Single-objective Genetic Algorithm Research Articles

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on the trips between stations is defined to discover groups of correlated stations, using a hierarchical agglomerative clustering method. Third, two levels for rebalancing are assumed as intra-clusters and inter-clusters with the aim of keeping the balance of the network at the beginning of days. The intra-cluster level keeps the balance of bike distribution inside each cluster, and the inter-cluster level connects different clusters in order to keep the balance between the clusters. Finally, rebalancing tours are optimized according to the positive or negative balance at both levels of the intra-clusters and inter-clusters using a single objective genetic algorithm. The rebalancing problem is modeled as an optimization problem, which aims to minimize the tour length. The proposed model is implemented in one week of bike sharing trip data set in Chicago, USA. Outcomes of the model are validated for two subsequent weekdays. Analyses show that the proposed model can reduce the length of the rebalancing tour by 30%.

Power management and sizing optimisation of renewable energy hydrogen production systems

This paper presents the combined sizing and power management optimisation methodology for a small-scale stand-alone hybrid renewable energy (RE) hydrogen production system. System cost is argued to be dependent on efficiency and reliability. The optimisation strategy developed, implements a strength Pareto evolutionary algorithm (SPEA) and a single objective genetic algorithm (GA) in cascade. The objectives for optimisation are: system efficiency, cost and reliability. Three different geographic sites with different wind and solar renewable energy input potentials are optimised. Results are presented as three-dimensional Pareto surfaces as well as two-dimensional scatter plots. Relationships between objectives are illustrated as well as important correlations between objectives and design variables. The expected conflicting relationship between cost and efficiency is clearly observed from the Pareto curves. The methodology developed highlights the importance of considering the simultaneous optimisation of sizing and power management. It is concluded that the optimisation methodology developed is useful in evaluating these and similar hybrid RE systems and provides insight into the design trade-offs for multiple conflicting objectives.

Automatic Scientific Document Clustering Using Self-organized Multi-objective Differential Evolution

Document clustering is the partitioning of a given collection of documents into various K- groups based on some similarity/dissimilarity criterion. This task has applications in scope detection of journals/conferences, development of some automated peer-review support systems, topic-modeling, latest cognitive-inspired works on text summarization, and classification of documents based on semantics, etc. In the current paper, a cognitive-inspired multi-objective automatic document clustering technique is proposed which is a fusion of self-organizing map (SOM) and multi-objective differential evolution approach. The variable number of cluster centers are encoded in different solutions of the population to determine the number of clusters from a data set in an automated way. These solutions undergo various genetic operations during evolution. The concept of SOM is utilized in designing new genetic operators for the proposed clustering technique. In order to measure the goodness of a clustering solution, two cluster validity indices, Pakhira-Bandyopadhyay-Maulik index, and Silhouette index, are optimized simultaneously. The effectiveness of the proposed approach, namely self-organizing map based multi-objective document clustering technique (SMODoc_clust) is shown in automatic classification of some scientific articles and web-documents. Different representation schemas including tf, tf-idf and word-embedding are employed to convert articles in vector-forms. Comparative results with respect to internal cluster validity indices, namely, Dunn index and Davies-Bouldin index, are shown against several state-of-the-art clustering techniques including three multi-objective clustering techniques namely MOCK, VAMOSA, NSGA-II-Clust, single objective genetic algorithm (SOGA) based clustering technique, K-means, and single-linkage clustering. Results obtained clearly show that our approach is better than existing approaches. The validation of the obtained results is also shown using statistical significant t tests.

Optimizing Software Modularity with Minimum Possible Variations

Abstract Poor design choices at the early stages of software development and unprincipled maintenance practices usually deteriorate software modularity and subsequently increase system complexity. In object-oriented software, improper distribution of classes among packages is a key factor, responsible for modularity degradation. Many optimization techniques to improve the software modularity have been proposed in the literature. The focus of these optimization techniques is to produce modularization solutions by optimizing different design quality criteria. Such modularization solutions are good from the different aspect of quality; however, they require huge modifications in the existing modular structure to realize the suggested solution. Thus these techniques are costly and time consuming if applied at early stages of software maintenance. This paper proposes a search-based optimization technique to improve the modularity of the software system with minimum possible variation between the existing and produced modularization solution. To this contribution, a penalized fitness function, namely, penalized modularization quality, is designed in terms of modularization quality and the Move or Join Effectiveness Measure metric. Furthermore, this fitness function is used in both single-objective genetic algorithm (SGA) and multi-objective genetic algorithm (MGA) to generate the modularization. The effectiveness of the proposed remodularization approach is evaluated over five open-source and three random generated software systems. The experimentation results show that the proposed approach is able to generate modularization solutions with improved quality along with lesser perturbation compared to their non-penalty counterpart and at the same time it performs better with the MGA compared to the SGA. The proposed approach can be very useful, especially when total remodularization is not feasible/desirable due to lack of time or high cost.

