Sequential Genetic Algorithm Research Articles

Thermo-economic design, optimization, and evaluation of a novel zeotropic ORC with mixture composition adjustment during operation

Organic Rankine cycle (ORC) is a proposing technology that converting low temperature (usually lower than 200 °C) thermal energy into power. Mostly, the ORC is operated under off-design conditions and the operation performance is deteriorated remarkably from that achieved under rated condition. In the present study, a novel composition-adjustable zeotropic ORC is proposed. A liquid separation condenser-based unit, coupling the heat transfer intensification and mixture composition tuning, is conceptually designed, and integrated into the zeotropic ORC. A regulation strategy for working fluid composition is developed. A thermo-economic evaluation and optimization model and a mixture composition adjustment model are formulated to investigate the superiority of the proposed ORC. A sequential method and genetic algorithm (GA) are applied to conduct the thermodynamic optimization, component design, and thermo-economic optimization of the proposed ORC. A case study is conducted to validate the thermo-economic superiority of the proposed composition adjustable ORC driven by geothermal energy. Results show that the proposed ORC features 0.52% higher annual average net power output, 2.20% higher annual average thermal efficiency, and 21.43% lower average electricity production cost than conventional ORC. The fluid composition-regulating system can achieve the target composition within acceptable time.

Screening Strategies for Deafness Genes and Functional Outcomes in Cochlear Implant Patients.

To review the current state of knowledge about the influence of specific genetic mutations that cause sensorineural hearing loss (SNHL) on cochlear implant (CI) functional outcomes, and how this knowledge may be integrated into clinical practice. A multistep and sequential population-based genetic algorithm suitable for the identification of congenital SNHL mutations before CI placement is also examined. A review was performed of the English literature from 2000 to 2019 using PubMed regarding the influence of specific mutations on CI outcomes and the use of next-generation sequencing for genetic screening of CI patients. CI is an effective habilitation option for patients with severe-profound congenital SNHL. However, it is well known that CI outcomes show substantial inter-patient variation. Recent advances in genetic studies have improved our understanding of genotype-phenotype relationships for many of the mutations underlying congenital SNHL, and have explored how these relationships may account for some of the variance seen in CI performance outcomes. A sequential genetic screening strategy utilizing next-generation sequencing-based population-specific gene panels may allow for more efficient mutation identification before CI placement. Understanding the relationships between specific mutations and CI outcomes along with integrating routine comprehensive genetic testing into pre-CI evaluations will allow for more effective patient counseling and open the door for the development of mutation-specific treatment strategies.

Overall tidal farm optimal design–Application to the Alderney Race and the Fromveur Strait (France)

This article presents an overall optimization method of Tidal Energy Converters arrangement and their electrical connection topology. This methodology is implemented as a software tool and formulated as a bi-level programming where two sequential genetic algorithms are used. A semi-analytical method is adopted to evaluate the loss of production caused by the wake effect. Electrical connection costs as well as line losses are taken into account. This developed tool takes a large panel of variables into consideration and delivers an optimal number of turbines and their locations, optimal AC electrical architecture and optimal sizing of electrical equipment as its output. The optimization approach is applied to the Alderney Race and the Fromveur Strait which represent the first and the second greatest tidal potential in France, respectively. The results show that the energy production cost considerably differs from one site to another and it strongly depends on the size of the farm.

Evaluation and optimization of axial piston pump textured slipper bearings with spherical dimples based on hybrid genetic algorithm

The spherical dimple texture have been designed on the rough surface of slipper bearing for improving the lubrication performance in axial piston pump. In this work, we have investigated and optimized the structure parameters of textures to obtain minimum friction coefficient as well as maximum loading capacity. Optimization of the geometry parameters of dimple texture by the integration of a hybrid evolutionary optimization method based on the sequential quadratic programming and genetic algorithm. Parametric analysis is applied for the evaluation of the impact level of geometry parameters on lubrication performance. The results shows that hybrid genetic method can be used for the optimization of slipper bearing with spherical dimple textures to generate lower friction coefficient and greater capacity of load carrying. The carrying capacity and friction coefficient of slipper bearing demonstrate a 64.8% and 4.5% improvements after multi-objective optimization. When the texture radius and depth are set to 18 µm and 0.8 µm, there exists the greatest load carrying force and lowest friction coefficient. This work presents a key designing guide for axial piston pump textured slipper bearings.

