Global Search Algorithm Research Articles

Application of focal-time analysis for improved induced seismicity depth control: A case study from the Montney Formation, British Columbia, Canada

Characterization of induced seismicity and associated microseismicity is an important challenge for enhanced oil recovery and development of tight hydrocarbon reservoirs. In particular, accurately correlating hypocenters of induced events to stratigraphic layers plays an important role in understanding the mechanisms of fault activation. Existing methods for estimating focal depth, however, are prone to a high degree of uncertainty. A comprehensive analysis of inferred focal depths is applied to induced events that occurred during completions of horizontal wells targeting the Montney Formation in British Columbia, Canada. Our workflow includes a probabilistic, nonlinear global-search algorithm (NonLinLoc), a hierarchical clustering algorithm for relative relocation (GrowClust), and depth refinement using the recently developed focal-time method. The focal-time method leverages stratigraphic correlations between P-P and P-S reflections to eliminate the need for an explicit velocity model developed specifically for hypocenter depth estimation. We find that this approach is robust in the presence of noisy picks and location errors from epicenters obtained using a global-search algorithm, but it is limited to areas where multicomponent 3D seismic data are available. We have developed a novel method to determine statics corrections to ensure that the passive seismic observations and 3D seismic data share a common datum in areas of moderate to high topography. Our results highlight the importance of transverse faults, which appear to provide permeable pathways for activation of other faults at distances of up to 2 km from hydraulic fracturing operations.

Design of evolutionary optimized finite difference based numerical computing for dust density model of nonlinear Van-der Pol Mathieu’s oscillatory systems

In this study, a new evolutionary optimized finite difference based computing paradigm is presented for dynamical analysis of dust density model for the ensemble of electrical charges and dust particles represented with nonlinear oscillatory system based on hybridization of Van-der Pol and Mathieu equation (VDP-ME). Strength of accurate and effective discretization ability of finite difference method (FDM) is exploited to transform VDP-ME to equivalent nonlinear system of algebraic equations. The residual error based fitness function of the transformed model is constructed by the competency of approximation theory in mean square sense. The optimization of the residual error of the system through hybrid meta-heuristic computing paradigm GA-SQP; genetic algorithm (GA) for viable global search aided with rapid fine tuning of sequential quadratic programming (SQP). The proposed GA-SQP-FDM is applied on variants of dust density model of VDP-ME by varying the rate of charged dust grain production, as well as, loss and comparison of results with state of art numerical procedure established the worth of the scheme in terms of accuracy and convergence measures endorsed through statistical observations on large dataset.

An integrated feedforward-feedback control structure utilizing a simplified global gravitational search algorithm to control nonlinear systems

This paper presents an integrated feedforward-feedback control structure to control nonlinear dynamical systems. This intelligent control system exploits a modified recurrent wavelet neural network (MRWNN) in the feedforward (FF) and the feedback (FB) loops of the control structure. Specifically, the MRWNN is proposed to boost the approximation performance of a previously reported network by employing two amendments to the original structure. To optimize the parameters of both the FF and the FB controllers, an enhanced version of the gravitational search algorithm (GSA) is developed to improve the searching capability of the original algorithm. In particular, two modifications were adopted, including the removal of two control parameters related to the gravitational constant in the original algorithm and the utilization of the global best solution to constitute the next generation of agents. Hence, the proposed algorithm is called the simplified global gravitational search algorithm (SGGSA), which has demonstrated better optimization performance compared to those of other techniques, including the original GSA. By conducting several evaluation tests using different nonlinear time-variant dynamical systems, the effectiveness of the proposed control structure was confirmed in terms of control precision and robustness against external disturbances. In addition, the MRWNN has exhibited a superior control performance compared with other related controllers.

A novel global harmony search algorithm for solving numerical optimizations

Harmony search (HS) is a type of population-based optimization algorithm that is introduced based on the idea of musical instruments being tuned to obtain the best harmony state. Several versions of HS have been presented, with global harmony search (GHS) considered one of the most popular. Although GHS is efficient in solving various optimization problems, a new position updating mechanism has been added to improve its efficiency and help it avoid getting stuck in local minima. The novel algorithm proposed in this paper is called the intersect mutation global harmony search algorithm (IMGHSA), which has been tested and evaluated on a set of well-known benchmark functions. The IMGHSA is compared with several improved variants of the HS algorithm, such as the basic version of harmony search (HS), improved differential harmony search, generalized opposition-based learning with global harmony search , and novel global harmony search. The experimental results show that the proposed IMGHSA performs better than the state-of-the-art HS variants and has a more robust convergence when optimizing objective functions in terms of the solution accuracy and efficiency.

