software Product line (SPL) engineering methodology utilizes reusable components to generate a new system for a specific domain. In fact, the product line establishes requirements, reusable components, architecture, and shared products to develop new products’ functionalities. In order to maintain high quality, there is a need for a thorough testing process. Each product in SPL having a different number of features need to be tested. Hence, the testing process of SPL can utilize a multi-objective optimization algorithm to optimize the testing process. This research, reports on the performance of a multi-objective Evolutionary Algorithms Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and NSGA-III on Feature Models (FMs) to optimize SPL testing

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population