A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit

Reducing CO2 emissions from fossil fuel fired power plants has been a major environmental concern over the last decade. Amongst various carbon capture and storage (CCS) technologies, the utilization of solvent-based post-combustion capture (PCC), played a major role in the reduction of CO2 emissions. This paper illustrates the development of machine learning models to predict the outputs of the PCC unit. A fine tree, Matérn Gaussian process regression (GPR), rational quadratic GPR, and squared exponential GPR models were developed and compared with a feed-forward artificial neural network (ANN) model. An accuracy of up to 98% in predicting the process outputs was achieved. Furthermore, the models were utilized to determine the optimum operating conditions for the process using a sequential quadratic programming algorithm (SQP) and genetic algorithm (GA). The use of the machine learning models has proven to be very useful since the complete mechanistic model is too large, and its runtime is too long to allow for rigorous optimal solutions. The machine learning models and optimization problems were developed and solved using MATLAB. The data used in this work was obtained from simulating the process using gPROMS process builder. The inputs of the model were selected to be reboiler duty, condenser duty, reboiler pressure, flow rate, temperature, and the pressure of the flue gas. The models were able to accurately predict the outputs of the process which are the system energy requirements (SER), capture rate (CR), and the purity of condenser outlet stream (PU).

A data-driven understanding of COVID-19 dynamics using sequential genetic algorithm based probabilistic cellular automata

COVID-19 pandemic is severely impacting the lives of billions across the globe. Even after taking massive protective measures like nation-wide lockdowns, discontinuation of international flight services, rigorous testing etc., the infection spreading is still growing steadily, causing thousands of deaths and serious socio-economic crisis. Thus, the identification of the major factors of this infection spreading dynamics is becoming crucial to minimize impact and lifetime of COVID-19 and any future pandemic. In this work, a probabilistic cellular automata based method has been employed to model the infection dynamics for a significant number of different countries. This study proposes that for an accurate data-driven modelling of this infection spread, cellular automata provides an excellent platform, with a sequential genetic algorithm for efficiently estimating the parameters of the dynamics. To the best of our knowledge, this is the first attempt to understand and interpret COVID-19 data using optimized cellular automata, through genetic algorithm. It has been demonstrated that the proposed methodology can be flexible and robust at the same time, and can be used to model the daily active cases, total number of infected people and total death cases through systematic parameter estimation. Elaborate analyses for COVID-19 statistics of forty countries from different continents have been performed, with markedly divergent time evolution of the infection spreading because of demographic and socioeconomic factors. The substantial predictive power of this model has been established with conclusions on the key players in this pandemic dynamics.

Parallel approaches of genetic algorithm in the MIC architecture of the Intel Xeon Phi

Today, genetic algorithms are widely used in many fields such as bioinformatics, computer science, artificial intelligence, finance ... Genetic algorithms are applied to create high quality solutions for complex optimization problems in the above industries. There have been many studies based on the proposed new hardware architecture that aims to speed up the execution of genetic algorithms as quickly as possible. Some studies suggest parallel genetic algorithms on systems with multicore CPUs and / or graphics processing units (GPUs). However, very few solutions propose a genetic algorithm that can be run on systems that use the new Intel Xeon Phi co-processor (Intel Many-Integrated Core (MIC) architecture). For that reason, we propose and develop the study of the genetic algorithm on high-performance computing systems with Intel Xeon Phi co-processors. This study will present the results of parallel approaches of genetic algorithm on one and more Intel Xeon Phi co-processors by the following methods: (i) Intel Xeon Phi programming model Offload and Native; and (ii) a combined model of MPI and OpenMP. The proposed genetic algorithm can find the optimal schedule for the energy-efficient scheduling problem of virtual machines on physical machines with the goal of minimization total energy consumption. The results of the simulations show the feasibility of implementing a genetic algorithm on one or many Intel Xeon Phi. Genetic algorithm on one or more distributed Intel Xeon Phi always results in faster algorithm execution time than sequential genetic algorithm and the ability to find better solutions using more Intel Xeon Phi. This research result can be applied to other meta-heuristic like TABU search, Ant Colony Optimization.