Optimal locations of magnetorheological fluid pockets embedded in an elastically supported honeycomb sandwich beams for supersonic flutter suppression

In this paper, the optimal locations of finite number of embedded magneto-rheological fluid (MRF) pockets are calculated by using Single Objective Genetic Algorithm (SOGA) to suppress the supersonic flutter of an elastically supported sandwich beam with regular honeycomb interlayer. The structural formulation is based on the classical beam theory along with the Winkler-Pasternak foundation model, the MRF is modeled as a first order Kelvin-Voigt material, and the quasi-steady first order supersonic piston theory is employed to define the aerodynamic loading. The aeroelastic time response of honeycomb sandwich beams with different number of embedded MRF pockets are measured with/without foundation and flow stream under impact load. The simulation shows that by using MRF pockets in optimized locations in honeycomb sandwich structure, not only both lightness of honeycombs and variability of structural damping of MRF are considered, but also the supersonic flutter can be postponed to higher dynamic pressure compared to entirely honeycomb or MRF sandwich beams. Finally, in order to achieve the optimum compromise between mass and the flutter suppression, the best number of MR pockets along with the optimum magnetic field values in honeycomb sandwich structure is presented.

Path optimization of taxi carpooling

The problem that passengers are hard to take taxis while empty driving rate is high widely exists under the traditional taxi operation mode. The implementation of taxi carpooling mode can alleviate the problem in a certain extent. The objective of this study is to optimize the taxi carpooling path. Firstly, the taxi carpooling path optimization model with single objective and its extended model with multiple objectives are built respectively. Then, the single objective path optimization model of taxi carpooling is solved based on the improved single objective genetic algorithm, and the multiple-objective path optimization model of taxi carpooling is solved based on the improved multiple-objective genetic algorithm. Finally, a case study is carried out based on a road network with 24 nodes. The case study results show the path optimization models and algorithms of taxi carpooling proposed in the paper can quickly get the taxi carpooling path, and can increase the income of taxi driver while reduce the cost for passengers.

Genetic and hybrid algorithms for optimization of non-singular 3PRR planar parallel kinematics mechanism for machining application

SUMMARYThis paper proposes a special non-symmetric topology of a 3PRR planar parallel kinematics mechanism, which naturally avoids singularity within the workspace and can be utilized for hybrid kinematics machine tools. Subsequently, single-objective and multi-objective optimizations are conducted to improve the performance. The workspace area and minimum eigenvalue, as well as the condition number of the homogenized Cartesian stiffness matrix across the workspace, have been chosen as the objectives in the optimization based on their relevance to the machining application. The single-objective optimization is conducted by using a single-objective genetic algorithm and a hybrid algorithm, whereas the multi-objective optimization is conducted by using a multi-objective genetic algorithm, a weighted sum single-objective genetic algorithm, and a weighted sum hybrid algorithm. It is shown that the single-objective optimization gives superior value in the optimized objective, while sacrificing the other objectives, whereas the multi-objective optimization compromises the improvement of all objectives by providing non-dominated values. In terms of the algorithms, it is shown that a hybrid algorithm can either verify or refine the optimal value obtained by a genetic algorithm.

Estimating Parameter of Nonlinear Bias Correction Method Using NSGA-II in Daily Precipitation Data

Nonlinear (NL) method is the most effective bias correction method for correcting statistical bias when observation precipitation data can not be approximated using gamma distribution. Since NL method only adjusts mean and variance, it does not perform well in handling bias on quantile values. This paperpresents a scheme of NL method with additional condition aiming to mitigate bias on quantile values. Non-dominated Sorting Genetic Algorithm II (NSGA-II) was applied to estimate parameter of NL method. Furthermore, to investigate suitability of application of NSGA-II, we performed Single Objective Genetic Algorithm (SOGA) as a comparison. The experiment results revealed NSGA-II was suitable when solution of SOGA produced low fitness. Application of NSGA-II could minimize impact of daily bias correction on monthly precipitation. The proposed scheme successfully reduced biases on mean, variance, first and second quantile However, biases on third and fourth moment could not be handled robustly while biases on third quantile only reduced during dry months.

