Standard Genetic Algorithm Research Articles

Novelty Search for the Synthesis of Current Followers

A topology synthesis method is introduced using genetic algorithms (GA) based on novelty search (NS). NS is an emerging meta-heuristic, that guides the search based on the novelty of each solution instead of the objective function. The synthesized topologies are current follower (CF) circuits; these topologies are new and designed using integrated circuit CMOS technology of 0.35m. Topologies are coded using a chromosome divided into four genes: small signal gene (SS), MOSFET synthesis gene (SMos), polarization gene (Bias) and current source synthesis gene (CM). The proposed synthesis method is coded in MatLab and uses SPICE to evaluate the CFs fitness. The GA based on NS (GA-NS) is compared with a standard objective-based GA, showing unique search dynamics and improved performance. Experimental results show twelve CFs synthesized by the GA-NS algorithm, and their main attributes are summarized and discussed. This work is the first to show that NS can be used asa promising alternative in the field of automatic circuit synthesis.

An Improved Particle Swarm Optimization Algorithm for Fractional Order PID Parameter Tuning

An improved particle swarm optimization algorithm is proposed to tune the parameters of fractional order PID controller in this paper. The fitness function is set based on the performance index of the system in time domain. In the process of iteration, partial particles are re-initialized when they evolved slower than the others. Meanwhile, the adaptive inertia weight value is adopted to get the optimal solution by globally searching. Finally, applying the proposed fractional PID controller into a typical servo control system, the simulation results indicate that the parameter tuning method based on improved particle swarm optimization has better convergence speed. The control system with fractional PID algorithm has better performance-short adjusting time, fast rising velocity, strong anti-jamming capability, at the same time, this fractional PID controller could be better than standard particle swarm optimization algorithm and genetic algorithm.

Constrained combinatorial optimization of multi-layered composite structures by means of Stud GA with proportionate selection and extinction

In this paper a revised algorithm, derived by the Stud GA, to optimize the stacking sequence of layered composite structures is presented. The constrained combinatorial problem is formulated and hence solved by means of the algorithm presented herein. Unlike the Stud GA, the proposed algorithm uses a fitness-proportionate selection to find potential mates to the Stud and a self-adaptive extinction to avoid premature convergence. Elitism is also allowed. Standard benchmark problems have been used to fully assess the validity of the proposed algorithm. Results in terms of reliability and efficiency are compared against three GAs and a Permutation Search (PS) algorithm. It has been demonstrated, by means of a parametric study, that a low probability of extinction is sufficient to ensure fast convergence as well as reliable results. Finally the optimization of a more complex aeronautic structure is addressed aiming to prove the validity of the proposed algorithm in a two level optimization strategy. Results in terms of efficiency are compared against those of an in-house standard GA.

An advanced GA–VNS combination for multicriteria route planning in public transit networks

Nowadays, passengers in urban public transport systems do not only seek a short-time travel, but they also ask for optimizing other criteria such as cost and effort. Therefore, an efficient routing system should incorporate a multiobjective analysis into its search process. Several algorithms have been proposed to optimally compute the set of nondominated journeys while going from one place to another such as the generalization of the algorithm of Dijkstra. However, such approaches become less performant or even inapplicable when the size of the network becomes very large or when the number of criteria considered is very important. Therefore, we propose in this paper an advanced heuristic approach whereby a Genetic Algorithm (GA) is combined with a Variable Neighborhood Search (VNS) to solve the Multicriteria Shortest Path Problem (MSPP) in multimodal networks. As transportation modes, we focus on railway, bus, tram and pedestrian. As optimization criteria, we consider travel time, monetary cost, number of transfers and the total walking time. The proposed approach is compared with the exact algorithm of Dijkstra, as well as, with a standard GA and a pure VNS. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that the proposed combination GA–VNS represents the best approach in terms of computational time and solutions quality for a real world routing system.

