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[en] These are slides on genetic algorithm for nuclear data evaluation. The following is covered: initial population, fitness (outer loop), calculate fitness, selection (first part of inner loop), reproduction (second part of inner loop), solution, and examples.
[en] The hub location and revenue management problems are two interesting research fields in the transportation and network design studies. The hub location model designs the transportation network structure and the revenue management model allocates the network’s capacity for customer classes based on their sensitivity to prices. In this paper, we consider the integrated hub location and revenue management problem in the airline industry to maximize the revenue of transportation network and minimize hub installation costs. p hubs have been chosen from a set of n nodes and connected to a central node based on star–star network design. The capacity of the installed links between the hubs and the central node and the hubs and non-hub nodes is limited. This limited capacity has been allocated to the stochastic demands of customer classes using the revenue management approach. A two-stage stochastic model has been derived to determine the locations of hubs and protection levels in the ticket sale process, and an efficient modification of genetic algorithm has been proposed for the large-scale problems. Numerical experiments have been carried out to assess the effectiveness of the proposed algorithm.
[en] This report describes a unified viscoplastic model for Grade 91 suitable for use with the Section III, Division 5 design by inelastic analysis procedures in the ASME Boiler and Pressure Vessel Code. The report identifies several key features a successful design inelastic model must capture along with critical aspects of the response of Grade 91 for elevated temperature loading conditions typical of expected reactor operating conditions. An inelastic model for Code design must capture the average response of a material including heat-to-heat and product form variations. This work develops a method for calibrating such a model to a large experimental database using genetic algorithm optimization. Finally, the report compares the final model to several specialized experimental tests to validate that it will be suitable for use in high temperature structural design. The techniques developed here can be applied to the remaining Section III, Division 5, Class A materials to develop a nonmandatory appendix to the Code describing inelastic material models suitable for use with the design by inelastic analysis procedures.
[en] In this study, the Load ESTimator (LOADEST) and eight-parameter regression models were evaluated to estimate instantaneous pollutant loads under various criteria and optimization methods. As shown in the results, LOADEST, commonly used in interpolating pollutant loads, could not necessarily provide the best results with the automatically selected regression model. The various regression models in LOADEST need to be considered to find the best solution based on the characteristics of watersheds. The recently developed eight-parameter model integrated with a genetic algorithm (GA) and the gradient descent method (GDM) was also compared with LOADEST, indicating that the eight-parameter model performed better than LOADEST; however, depending on whether the eight-parameter model was used for calibration or validation, its performance varied. The eight-parameter model with GDM could reproduce the nitrogen loads properly outside the calibration period (validation). Furthermore, the accuracy and precision of model estimations were evaluated using various criteria (e.g., R2, gradient, and constant of a linear regression line). The results showed higher precisions with the R2 values close to 1.0 in LOADEST and better accuracy with the constants (in linear regression line) close to 0.0 in the eight-parameter model with GDM. Hence, on the basis of these findings, we recommend that users need to evaluate the regression models under various criteria and calibration methods to ensure more accurate and precise results for nitrogen load estimations.
[en] Highlights: • Present a multi-objective optimization approach for facility location problem to consider intentional attacks. • Describe a simultaneous game for facility protection under secrecy of information. • Prove secrecy to be better than truthful disclosure of information. • Present an approach for solving the proposed simultaneous game. • Use Swain dataset and London dataset as illustration of the proposed approach. - Abstract: To preserve continued effective performance of facilities, their protection against intentional attacks needs to be considered while determining optimal facility location solutions. We propose a simultaneous game between a defender and an attacker to study facility protection against intentional attacks while keeping the information about protection resource allocation secret. To deal with the complexity of solving the proposed simultaneous game, we employ an algorithm with necessary adaptations to identify its mixed-strategy Nash equilibrium solution, which is used to evaluate the disruption inflicted by intentional attacks on the efficiency of a facility location solution. The facility location problem with protection against intentional attacks is then modeled as a multi-objective optimization problem, in order to balance the cost of opening facilities and the efficiency of facilities with and without facility failures inflicted by intentional attacks. MO-PSDA, a multi-objective evolutionary algorithm, is employed to solve the proposed multi-objective optimization problem.
[en] Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.
[en] Supply chain management is monitoring of activities involved in supply chain, for integrating and coordinating between suppliers, manufacturing, inventory and transportation both within and among members. The ultimate aim of supply chains is to reduce costs and increase market coverage. Procuring and purchasing requested items in a timely manner are the two most important issues for supply chain stockholders. Group purchasing is one of the purchasing strategies in supply chains. It offers great potential by ordering large volumes to decrease expenses which can increase services to customers. A clustering optimization approach is employed to model group purchasing for a set of pharmacies in the field of healthcare. The proposed model determines a cooperation strategy based on factors such as distance between pharmacies and procurement expenditure in this network. Moreover, the proposed multi-objective group purchasing model is optimized using both goal programming and non-dominated sorting genetic algorithm. To illustrate the application of the proposed model, designing a purchasing group organization for Chalus city pharmacies is investigated. Purchasing groups are established in the way that sum of pharmacies ordering quantity has been located in the second or the third level of discount rate. Thus, the results show that GPOs can take the advantage of this cooperation.
[en] An algorithm is developed to find Weak Stability Boundary transfer trajectories to Moon in high fidelity force model using forward propagation. The trajectory starts from an Earth Parking Orbit (circular or elliptical). The algorithm varies the control parameters at Earth Parking Orbit and on the way to Moon to arrive at a ballistic capture trajectory at Moon. Forward propagation helps to satisfy launch vehicle’s maximum payload constraints. Using this algorithm, a number of test cases are evaluated and detailed analysis of capture orbits is presented.
[en] This paper proposes a novel approach of coordinating decisions in an integrated supply chain (ISC): coordinating order acceptance (OA) and batch delivery (BD) due to round trip transportation (RTT) and using third-party logistics (3PL) vehicles. The paper aims at trading off among accepted orders revenue, delivery costs as well as any penalties incurred in the ISC to maximize the total benefit. A novel mixed-integer programming is proposed for the problem. In addition, the paper provides a heuristic to form batches and develops a hybrid evolutionary computation algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) to solve the problem. An information sharing mechanism is improved and applied. To explore and to locate the proposed PSO in a better neighborhood, a local search is proposed. Taguchi experimental design is utilized to set the appropriate values of the algorithms’ parameters and random instances are generated to evaluate the performance of the algorithms. The paper investigates the profitability sensitivity of the problem to parameters and analyzes the effect of the changes in the parameters on the performance of our proposed algorithms. The attained results show the appropriate performance of our algorithms.
[en] The article A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming, written by Suning Liu and Haiyun Shi was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 16 December 2018 without open access.