Multi-objective arrangement optimization of a tube bundle in cross-flow using CFD and genetic algorithm

Recently, energy saving problem attracts increasingly attention from researchers. The present study is aimed at determining the optimal arrangement of a tube bundle consisting of four heat exchange tubes. The tube bundle with temperature of 353.15 K on the outer surface, is cooled by forced convection in a channel. The two-dimensional (2-D) axisymmetric model is solved by the finite element method. By determining four optimal coordinates of the discrete tubes, multi-objective genetic algorithm is employed to obtain the Pareto solutions, where the objective functions are the pressure drop Δp and the average heat flux q, respectively. A decision maker method, TOPSIS is subsequently applied to determine the best solution from Pareto front. Compared with the initial tube bundle, the optimal solution increases 53.51% in q and decreases 1.27% in Δp. Overall, the TOPSIS method could improve the heat transfer performance of the tube bundle without bringing a higher pressure drop. Besides, the Pareto-based multi-objective optimization technique is more comprehensive, compared with single-objective genetic algorithm using performance evaluation criteria (PEC) or the efficiency evaluation criterion (EEC) as evaluation criterion.

Automated calibration of a two-dimensional overland flow model by estimating Manning's roughness coefficient using genetic algorithm

Abstract Calibration is an important phase in the hydrological modelling process. In this study, an automated calibration framework is developed for estimating Manning's roughness coefficient. The calibration process is formulated as an optimization problem and solved using a genetic algorithm (GA). A heuristic search procedure using GA is developed by including runoff simulation process and evaluating the fitness function by comparing the experimental results. The model is calibrated and validated using datasets of Watershed Experimentation System. A loosely coupled architecture is followed with an interface program to enable automatic data transfer between overland flow model and GA. Single objective GA optimization with minimizing percentage bias, root mean square error and maximizing Nash–Sutcliffe efficiency is integrated with the model scheme. Trade-offs are observed between the different objectives and no single set of the parameter is able to optimize all objectives simultaneously. Hence, multi-objective GA using pooled and balanced aggregated function statistic are used along with the model. The results indicate that the solutions on the Pareto-front are equally good with respect to one objective, but may not be suitable regarding other objectives. The present technique can be applied to calibrate the hydrological model parameters.

A Bi-objective Genetic Algorithm Optimization of Chaos-DNA Based Hybrid Approach

Abstract The paper implements and optimizes the performance of a currently proposed chaos-deoxyribonucleic acid (DNA)-based hybrid approach to encrypt images using a bi-objective genetic algorithm (GA) optimization. Image encryption is a multi-objective problem. Optimizing the same using one fitness function may not be a good choice, as it can result in different outcomes concerning other fitness functions. The proposed work initially encrypts the given image using chaotic function and DNA masks. Further, GA uses two fitness functions – entropy with correlation coefficient (CC), entropy with unified average changing intensity (UACI), and entropy with number of pixel change rate (NPCR) – simultaneously to optimize the encrypted data in the second stage. The bi-objective optimization using entropy with CC shows significant performance gain over the single-objective GA optimization for image encryption.

Comparison of simple and curved trapezoidal longitudinal vortex generators for optimum flow characteristics and heat transfer augmentation in a heat exchanger

In the present study, a steady state numerical simulation is performed for investigating the thermal and fluid flow characteristics in a turbulent boundary layer for a flat channel as an element of a compact heat exchanger. A trapezoidal winglet pair (TWP) and curved trapezoidal winglet pair (CTWP) are mounted in the computational domain in order to deliberate the effects of longitudinal vortices induced by CTWP and TWP as the vortex generators. The simulation of the turbulent flow is performed by means of the Reynolds stress model (RSM) and the flow has a Reynolds number in the range 7000–35,000. The flow structure at the presence of longitudinal vortex generator consists of corner (horseshoe) vortex, induced vortex, and the main vortex which cause significant heat transfer effects in the downstream region. The effects on the thermal boundary layer are also studied from the field synergy point of view. Numerical results showed that CTWP has a lower pressure drop and a better overall performance compared to TWP. To achieve a maximum heat transfer augmentation and a minimum pressure drop, optimal height and clearance between two vortex generators have been determined using and combining computational fluid dynamics analysis, artificial neural networks and single-objective genetic algorithm.