Classification of human cancer diseases by gene expression profiles

A cancers disease in virtually any of its types presents a significant reason behind death surrounding the world. In cancer analysis, classification of varied tumor types is of the greatest importance. Microarray gene expressions datasets investigation has been seemed to provide a successful framework for revising tumor and genetic diseases. Despite the fact that standard machine learning ML strategies have effectively been valuable to realize significant genes and classify category type for new cases, regular limitations of DNA microarray data analysis, for example, the small size of an instance, an incredible feature number, yet reason for limitation its investigative, medical and logical uses. Extending the interpretability of expectation and forecast approaches while holding a great precision would help to analysis genes expression profiles information in DNA microarray dataset all the most reasonable and proficiently. This paper presents a new methodology based on the gene expression profiles to classify human cancer diseases. The proposed methodology combines both Information Gain (IG) and Standard Genetic Algorithm (SGA). It first uses Information Gain for feature selection, then uses Genetic Algorithm (GA) for feature reduction and finally uses Genetic Programming (GP) for cancer types’ classification. The suggested system is evaluated by classifying cancer diseases in seven cancer datasets and the results are compared with most latest approaches. The use of proposed system on cancers datasets matching with other machine learning methodologies shows that no classification technique commonly outperforms all the others, however, Genetic Algorithm improve the classification performance of other classifiers generally.

Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm

To consider the service-matching degree, the composition harmony degree, and the service composition complexity in cloud manufacturing service composition optimization problems, a new composition optimization approach, called cloud-entropy enhanced genetic algorithm (CEGA), is put forward to solve such problems with multi-objectives. The definitions of service-matching degree, composition harmony degree, and cloud-entropy and the corresponding calculation methods are given. A multi-objective optimization mathematical model of cloud manufacturing service composition is built. The manufacturing task of AGV (automated guided vehicle) is taken as an example to verify the proposed CEGA algorithm on the established composition model. The studied result shows that CEGA converges faster than a standard genetic algorithm with shorter time.

Uplink Scheduling of Navigation Constellation Based on Immune Genetic Algorithm.

The uplink of navigation data as satellite ephemeris is a complex satellite range scheduling problem. Large–scale optimal problems cannot be tackled using traditional heuristic methods, and the efficiency of standard genetic algorithm is unsatisfactory. We propose a multi-objective immune genetic algorithm (IGA) for uplink scheduling of navigation constellation. The method focuses on balance traffic and maximum task objects based on satellite-ground index encoding method, individual diversity evaluation and memory library. Numerical results show that the multi–hierarchical encoding method can improve the computation efficiency, the fuzzy deviation toleration method can speed up convergence, and the method can achieve the balance target with a negligible loss in task number (approximately 2.98%). The proposed algorithm is a general method and thus can be used in similar problems.

Computing of the Ground State Energy of a Hydrogen Like Impurity in a Spherical Quantum Dot using QPSO Algorithm

In this work, quantum particle swarm optimization (QPSO1) algorithm method is applied to the problem of impurity at the center of a spherical quantum dot for infinite confining potential case. For this purpose, a trial variational wave function is considered for ground state, and then energy values are calculated as a function of the radius of a spherical quantum dot. Also, the evolution of the energy eigenvalue for different dot radii and different optimized parameter is determined. The energy converges remarkably fast, after a few numbers of iteration. In comparison with the two other available methods, standard variational procedure and genetic algorithm method (GA), the results coming out from QPSO algorithm are in more satisfactory agreement with the real values.

A new swarm intelligent optimization algorithm: Pigeon Colony Algorithm (PCA)

In this paper, a new Pigeon Colony Algorithm (PCA) based on the features of a pigeon colony flying is proposed for solving global numerical optimization problems. The algorithm mainly consists of the take-off process, flying process and homing process, in which the take-off process is employed to homogenize the initial values and look for the direction of the optimal solution; the flying process is designed to search for the local and global optimum and improve the global worst solution; and the homing process aims to avoid having the algorithm fall into a local optimum. The impact of parameters on the PCA solution quality is investigated in detail. There are low-dimensional functions, high-dimensional functions and systems of nonlinear equations that are used to test the global optimization ability of the PCA. Finally, comparative experiments between the PCA, standard genetic algorithm and particle swarm optimization were performed. The results showed that PCA has the best global convergence, smallest cycle indexes, and strongest stability when solving high-dimensional, multi-peak and complicated problems.

