Global Search Optimization Research Articles

Diagnosis of Faulty Elements in Array Antenna using Nature Inspired Cuckoo Search Algorithm

Detection and correction of faulty elements in a linear array have great importance in radar, sonar, mobile communications and satellite. Due to single element failure, the whole radiation pattern damage in terms of side lobes level and nulls. Once we have detect the position of defective element, then correction method is applied to achieve the desired pattern. In this work, we introduce a nature inspired meta-heuristic cuckoo search algorithm to diagnose the position of defective elements in a linear array. The nature inspired cuckoo search algorithm is new to the optimization family and is used first time for fault detection in an array antenna. Cuckoo search algorithm is a global search optimization technique. The cost function is used as a fitness function which defines an error between the degraded far field power pattern and the estimated one. The proposed technique is used effectively for the diagnosis of complete, as well as, for partial faulty elements position. Different simulation results are evaluated for 40 elements Taylor pattern to validate and check the performance of the proposed technique.

RETRACTED ARTICLE: Dynamic game model analysis of marketing resources allocation optimization

This paper analyzes the dynamic game model of optimizing the allocation of marketing resources, and investigates the status and trend of the marketing of an enterprise, and points out the significance of the subject in the marketing decision system of the enterprise, and establishes the expert decision-making system for the enterprise and forecasting systems to provide reliable data support. We select the programming environment and genetic algorithm toolbox as a software platform to learn and improve the tool functions used in the toolbox to meet the design requirements. From the analysis of the results of this paper, it is shown that the global search optimization performance of GA is fully demonstrated, and the reasonable design of the algorithm can make it well combined with the purpose of this paper.

Neural network and fuzzy system for the tuning of Gravitational Search Algorithm parameters

A good trade-off between exploration and exploitation to find optimal values in search algorithms is very hard to achieve. On the other hand, the combination of search methods may cause computational complexity increase problems. The Gravitational Search Algorithm (GSA) is a swarm optimization algorithm based on the law of gravity, where the solution search process depends on the velocity of particles. The application of intelligent techniques can improve the search performances of GSA. This paper proposes the design of a Neuro and Fuzzy Gravitational Search Algorithm (NFGSA) to achieve better results than GSA in terms of global optimum search capability and convergence speed, without increasing the computational complexity. Both the algorithms have the same computational complexity O(nd), where n is the number of agents and d is the search space dimension. The main task of the designed intelligent system is to adjust a GSA parameter on a revised version of GSA. NFGSA is compared with GSA, a Plane Surface Gravitational Search Algorithm (PSGSA) and a Modified Gravitational Search Algorithm (MGSA). The results show that NFGSA improves the optimization performances of GSA and PSGSA, without adding computational costs. Moreover, the proposed algorithm is better than MGSA for a benchmark function and achieves similar results for two test functions. The analysis on the computational complexity shows that NFGSA has a better computational complexity than MGSA, because NFGSA has complexity O(nd), whereas MGSA has complexity O((nd)2).

Review of Non-Traditional Optimization Methods for Allocation of Distributed Generation and Energy Storage in Distribution System

The integration of distributed energy sources transforms passive distributed grid, in which the energy flows only in one direction (from the source to the consumer), in an active one, in which energy flows in both directions. To maximize positive impacts, which distributed generation (DG) can provide to the distribution network, it is necessary to determine the optimal allocation of distributed generation. The optimal allocation can be determined by using the optimization method. There are two main categories: exact methods (traditional) and heuristic (non-traditional) methods. Exact methods search for global optimum while heuristic methods achieve satisfactory solutions with greater computation speed. This paper gives a brief review of non-traditional methods used for determining optimal location and optimal power of DG with the aim to reduce real power losses and to improve voltage characteristics. Also, there is a review of the application of those methods in determining the optimal power, optimal location and optimal cycle of charging/discharging of electrical energy storage systems.

A chaos genetic algorithm based extended Kalman filter for the available capacity evaluation of lithium-ion batteries

Lithium-ion batteries are developed rapidly in electric vehicles, whose safety and functional capabilities are influenced greatly by the evaluation of available capacity. Combined with the evolution trend description of state of charge from extended Kalman filter and an adaptive switch mechanism, this paper advances an adaptive chaos genetic algorithm based extended Kalman filter for the state of charge determination of lithium-ion batteries, where a combined state space model is used for simulating their dynamics. It combines the advantage of local linear approximation capability from extended Kalman filter with the global optimal search mechanism from chaos genetic algorithm. The method is applied for the state of charge determination of lithium-ion batteries, and results of lab tests on physical cells, compared with model prediction, are presented. Furthermore, the innovation magnitude bound test and innovation whiteness test are employed for verifying the performance of the proposed method. Results confirm that the advanced method may quickly evaluate state of charge with high accuracy and has great robustness without being affected by the uncertain initial value.