Genomics, Epigenetics, and Hearing Loss in Neurofibromatosis Type 2.

In this review, we discuss current knowledge about the genetics and epigenetics of vestibular schwannoma (VS) in relation to hearing loss. A multistep and sequential genetic algorithm suitable for the identification of Neurofibromatosis Type 2 (NF2) constitutional and somatic mutations is discussed. A review was performed of the English literature from 1990 to 2019 using PubMed regarding genetics and epigenetics of vestibular schwannoma and NF2. NF2 is a genetic disorder characterized by NF2 mutations that affect the function of a tumor suppressor called merlin. In particular, individuals with NF2 develop bilateral VS that can lead to hearing loss and even deafness. Recent advances in genetic and epigenetic studies have improved our understanding of the genotype-phenotype relationships that affect hearing in NF2 patients. Specific constitutional NF2 mutations including particular truncating, deletion, and missense mutations have been associated with poorer hearing outcomes and more severe clinical manifestations. Epigenetic events, such as DNA methylation and histone modifications, also contribute to the development and progression of hearing loss in NF2 patients. Furthermore, the accumulation of multiple NF2 and non-NF2 genetic and epigenetic abnormalities at the level of the tumor may contribute to worse hearing outcomes. Understanding genetic and epigenetic signatures in individual NF2 patients and particularly in each VS will allow us to develop novel gene therapies and precision medicine algorithms to preserve hearing in NF2 individuals.

Parallel genetic algorithm for N‐Queens problem based on message passing interface‐compute unified device architecture

AbstractN‐Queens problem derives three variants: obtaining a specific solution, obtaining a set of solutions and obtaining all solutions. The purpose of the variant I is to find a constructive solution, which has been solved. Variant III is aiming to find all solutions and the largest number of queens currently being resolved is 26. Variant II whose purpose is to obtain a set of solutions for larger‐scale problems relies on various intelligent algorithms. In this paper, we use a master‐slave model genetic algorithm that combines the idea of the evolutionary algorithm and simulated annealing algorithm to solve Variant III, and use a parallel fitness function based on compute unified device architecture. Experimental results show that our scheme achieved a maximum 60‐fold speedup over the single‐CPU counterpart. On this basis, a two‐level parallel genetic algorithm based on the island model and master‐slave model is implemented on the GPU cluster by using message passing interface technology. Using two‐node and three‐node GPU cluster, speedup of 1.46 and 2.01 are obtained on average over single‐node, respectively. Compared with the sequential genetic algorithm, the two‐level parallel genetic algorithm makes full use of the parallel computing power of GPU cluster in solving N‐Queen variant II and improves the performance by 99.19 times in the best case.

Analysis of credit-rating migrations with genetic algorithms

Modelling dependent credit-rating migrations of assets classified into M credit classes and S industries, M × S + 2M×S parameters have to be estimated. For a realistic choice of M and S, this number is huge and it greatly exceeds the number of available observations. To avoid brute-force calculations, we suggest sequential and parallel genetic algorithms. Considering a practically important combination of M = 7 and S = 6, the approach is tested on Standard and Poor's data.