Distribution network reconfiguration using a genetic algorithm with varying population size

Genetic algorithm (GA) has been shown to be an effective way to solve the complex combinatorial, and constrained non-linear mixed integer optimization problem of distribution network reconfiguration. Extensive work has been done in the literature to efficiently apply GA to the reconfiguration problem. Nonetheless, to date, all the previous work in this area have presupposed that the GA population size should remain constant throughout the evolution of the search process. In this paper, we propose the application of a genetic algorithm with variable population size (GAVAPS) to the reconfiguration problem. We demonstrate that allowing the population size to adaptively grow and shrink according to the status of the GA search can allow for a more efficient solution, compared to standard genetic algorithm.

Three Natural Computation methods for joint channel estimation and symbol detection in multiuser communications

This paper studies three of the most important optimization algorithms belonging to Natural Computation (NC): genetic algorithm (GA), tabu search (TS) and simulated quenching (SQ). A concise overview of these methods, including their fundamentals, drawbacks and comparison, is described in the first half of the paper. Our work is particularized and focused on a specific application: joint channel estimation and symbol detection in a Direct-Sequence/Code-Division Multiple-Access (DS/CDMA) multiuser communications scenario; therefore, its channel model is described and the three methods are explained and particularized for solving this. Important issues such as suboptimal convergence, cycling search or control of the population diversity have deserved special attention. Several numerical simulations analyze the performance of these three methods, showing, as well, comparative results with well-known classical algorithms such as the Minimum Mean Square Error estimator (MMSE), the Matched Filter (MF) or Radial Basis Function (RBF)-based detection schemes. As a consequence, the three proposed methods would allow transmission at higher data rates over channels under more severe fading and interference conditions. Simulations show that our proposals require less computational load in most cases. For instance, the proposed GA saves about 73% of time with respect to the standard GA. Besides, when the number of active users doubles from 10 to 20, the complexity of the proposed GA increases by a factor of 8.33, in contrast to 32 for the optimum maximum likelihood detector. The load of TS and SQ is around 15–25% higher than that of the proposed GA.

Research on the base station calibration of multi-station and time-sharing measurement based on hybrid genetic algorithm

Abstract In order to detect the motion error of machine tool quickly and accurately, the multi-station and time-sharing measurement by laser tracker is introduced in the paper. Merely distance is involved in the measurement, thus the influence of angle measurement on the whole measuring accuracy of laser tracker can be effectively avoided. With this method, the measurement algorithm mainly relates to base station calibration and measuring point determination. Moreover, how to quickly and accurately calibrate the position of base station is a critical issue, which directly influences measuring accuracy. To solve the problem, the redundant measurement method is adopted. By establishing the mathematical model of detecting the motion error of machine tool with multi-station and time-sharing measurement, the nonlinear redundant equations concerning base station calibration and measuring point determination can be obtained by large amount of measured data based on the GPS principle. To overcome the deficiencies of prematurity and poor local searching ability of standard genetic algorithm in solving nonlinear problem, the simplex method is integrated into the genetic algorithm to constitute a hybrid genetic algorithm, then the deficiencies can be effectively overcome for further improvement of the solving accuracy. The hybrid genetic algorithm derived is used to determine the position of base station and the coordinates of measuring point, and the simulations and experiments are conducted to verify the effectiveness of multi-station and time-sharing measurement algorithm deduced on the basis of hybrid genetic algorithm.

Extended solvent-contact model approach to blind SAMPL5 prediction challenge for the distribution coefficients of drug-like molecules.

The performance of the extended solvent-contact model has been addressed in the SAMPL5 blind prediction challenge for distribution coefficient (LogD) of drug-like molecules with respect to the cyclohexane/water partitioning system. All the atomic parameters defined for 41 atom types in the solvation free energy function were optimized by operating a standard genetic algorithm with respect to water and cyclohexane solvents. In the parameterizations for cyclohexane, the experimental solvation free energy (ΔG sol ) data of 15 molecules for 1-octanol were combined with those of 77 molecules for cyclohexane to construct a training set because ΔG sol values of the former were unavailable for cyclohexane in publicly accessible databases. Using this hybrid training set, we established the LogD prediction model with the correlation coefficient (R), average error (AE), and root mean square error (RMSE) of 0.55, 1.53, and 3.03, respectively, for the comparison of experimental and computational results for 53 SAMPL5 molecules. The modest accuracy in LogD prediction could be attributed to the incomplete optimization of atomic solvation parameters for cyclohexane. With respect to 31 SAMPL5 molecules containing the atom types for which experimental reference data for ΔG sol were available for both water and cyclohexane, the accuracy in LogD prediction increased remarkably with the R, AE, and RMSE values of 0.82, 0.89, and 1.60, respectively. This significant enhancement in performance stemmed from the better optimization of atomic solvation parameters by limiting the element of training set to the molecules with experimental ΔG sol data for cyclohexane. Due to the simplicity in model building and to low computational cost for parameterizations, the extended solvent-contact model is anticipated to serve as a valuable computational tool for LogD prediction upon the enrichment of experimental ΔG sol data for organic solvents.