Control strategy of grid-connected photovoltaic generation system based on GMPPT method

There are multiple local maximum power points when photovoltaic (PV) array runs under partial shading condition (PSC).However, the traditional maximum power point tracking (MPPT) algorithm might be easily trapped in local maximum power points (MPPs) and cannot find the global maximum power point (GMPP). To solve such problem, a global maximum power point tracking method (GMPPT) is improved, combined with traditional MPPT method and particle swarm optimization (PSO) algorithm. Under different operating conditions of PV cells, different tracking algorithms are used. When the environment changes, the improved PSO algorithm is adopted to realize the global optimal search, and the variable step incremental conductance (INC) method is adopted to achieve MPPT in optimal local location. Based on the simulation model of the PV grid system built in Matlab/Simulink, comparative analysis of the tracking effect of MPPT by the proposed control algorithm and the traditional MPPT method under the uniform solar condition and PSC, validate the correctness, feasibility and effectiveness of the proposed control strategy.

REDUCTION OF ACTIVE POWER LOSS BY CHAOTIC SEARCH BASED ARTIFICIAL BEE COLONY ALGORITHM

This paper presents Chaotic Search Based Artificial Bee Colony Optimization Algorithm (CSABC) for solving the optimal reactive power problem. Basic Artificial Bee Colony algorithm (ABC) has the advantages of strong robustness, fast convergence and high flexibility, fewer setting parameters, but it has the disadvantages premature convergence in the later search period and the accuracy of the optimal value which cannot meet the requirements sometimes. In this paper the Chaotic Local Search method is applied to solve the reactive power problem of global optimal value. The premature convergence issue of the Artificial Bee Colony algorithm has been improved by increasing the number of scout and rational using of the global optimal value and Chaotic Search. The proposed Chaotic Search Based Artificial Bee Colony Optimization (CSABC) algorithm has been tested in stand IEEE 30, 118- bus & practical 191 Indian utility test systems. The results show that the proposed algorithm performs well in reducing the real power loss and prevent premature convergence to high degree with rapid convergence.

Efficient Tracking of Moving Target Based on an Improved Fast Differential Evolution Algorithm

Computer vision, which is used to detect and track a specific target in image sequences, has drawn great attention in recent years. The process of tracking can be formulated as a dynamic optimization problem that identifies the optimal position of the target in each image. Differential evolution (DE), who owns the advantages of simplicity, parallel computing, and self-adaptive search for global optimization, is envisioned as a promising algorithm to provide effective target tracking. In this paper, several improvements are made in DE for better adaptability in target tracking. Specifically, we introduce two inferior individuals into the mutation stage, which further enriches the diversity of the population and speeds up the offspring's evolution. We also proceed several image preprocessing and build an adaptive Gaussian mixture model of the target to deal with the complex tracking scenarios. Experimental results show that the designed tracking algorithm based on our improved DE demonstrates a higher tracking accuracy and faster tracking speed in several challenging tracking scenarios.

The Chaos and Stability of Firefly Algorithm Adjacent Individual

In this paper, in order to overcome the defect of the firefly algorithm, for example, the slow convergence rate, low accuracy and easily falling into the local optima in the global optimization search, we propose a dynamic population firefly algorithm based on chaos. The stability between the fireflies is proved, and the similar chaotic phenomenon in firefly algorithm can be simulated.

Optimum Parameters of a Tuned Liquid Column Damper in a Wind Turbine Subject to Stochastic Load

Parameter optimization for tuned liquid column dampers (TLCD), a class of passive structural control, have been previously proposed in the literature for reducing vibration in wind turbines, and several other applications. However, most of the available work consider the wind excitation as either a deterministic harmonic load or random load with white noise spectra. In this paper, a global direct search optimization algorithm to reduce vibration of a tuned liquid column damper (TLCD), a class of passive structural control device, is presented. The objective is to find optimized parameters for the TLCD under stochastic load from different wind power spectral density. A verification is made considering the analytical solution of undamped primary system under white noise excitation by comparing with result from the literature. Finally, it is shown that different wind profiles can significantly affect the optimum TLCD parameters.