Effect of splitter plate on passive control and drag reduction for fluid flow past an elliptic cylinder

In this study, the effect of splitter plate on fluid flow characteristics past an elliptic cylinder of different axis ratios (AR=1.0−0.5) is numerically investigated for various Reynolds number (Re = 50–200). The equations governing fluid flow are discretized using Streamline Upwind/Petrov-Galerkin (SUPG) based finite element method (FEM). The presence of a splitter plate alters the wake region where vortex shedding is suppressed. Depending on AR and Re two critical lengths of splitter plate are defined, one for suppression of vortex shedding and the other when shear layer interaction in the wake is inhibited. The correlations for the critical lengths of splitter plate as a function of AR and Re are derived using regression analysis. It is observed that, the variation of integral parameters like drag, lift and St are not monotonic with increasing plate length. From the results it is concluded that for a particular combination of Re and AR with suitable splitter plate length, the total drag is minimized approximately from 2% to 38%. Finally, Cd_avg is selected as an objective function and modelled using Response Surface Approximation (RSA). Furthermore, a single-objective Genetic algorithm has been implemented to obtain optimum configuration for minimum Cd_avg.

Multiobjective design and optimization of polymer flood performance

The design and optimization of polymer flood projects have traditionally focused on increasing oil production only. For economic viability, the optimization strategy should maximize oil production while minimizing polymer usage. However, these two objectives can be conflicting. The challenge is how to optimize polymer flood design considering the trade-off between these conflicting objectives.In this work, we utilize the concept of Pareto optimality to generate a set of Pareto optimal solutions that represent a trade-off relation between the conflicting objectives viz. polymer utility factor and incremental oil gain. The Pareto optimal solutions can be searched by a non-domination based genetic algorithm (NSGA-II). The algorithm evaluates fitness by a dominance relationship instead of fitness measures as in the ordinary single-objective genetic algorithm. The dominance concept defines levels of optimality by assigning several ranks to each population. The algorithm iteratively minimizes ranks until all members of the population become rank one which implies that the Pareto optimality condition is reached.The application of our proposed method is demonstrated by a 2D synthetic example and a 3D field-scale application. The Pareto-based approach is implemented to simultaneously optimize two objectives: (1) polymer utility factor (UF) which is the amount of polymer required per barrel of incremental oil and (2) cumulative incremental oil production (Np). The results demonstrate that beyond the optimal design, relatively minor increase in oil production requires significantly more polymer injection and the polymer UF is increased substantially.To optimize both oil production and polymer utility, the Pareto optimal solutions provide the best compromise that enables maximizing oil production while maintaining the efficiency of polymer usage.

Balancing multiple criteria in formulation of weighted, single-objective genetic algorithm optimization for CNC machining problems

This paper presents results obtained from the implementation of a genetic algorithm (GA) to a simplified multi-objective machining optimization problem. The major goal is to examine the effect of crucial machining parameters imparted to computer numerical control machining operations when properly balanced conflicting criteria referring to part quality and process productivity are treated as a single optimization objective. Thus the different combinations of weight coefficient values were examined in terms of their significance to the problem’s response. Under this concept, a genetic algorithm was applied to optimize the process parameters exist in typical; commercially available CAM systems with significantly low computation cost. The algorithm handles the simplified linear weighted criteria expression as its objective function. It was found that optimization results vary noticeably under the influence of different weighing coefficients. Thus, the obtained optima differentiate, since balancing values strongly affect optimization objective functions.

A genetic algorithm-based integrated design environment for the preliminary design and optimization of aeronautical piston engine components

The enhancement of the early design stages, in the production of aeronautical engines, has been shown decisive, for developing efficient and reliable final products. Nevertheless, in most of industrial engineering design problems, the amount of design variables is large. Moreover, several nonlinearities characterize the behaviour of the physical phenomena involved and the derivatives are seldom known for all the functions. Besides, objective functions exhibit several local extremes, whereas the designer as well as the practitioner is usually interested in the global one. In this context, stochastic and evolutionary optimization have been shown capable to provide reliable solutions while keeping the computational cost at a reasonable level. Existing tools for the design and optimization of engine components deal with the optimal and detailed design of specific engine components, thus requiring several computational time and efforts to gain optimized design parameters. Hence, existing tools fit for later design phases. Conversely, this paper proposes an integrated design and optimization environment, for automatically designing optimal aeronautical piston engine configuration, still in the conceptual design stage. The optimization is performed using the MATLAB genetic algorithm (GA) toolbox®, while the automatic design of the optimized components is carried out in cascade to the optimization phase. In particular, a single-objective GA is used to evaluate the optimal dimensions of engine components related to motion, namely: crankshaft, connecting rods and screws, flywheel, propeller shaft and torsional vibration damper. For testing the efficiency of the integrated environment, the conceptual design of components of a 4-in-line Diesel aeronautical piston engine is proposed, starting from an existing similar engine. Results show a reduction of the 20 % of weight of the crankshaft in comparison to the original configuration. The proposed environment seems to be a promising tool for a fast and reliable conceptual design of piston engines for aeronautical purposes.