A Parallel Genetic Algorithm Framework for Transportation Planning and Logistics Management

Small to medium sized transportation and logistics companies are usually constrained by limited computing and IT professional resources on implementing an efficient parallel metaheuristic algorithm for planning or management solutions. In this paper we extend the standard meta-description for genetic algorithms (GA) with a simple non-trivial parallel implementation. Our parallel GA framework is chiefly concerned with the development of a straightforward way for engineers to modify existing genetic algorithm implementations for real transportation and logistics problems to make use of commonly available hardware resources without completely reworking complex, useful and usable codes. The framework presented at its parallel base is a modification of the primitive parallelization concept, but if implemented as described it may be gradually extended to fit the qualities of any underlying problem better (via the adaptation of the merging and communications functions).We present our framework and computational results for a classical transportation related combinatorial optimization problem - the traveling salesman problem with a standard sequential genetic algorithm implementation. Our empirical analysis shows that this simple extension can lead to considerable solution improvements. We also tested our assumptions that the framework is easily implemented by an engineer not initially familiar with genetic algorithms to implement the framework for another minimum multiprocessor scheduling problem. These case studies verify that our framework is better than primitive parallelization because it gives empirically better results under equitable conditions. It also outperforms fine grained parallelization as it is easier and faster to implement.

A Master Slave Parallel Genetic Algorithm for Feature Selection in High Dimensional Datasets

Feature Selection in High Dimensional Datasets is a combinatorial problem as it selects the optimal subsets from N dimensional data having 2N possible subsets. Genetic Algorithms are generally a good choice for feature selection in large datasets, though for some high dimensional problems it may take varied amount of time - few seconds, few hours or even few days. Therefore, it is important to use Genetic Algorithms that can give quality results in reasonably acceptable time limit. For this purpose, it is becoming necessary to implement Genetic Algorithms in an efficient manner. In this paper, a Master Slave Parallel Genetic Algorithm is implemented as a Feature Selection procedure to diminish the time intricacies of sequential genetic algorithm. This paper describes the speed gains in parallel Master-Slave Genetic Algorithm and also discusses the theoretical analysis of optimal number of slaves required for an efficient master slave implementation. The experiments are performed on three high-dimensional gene expression data. As Genetic Algorithm is a wrapper technique and takes more time to find the importance of any feature, Information Gain technique is used first as pre-processing task to remove the irrelevant features.

ExxonMobil to invest in Singapore site to produce lube basestocks and distillates

Reducing CO2 emissions from fossil fuel fired power plants has been a major environmental concern over the last decade. Amongst various carbon capture and storage (CCS) technologies, the utilization of solvent-based post-combustion capture (PCC), played a major role in the reduction of CO2 emissions. This paper illustrates the development of machine learning models to predict the outputs of the PCC unit. A fine tree, Matérn Gaussian process regression (GPR), rational quadratic GPR, and squared exponential GPR models were developed and compared with a feed-forward artificial neural network (ANN) model. An accuracy of up to 98% in predicting the process outputs was achieved. Furthermore, the models were utilized to determine the optimum operating conditions for the process using a sequential quadratic programming algorithm (SQP) and genetic algorithm (GA). The use of the machine learning models has proven to be very useful since the complete mechanistic model is too large, and its runtime is too long to allow for rigorous optimal solutions. The machine learning models and optimization problems were developed and solved using MATLAB. The data used in this work was obtained from simulating the process using gPROMS process builder. The inputs of the model were selected to be reboiler duty, condenser duty, reboiler pressure, flow rate, temperature, and the pressure of the flue gas. The models were able to accurately predict the outputs of the process which are the system energy requirements (SER), capture rate (CR), and the purity of condenser outlet stream (PU).

Performance analysis of synchronous and asynchronous distributed genetic algorithms on multiprocessors

Abstract Because of their effectiveness and flexibility in finding useful solutions, Genetic Algorithms (GAs) are very popular search techniques for solving complex optimization problems in scientific and industrial fields. Parallel GAs (PGAs), and especially distributed ones have been usually presented as the way to overcome the time-consuming shortcoming of sequential GAs. In the case of applying PGAs, we can expect better performance, the reason being the exchange of knowledge during the parallel search process. The resulting distributed search is different compared to what sequential panmictic GAs do, then deserving additional studies. This article presents a performance study of three different PGAs. Moreover, we investigate the effect of synchronizing communications over modern shared-memory multiprocessors. We consider the master-slave model along with synchronous and asynchronous distributed GAs (dGAs), presenting their different designs and expected similarities when running in a number of cores ranging from one to 32 cores. The master-slave model showed a competitive numerical effort versus the other dGAs and demonstrated to be able to scale-up well over multiprocessors. We describe how the speed-up and parallel performance of the dGAs is changing as the number of cores enlarges. Results of the island model show that synchronous and asynchronous dGAs have different numerical performances on a multiprocessor, the asynchronous algorithm having a faster execution, thus more attractive for time demanding applications. Our results and statistical analyses help in developing a novel body of knowledge on PGAs running in shared memory multiprocessors (versus overwhelming literature oriented to distributed memory clusters), something useful for researchers, beginners, and final users of these techniques.