Hybrid Gbest-guided Artificial Bee Colony for hard partitional clustering

Clustering is an unsupervised classification method in the field of data mining and plays an essential role in applications in diverse fields. The K-means and K-Medoids are popular examples of conventional partitional clustering methods, which have been prominently applied in various applications. However, they possess several disadvantages, e.g., final solution is dependent on the initial solution, they easily struck into a local optimum solution. The nature-inspired swarm intelligence (SI) methods are global search optimization methods, which offer to be effective to overcome deficiencies of the conventional methods as they possess several desired key features like upgrading the candidate solutions iteratively, decentralization, parallel nature, and a self organizing behavior. The Artificial Bee Colony (ABC) algorithm is one of the recent and well-known SI method, which has been shown effective in various real-world problems. However, it exhibits lack of balance in the exploration and exploitation and shows a poor convergence speed when the number of features (dimensions) increases. Therefore, we make two modifications in it to enhance its exploration and exploitation capabilities to improve quality of the clustering. First, we introduce a gbest-guided search procedure for the fast convergence, which works effectively in large number of features also as it considers all the dimensions simultaneously. Second, in order to avoid being trapped in a local optima and to enhance the information exchange (social learning) between bees for improved search, we incorporate a crossover operator of the genetic algorithm (GA) into it. The proposed strategy is named as Hybrid Gbest-guided Artificial Bee Colony (HGABC) algorithm. We compare clustering results of the HGABC with ABC, variants of the ABC and other recent competitive methods in the swarm and evolutionary intelligence domain on ten real and two synthetic data sets using external quality measures F-measure and Rand-index. The obtained results demonstrate superiority of the proposed method over its competitors in terms of efficiency and effectiveness.

An improved hybrid of particle swarm optimization and the gravitational search algorithm to produce a kinetic parameter estimation of aspartate biochemical pathways

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions.This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions.

Intelligent computing strategy to analyze the dynamics of convective heat transfer in MHD slip flow over stretching surface involving carbon nanotubes

Abstract The research work presents the dynamics of system based on the model of convective heat transfer in Magnetodydrodynamics (MHD) slip flow over stretching surface with single-wall carbon nanotubes (CNTs) by exploiting the strengths of intelligent computing procedures including artificial neural networks (ANNs), genetic algorithms (GAs) and sequential quadratic technique (SQP). Using similarity transformation, the boundary layer mechanism for the baseline nanofluidics system modeled by partial differential equations (PDEs) is reduced to coupled ordinary differential equations (ODEs). The transformed system is modeled in an unsupervised manner with feed-forward ANNs and efficient adaptation of these networks is performed with global search optimization using GA, aided with SQP for local search. The proposed scheme is evaluated to analyze the dynamics of nanofluidics by taking different concentrations of single-wall CNTs based nano-materials mixed with base fluids such as water, kerosene oil or engine oil. Reliability and correctness of the proposed scheme is established through simulation experiments with results which consistently match with Adams numerical procedure. Moreover, Monte Carlo simulations based statistical analysis has been performed to validate the accuracy of the results in terms of performance metrics of variance account for (VAF), mean absolute error (MAE) and root mean square error (RMSE).

Parametric Study and Multiobjective Optimization of Fixed-Bed Fischer–Tropsch (FT) Reactor: The Improvement of FT Synthesis Product Formation and Synthetic Conversion

A mathematical model of a fixed-bed reactor for Fischer–Tropsch synthesis (FTS) over 37% Co/SiO2 catalyst was developed to investigate the performance of the whole process for products’ selectivity and syngas conversion. The model was capable of calculating the changes of reactant and products’ concentrations, partial pressures, conversion, and selectivity. In a previous study, a series of combined novel FT and water gas shift (WGS) reaction mechanisms (eight elementary FT reaction pathways along with seven WGS kinetics models) were developed in order to calibrate and validate the mathematical model along with reaction kinetics at different experimental conditions. Such mathematical models with reaction networks can be used as a key tool to emphasize the most significant facts of FTS catalysis and chemistry. Integration of the global search optimization algorithm with the developed model was explained for estimation of kinetics parameters. Data analyses were carried out to ensure that the predicted model ...

Optimal Design of an Autotensioner in an Automotive Belt Drive System Via a Dynamic Adaptive PSO-GA

Noise, vibration, and harshness performances are always concerns in design of an automotive belt drive system. The design problem of the automotive belt drive system requires the minimum transverse vibration of each belt span and minimum rotational vibrations of each pulley and the tensioner arm at the same time, with constraints on tension fluctuations in each belt span. The autotensioner is a key component to maintain belt tensions, avoid belt slip, and absorb vibrations in the automotive belt drive system. In this work, a dynamic adaptive particle swarm optimization and genetic algorithm (DAPSO-GA) is proposed to find an optimum design of an autotensioner to solve this design problem and achieve design targets. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization (PSO) algorithm to balance the convergence rate and global optimum search ability by adaptively adjusting the search velocity during the search process. genetic algorithm (GA)-related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the PSO algorithm to further exploit optimal solutions generated by the PSO algorithm. These operators are used to diversify the swarm and prevent premature convergence. The objective function is established using a weighted-sum method, and the penalty function method is used to deal with constraints. Optimization on an example automotive belt drive system shows that the system vibration is greatly improved after optimization compared with that of its original design.