A hybrid approach for cost-optimized lateral transshipment in a supply chain environment

Purpose – When making sourcing decisions, both cost optimization and customer demand fulfillment are equally important for firm competitiveness. The purpose of this paper is to develop a stochastic search technique, hybrid genetic algorithm (HGA), for cost-optimized decision making in wholesaler inventory management in a supply chain network of wholesalers, retailers and suppliers. Design/methodology/approach – This study develops a HGA by using a mixture of greedy-based and randomly generated solutions in the initial population and a local search method (hill climbing) applied to individuals selected for performing crossover before crossover is implemented and to the best individual in the population at the end of HGA as well as gene slice and integration. Findings – The application of the proposed HGA is illustrated by considering multiple scenarios and comparing with the other commonly adopted methods of standard genetic algorithm, simulated annealing and tabu search. The simulation results demonstrate the capability of the proposed approach in producing more effective solutions. Practical implications – The pragmatic importance of this method is for the inventory management of wholesaler operations and this can be scalable to address real contexts with multiple wholesalers and multiple suppliers with variable lead times. Originality/value – The proposed stochastic-based search techniques have the capability in producing good-quality optimal or suboptimal solutions for large-scale problems within a reasonable time using ordinary computing resources available in firms.

Service composition based on discrete particle swarm optimization in military organization cloud cooperation

This paper addresses the problem of service composition in military organization cloud cooperation(MOCC). Military service providers(MSP) cooperate together to provide military resources for military service users(MSU). A group of atom services, each of which has its level of quality of service(QoS), can be combined together into a certain structure to form a composite service. Since there are a large number of atom services having the same function, the atom service is selected to participate in the composite service so as to fulfill users’ will. In this paper a method based on discrete particle swarm optimization(DPSO) is proposed to tackle this problem. The method aims at selecting atom services from service repositories to constitute the composite service, satisfying the MSU’s requirement on QoS. Since the QoS criteria include location-aware criteria and location-independent criteria, this method aims to get the composite service with the highest location-aware criteria and the best-match location-independent criteria. Simulations show that the DPSO has a better performance compared with the standard particle swarm optimization(PSO) and genetic algorithm(GA).

Research on the application of the improved genetic algorithm in the electroencephalogram-based mental workload evaluation for miners

Electroencephalogram is the electrical phenomena in the cerebral cortex or the scalp surface due to the electrophysiological activity of brain cells. Electroencephalogram has great theoretical and practical significance in measuring mental workload of people. More precise electroencephalographic is a precondition to study mental workload of miners. In this article, based on the actual situation of the electroencephalographic measurement of miners, the particle swarm optimization is introduced to improve the standard genetic algorithm, and put forward a combined method integrating the genetic algorithm with particle swarm optimization for achieving electroencephalogram-based measures of miners' mental workload. Moreover, the MATLAB simulation platform is used for simulation testing. Testing results prove the effectiveness of the combined method.

Reactive Power Optimization with Chaotic Firefly Algorithm and Particle Swarm Optimization in A Distribution Subsystem Network

In this paper the minimization of power losses in a real distribution network have been described by solving reactive power optimization problem. The optimization has been performed and tested on Konya Eregli Distribution Network in Turkey, a section of Turkish electric distribution network managed by MEDAŞ (Meram Electricity Distribution Corporation). The network contains about 9 feeders, 1323 buses (including 0.4 kV, 15.8 kV and 31.5 kV buses) and 1311 transformers. This paper prefers a new Chaotic Firefly Algorithm (CFA) and Particle Swarm Optimization (PSO) for the power loss minimization in a real distribution network. The reactive power optimization problem is concluded with minimum active power losses by the optimal value of reactive power. The formulation contains detailed constraints including voltage limits and capacitor boundary. The simulation has been carried out with real data and results have been compared with Simulated Annealing (SA), standard Genetic Algorithm (SGA) and standard Firefly Algorithm (FA). The proposed method has been found the better results than the other algorithms.