A component-based study of energy consumption for sequential and parallel genetic algorithms

Recently, energy efficiency has gained attention from researchers interested in optimizing computing resources. Solving real-world problems using optimization techniques (such as metaheuristics) requires a large number of computing resources and time, consuming an enormous amount of energy. However, only a few and limited research efforts in studying the energy consumption of metaheuristics can be found in the existing literature. In particular, genetic algorithms (GAs) are being used so widely to solve a large range of problems in scientific and real-world problems, but hardly found explained in their internal consumption behavior. In the present article, we analyze the energy consumption behavior of such techniques to offer a useful set of findings to researchers in the mentioned domains. We expand our study to include several algorithms and different problems and target the components of the algorithms so that the results are still more appealing for researchers in arbitrary domains of application. Our experiments on the sequential GAs show the controlling role of the fitness operator on energy consumption and also reveal possible energy hot spots in GAs operations, such as mutation operator. Further, our distributed evaluations besides a statistical analysis of the results demonstrate that the communication scheme could highly affect the energy consumption of the parallel evaluations of the GAs.

A method for optimizing the aerodynamic layout of a helicopter that reduces the effects of aerodynamic interaction

The aerodynamic environment during the flight of a helicopter is complex because of the severe aerodynamic interaction between various components. To fully consider the effect of aerodynamic interaction in the initial stages of helicopter design and to eliminate or reduce its adverse effects, a comprehensive design optimization method for the aerodynamic layout of a helicopter that is capable of reducing the adverse effects of aerodynamic interaction is developed in this paper. To satisfy the requirements for precision and efficiency in the calculation model, an aerodynamic interaction analysis model of various helicopter components was established based on a viscous vortex particle and the unsteady panel hybrid method. To simultaneously consider the influences of the position and shape of the aerodynamic components on the aerodynamic interaction during the optimization process, parameter modeling of the helicopter's shape was performed based on the class function/shape function transformation (CST) method. A Kriging surrogate model of the objective function was further developed and combined with a hybrid sequential quadratic algorithm and genetic algorithm optimization strategy to establish a comprehensive optimization flow for the aerodynamic layout of a helicopter that reduces the adverse effects of aerodynamic interaction. Verification was carried out based on a fuselage shape derived from UH-60 helicopter. The optimization results showed that the use of the comprehensive optimization method for the aerodynamic layout of a helicopter can effectively reduce the adverse effects of aerodynamic interaction. Based on the optimization objectives, the efficiency of hovering increased by 4.7%, the hovering ceiling increased by 3.48%, the speed stability derivative increased by 264.7%, and the angle of attack stability derivative decreased by 26.4%.

Inverse kinematics solution for robotic manipulator based on extreme learning machine and sequential mutation genetic algorithm

This article presents an intelligent algorithm based on extreme learning machine and sequential mutation genetic algorithm to determine the inverse kinematics solutions of a robotic manipulator with six degrees of freedom. This algorithm is developed to minimize the computational time without compromising the accuracy of the end effector. In the proposed algorithm, the preliminary inverse kinematics solution is first computed by extreme learning machine and the solution is then optimized by an improved genetic algorithm based on sequential mutation. Extreme learning machine randomly initializes the weights of the input layer and biases of the hidden layer, which greatly improves the training speed. Unlike classical genetic algorithms, sequential mutation genetic algorithm changes the order of the genetic codes from high to low, which reduces the randomness of mutation operation and improves the local search capability. Consequently, the convergence speed at the end of evolution is improved. The performance of the extreme learning machine and sequential mutation genetic algorithm is also compared with that of a hybrid intelligent algorithm, and the results showed that there is significant reduction in the training time and computational time while the solution accuracy is retained. Based on the experimental results, the proposed extreme learning machine and sequential mutation genetic algorithm can greatly improve the time efficiency while ensuring high accuracy of the end effector.