Development of local dynamic mode decomposition with control: Application to model predictive control of hydraulic fracturing

Dynamic mode decomposition with control (DMDc) is a modal decomposition method that extracts dynamically relevant spatial structures disambiguating between the underlying dynamics and the effects of actuation. In this work, we extend the concepts of DMDc to better capture the local dynamics associated with highly nonlinear processes and develop temporally local reduced-order models that accurately describe the fully-resolved data. In this context, we first partition the data into clusters using a Mixed Integer Nonlinear Programming based optimization algorithm, the Global Optimum Search, which incorporates an added feature of predicting the optimal number of clusters. Next, we compute the reduced-order models tailored to each cluster by applying DMDc within each cluster. The developed models are subsequently used to compute approximate solutions to the original high-dimensional system and to design a feedback control system of hydraulic fracturing processes for the computation of optimal pumping schedules.

Study of probability integration method parameter inversion by the genetic algorithm

In order to obtain accurate probability integration method (PIM) parameters for surface movement of multi-panel mining, a genetic algorithm (GA) was used to optimize the parameters. As the measured surface movement is affected by more than one mining panel, traditional PIM parameter inversion model is difficult to ensure the reliability of the results due to the complexity of rock movement. With crossover, mutation and selection operators, GA can perform a global optimization search and has high computation efficiency. Compared with the pattern search algorithm, the fitness function can avoid falling into local minima traps. GA reduces the risk of local minima traps which improves the accuracy and reliability with the mutation mechanism. Application at Xuehu colliery shows that GA can be used to inverse the PIM parameters for multi-panel surface movement observation, and reliable results can be obtained. The research provides a new way for back-analysis of PIM parameters for mining subsidence under complex conditions.

Parallel genetic algorithms on the graphics processing units using island model and simulated annealing

To solve a non-deterministic polynomial-hard problem, we can adopt an approximate algorithm for finding the near-optimal solution to reduce the execution time. Although this approach can come up with solutions much faster than brute-force methods, the downside of it is that only approximate solutions are found in most situations. The genetic algorithm is a global search heuristic and optimization method. Initially, genetic algorithms have many shortcomings, such as premature convergence and the tendency to converge toward local optimal solutions; hence, many parallel genetic algorithms are proposed to solve these problems. Currently, there exist many literatures on parallel genetic algorithms. Also, a variety of parallel genetic algorithms have been derived. This study mainly uses the advantages of graphics processing units, which has a large number of cores, and identifies optimized algorithms suitable for computation in single instruction, multiple data architecture of graphics processing units. Furthermore, the parallel simulated annealing method and spheroidizing annealing are also used to enhance performance of the parallel genetic algorithm.

The AFSA-GA Algorithm for the Quay Crane Scheduling Problem of the Loading and Unloading Operations

In order to improve the efficiency of container terminals, eliminate the empty quay cranes movements, the simultaneous loading and unloading operations in same ship-bay is advanced. The AFSA-GA algorithm is proposed to solve the mixed integer programming model of the dual-cycle operation, which take advantage of the strong local search ability of GA and the global optimum search ability of AFSA. The experiment shows that AFSA-GA algorithm can improve the operation efficiency of quay crane significantly.

Global-Simplex Optimization Algorithm Applied to FEM-Based Optimal Design of Electric Machine

Finite element analysis-based optimal design of electric machines takes up a considerable amount of time for characteristic analyses and the analysis region has many local optima. Thus, selecting the appropriate global search optimization algorithm, known as the with fast convergence characteristics is necessary in the optimal design of electric machines. In this paper, a novel global search optimization algorithm known as the global-simplex optimization (GSO) algorithm, is proposed. The GSO is an optimization algorithm that adopts the characteristics of the simplex method for searching the optimum point but utilizes its global searching capability by scattering many initial points at the beginning of the algorithm. The global searching capability and convergence speed of GSO is validated through a comparison with particle swarm optimization, which has already been proven for its global searching capability through its application in the well-known Goldstein-Price benchmark function. In addition, the GSO has been applied to the optimal design of an interior permanent magnet synchronous machine to reduce the torque ripple and total harmonic distortion in its back electromotive force.