Diversity of Meq gene from clinical Marek's disease virus infection in Saudi Arabia.

Aim:The aim of this study was to demonstrate the genomic features of Meq gene of Marek’s disease virus (MDV) recently circulating in Saudi Arabia (SA).Materials and Methods:Two poultry flocks suffering from mortalities and visceral tumors were presented to the Veterinary Teaching Hospital, King Faisal University, SA. Subjected to different diagnostic procedures: Case history, clinical signs, and necropsy as well as polymerase chain reaction followed by Meq gene sequence analysis.Results:Case history, clinical signs, and necropsy were suggestive of MDV infection. The Meq gene was successfully detected in liver and spleen of infected chickens. A 1062 bp band including the native Meq ORF in addition to a 939 bp of S-Meq (short isoform of Meq) were amplified from Saudi 01-13 and Saudi 02-13, respectively. The nucleotide and deduced amino acids sequences of the amplified Meq genes of both Saudi isolates showed distinct polymorphism when compared with the standard USA virulent isolates Md5 and GA. The sequence analysis of the S-Meq gene showed a 123 bp deletion representing 41 amino acids between two proline-rich areas without any frameshift. The Meq gene encoded four repeats of proline-rich repeats (PRRs sequences), whereas the S-Meq contains only two PRRs. Interestingly, the phylogenetic analysis revealed that both of SA MDV isolates are closely related to the MDV strains from Poland.Conclusion:The two MDV isolates contain several nucleotide polymorphisms resulting in distinct amino acid substitutions. It is suggested that migratory and wild birds, as well as world trading of poultry and its by-products, have a great contribution in the transmission of MDVs overseas.

Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges

During the service life of bridges, the bridge management systems (BMSs) seek to handle all performed assessment activities by controlling regular inspections, evaluations, and maintenance of these structures. However, the BMSs still rely heavily on qualitative and visual bridge inspections, which compromise the structural evaluation and, consequently, the maintenance decisions as well as the avoidance of bridge collapses. The structural health monitoring appears as a natural field to aid the bridge management, providing more reliable and quantitative information. Herein, the machine learning algorithms have been used to unveil structural anomalies from monitoring data. In particular, the Gaussian mixture models (GMMs), supported by the expectation-maximization (EM) on the parameter estimation, have been proposed to model the main clusters that correspond to the normal and stable state conditions of a bridge, even when it is affected by unknown sources of operational and environmental variations. Unfortunately, the performance of the EM algorithm is strongly dependent on the choice of the initial parameters. This paper proposes a hybrid approach based on a standard genetic algorithm (GA) to improve the stability of the EM algorithm on the searching of the optimal number of clusters and their parameters, strengthening the damage classification performance. The superiority of the GA-EM-GMM approach, over the classic EM-GMM one, is tested on a damage detection strategy implemented through the Mahalanobis-squared distance, which permits one to track the outlier formation in relation to the chosen main group of states, using real-world data sets from the Z-24 Bridge, in Switzerland. Copyright © 2016 John Wiley & Sons, Ltd.

A coordinated signal control method for arterial road of adjacent intersections based on the improved genetic algorithm

To reduce the vehicle delay effectively, a coordinated signal control method for arterial road of adjacent intersections has been proposed, which is based on the genetic algorithm improved. The method takes the signal coordinated control system for arterial road of adjacent intersections as the research object, by analyzing the signal states encountered by the head and tail of fleet before the crossing of the arterial road, and adopting the minimum total delay of waiting queue team as the performance index, to establish the coordinate control system optimization model for arterial road, in which a cycle length, signal timing and phase difference are optimization parameters. And the method introduce minimum spanning tree clustering idea to improve the defects of the standard genetic algorithm (Standard Genetic Algorithms, SGA), and then optimizes the aforementioned the coordinate control system optimization model for arterial road. The experiment shows that the results obtained by the improved genetic algorithm are better than the ones obtained by standard genetic algorithm and traditional algebraic method respectively.