SWIMS: A dynamic life cycle-based optimisation and decision support tool for solid waste management

Abstract Solid waste management (SWM) decision makers are under increasing pressure to implement strategies that are both cost effective and environmentally sound. Consequently, SWM has developed into a highly complex systemic planning problem and analytical tools are needed to assist in the development of more sustainable SWM strategies. Here, we present the Solid Waste Infrastructure Modelling System (SWIMS) software, which is the first non-linear dynamic, LCA-based optimisation tool for SWM that optimises for both economic and environmental performance. The environmental and economic costs of treating generated wastes at available treatment facilities are calculated through a series of life cycle process models, based on non-linear expressions defined for each waste material and each treatment process type. Possible treatment paths for waste streams are identified using a depth first search algorithm and a sequential evolutionary genetic algorithm is used to prioritise the order of these paths, in lieu of user defined optimisation criteria and constraints. SWIMS calculates waste arisings into the future and determines if it is possible to treat generated waste, while considering present and future constraints (e.g. capacity). If additional capacity is required, SWIMS will identify the optimum infrastructure solution to meet this capacity demand. A demonstrative case study of MSW management in GB from 2010 to 2050 is presented. Results suggest that sufficient capacity is available in existing and planned infrastructure to cope with future demand for SWM and meet national regulatory and legislative requirements with relatively little capital investment beyond 2020. SWIMS can be used to provide valuable information for SWM decision makers, particularly when used to analyse the effects of possible future national or regional policies.

Comparison of gradient and nongradient algorithms in the structural optimization course

Gradient and nongradient optimization algorithms are currently available for structural design in structural optimization course. Despite the successful application of gradient algorithm in structural optimization, nongradient algorithm is also extensively adopted to solve the structural optimization problem. However, the efficiency of nongradient algorithm has caused a heated debate recently. To clarify this issue for the graduate students, sequential linear programming and genetic algorithm are, respectively, chosen as the representatives of gradient and nongradient algorithms to solve truss size optimization problem. Firstly, the size optimization formulations of truss structure for sequential linear programming and genetic algorithm are summarized, respectively. Secondly, an educational finite element software for truss structure is developed by using the object-oriented programming to create the software framework. This study aims to provide an open-source, extensible, and benchmarking software, which do assist the students to understand the structural optimization process in engineering education. Finally, two benchmarking examples are introduced to compare the efficiency and accuracy of sequential linear programming and genetic algorithm.

Optimal sensor placement for cable force monitoring based on multioutput support vector regression model

Cable force monitoring is an essential and critical part of structural health monitoring for cable-supported bridges. The quality of obtained information depends considerably on the number and location of limited sensors. The purpose of this article is to provide a method for optimal sensor placement for cable force monitoring in cable-supported bridges. Based on the spatial correlation between neighbouring or symmetrical cable forces, the structural information of non-monitored cables can be predicted by multioutput support vector regression models, established between monitored (input) and the non-monitored (output) cable forces. The number and placement of cable force sensors have significant influence on prediction performance of established multioutput support vector regression models. The proposed optimal sensor configuration is to select multioutput support vector regression models with minimum prediction error from all possible sensor locations. In this study, information entropy is employed to measure the prediction performance of different sensor configurations and formulate the objective function, optimised by three computationally effective algorithms: forward sequential sensor placement algorithm, backward sequential sensor placement algorithm and genetic algorithm. The application of proposed method to Nanjing No. 3 Yangtze River Bridge confirmed the efficiency, accuracy and effectiveness of the proposed method.