A New Tool to Optimize ICE Performance and Emissions Via 1D Code Coupled with GAs

The aim of this paper is to propose a new strategy to optimize the performance and to reduce the emission levels of Internal Combustion Engines by varying intake valve lift profile and timing. The object of the study was an ICE – SI, GDI, 1.4 l, four cylinders, 16V, turbocharged. It was equipped with an electrohydraulic VVA system which allows the intake valves to vary, at the same time, lift and timing in order to realize early IVC and/or late IVO. Thanks to this, the engine can always operate in the optimal fluid dynamics conditions in order to achieve the best performance and emission levels. A model of the engine was implemented in GT-Power™ for several operating conditions (partial load, full load, low and high engine speed), and then coupled with a single-objective genetic algorithm, evolved subsequently into a multi-objective genetic algorithm. Two different analysis were carried out: the first one for reducing CO2 emissions at partial load and low engine speed (single-objective optimization), and the second one for increasing the brake torque at full load (multi-objective optimization). The proposed model shows the possibility to quickly find optimal solutions for the test cases considered, and it let the opportunity to be further developed and improved in order to optimize many other parameters of the ICE.

Optimization of distributed-loss circuit of gyrotron traveling wave amplifier using multi-objective genetic algorithm

The resistance and thickness of distributed-loss circuit of gyrotron traveling wave amplifier are optimized by using the multi-objective genetic algorithm. Maximum gain of 15.9 dB is obtained by optimizing the thickness and resistivity of loss layer as 0.116 mm and 77 882 times the resistivity of copper, respectively. The accuracy is verified by the single-objective genetic algorithm, which makes the amplifier realize effective restraint of gyrotron backward wave oscillation and achieve the maximum gain.

Optimization of Calibration Parameters for an Event Based Watershed Model Using Genetic Algorithm

In this study, an event based rainfall runoff model has been integrated with Single objective Genetic Algorithm (SGA) and Multi-objective Genetic Algorithm (MGA) for optimization of calibration parameters (i.e. saturated hydraulic conductivity (Ks), average capillary suction at the wetting front (Sav), initial water content (θi) and saturated water content (θs )). The integrated model has been applied for Harsul watershed located in India, and Walnut Gulch experimental watershed located in Arizona, USA. Nash-Sutcliffe Efficiency (NSE) and correlation coefficient (r) between observed and simulated runoff have been used to test the performance of runoff models. The SGA and MGA integrated runoff model performance is also compared with the performance of the Hydrologic Engineering Center- Hydrologic Modeling System (HEC_HMS) model. Range of NSE values for study watersheds with integrated MGA, integrated SGA, HEC_HMS and for the event based rainfall runoff models are [−0.61 to 0.79], [−0.5 to 0.74], [−3.37 to 0.82] and [−5.78 to 0.53] respectively. Range of correlation coefficient values for study watersheds with integrated MGA, integrated SGA, HEC_HMS and for the event based rainfall runoff models are [0.18 to 0.95], [−0.55 to 0.90], [−0.18 to 0.97] and [−0.12 to 0.86] respectively. From the results, it is evident that the integrated model is giving the best calibrated parameters as compared to manual calibration methods. Genetic Algorithm (GA) integrated runoff models can be used to simulate the flow parameters of data sparse watersheds.

A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms

Knowledge on asynchronous machine parameters (resistances, inductances…) has become necessary for the manufacturing industry in the interest of optimizing performances in a production system (roll-to-roll processing, wind generator…). Indeed, accurate values of this machine allow improving control of the torque, speed and position, managing power consumption in the best way possible, and predicting induction machine failures with great effectiveness. In these regards, the authors of this paper propose a black-box modeling for a powerful identification of asynchronous machine parameters relying on stochastic research algorithms. The algorithms used for the estimation process are a single objective genetic algorithm, the well-known NSGA II and the new ?-NSGA III (multi-objective genetic algorithms). Results provided by those show that the best estimation of asynchronous machines parameters is given by ?-NSGA III. In addition, this outcome is confirmed by performing the identification process on three different induction machines.