Data field application in removing large P-phase arrival picking errors and relocating a mine microseismic event

Seismic source location is a key parameter in the microseismic (MS) monitoring technology, and its accuracy is correlated with the calculation of some other event source parameters (e.g., event magnitude and focal mechanism). The conventional location methods usually take advantages of objective functions based on all arrival-time dataset and solve them through optimization algorithms, while a local optimization may be obtained due to the influence of initial iteration point. Even if a global grid search algorithm is applied, it is likely to obtain a poor location result when there are large P-phase arrival picking errors. Therefore, this study proposed a method to remove large picking errors from P-phase arrival time dataset and relocate the seismic source based on bootstrap sampling selected sub datasets and data field theory. Its basic principles are shown as follows: by using the time difference (TD2) method through the simplex algorithm, seismic sources are located based on sub datasets of P-phase arrival time from multiple bootstrap sampling, and the location mean value of points with the 50 largest potential values in the data field is taken as the approximate location result of a MS event. Furthermore, according to the approximate location, the difference between theoretical travel time of P waves at each sensor and travel time based on the observed arrival time is calculated, and then a threshold value is set to remove large picking errors. Furthermore, by repeating the above steps of bootstrap sampling and data field based location, an accurate relocation is obtained. The synthetic tests and application were based on sensor locations of the Institute of Mine Seismology (IMS) acquisition system settled in the Yongshaba mine (China). Two typical synthetic events were separately set inside and outside the sensor array, and eight blasting events with known locations were treated as test data. These events were located and studied by utilizing the TD2 method, data field based TD2 method (DF1–TD2) and data field based TD2 method with large picking errors removed (DF2–TD2). Results demonstrate that the TD2 method shows a poor location stability when large picking errors are present, while location errors obtained by the DF1–TD2 and DF2–TD2 methods are obviously smaller than that of the TD2 method. Moreover, location results obtained by the DF2–TD2 method with large picking errors removed are superior to those without removing large picking errors. Furthermore, the DF2–TD2 method was applied to locate 300 MS events, the location elevations are well consistent with the stope distribution, showing that the method has a good application prospect. Finally, the data field theory was combined with other location objective functions and P-phase arrival time picked by the Akaike information criterion (AIC) method, and we obtained good location results. In addition, the data field based location results obtained through a grid search algorithm and the simplex algorithm are similar. Therefore, the combinations of data field with different objective functions, automatic P-phase arrival picking methods and iteration algorithms show broad prospects.

Local search algorithms based on benchmark test functions problem

<p>Optimization process is normally implemented to solve several objectives in the form of single or multi-objectives modes. Some traditional optimization techniques are computationally burdensome which required exhaustive computational times. Thus, many studies have invented new optimization techniques to address the issues. To realize the effectiveness of the proposed techniques, implementation on several benchmark functions is crucial. In solving benchmark test functions, local search algorithms have been rigorously examined and employed to diverse tasks. This paper highlights different algorithms implemented to solve several problems. The capacity of local search algorithms in the resolution of engineering optimization problem including benchmark test functions is reviewed. The use of local search algorithms, mainly Simulated Annealing (SA) and Great Deluge (GD) according to solve different problems is presented. Improvements and hybridization of the local search and global search algorithms are also reviewed in this paper. Consequently, benchmark test functions are proposed to those involved in local search algorithm.</p>

Consideration of Uncertainties in Analyzing Nuclear Power Facilities’ Fire Hazard

Paying increased attention to the safety of nuclear power facilities is a key element for their further development. An approach to analyzing their fire safety with due regard to the uncertainty factors associated with the parameters of applied models and with the fire breakout and development conditions is presented. The two-zone fire modeling code that takes into account the specific features of transportable (floating) nuclear power plants is used, and its basic characteristics are given. An original approach to a dynamic probabilistic safety assessment is applied, which is based on a global optimum search algorithm. This algorithm is used in this study for taking into account the uncertainties associated with combustion models, possible human actions, and change in the equipment’s operation modes. The proposed method incorporates a genetic algorithm for searching the global optimum, the main purpose of which is to find the most hazardous fire development scenarios in accordance with adaptive variation of parameters within their uncertainty boundaries. Application of the method is demonstrated for two versions of fire breakout in the process rooms of a transportable power unit equipped with a nuclear power installation. The basic parameters of the two-zone fire model and dynamic probabilistic safety assessment are given. The results of computations for the dynamic probabilistic safety assessment demonstrate the complex nature of interaction among different processes. The data from analyzing the influence of the main parameters are given, a comparison with the standard calculation version and with the use of uncertainty analysis is carried out, and the conditional probability of the worst scenario is estimated. Owing to the consideration of uncertainties and the search for the most dangerous scenarios, it became possible to establish the fire development scenarios that can lead to essentially more severe consequences than those determined in using the standard parameters of two-zone fire models.

A Novel Application of Pattern Search Algorithm for Efficient Estimation of Channel State Information in MIMO Network

A novel intelligent computation mechanism is proposed for the estimation of channel state information in MIMO architecture. In this computing technique, the global optimizer Pattern Search algorithm is used to find the accurate channel state information which is based on coefficients of the channel matrix. The pattern search algorithm is exploited for the square channel matrix which consists of an equal number of array elements at both transmitter and receiver end of the communication system. Rayleigh fading channel modeling is explored in this work for evaluating the response of received signal over channel matrix of different dimensions. Also, two cases of measured white Gaussian noise are applied for evaluating the trend of algorithm which are without noise and 20 dB of noise. A Mean Square Error based evaluation function is formulated for assessing the true response of channel coefficients with the estimated one. This error function is effective enough to reach the best coefficient with only one symbol. The efficiency and performance of the pattern search algorithm are examined with different types of analysis like accuracy, convergence, and computational complexity on the bases of statistical parameters. A sufficient number of absolute Monte-Carlo simulations are practiced for the extensive statistical study of the proposed algorithm.

A Hybrid Diffractive Optical Element Design Algorithm Combining Particle Swarm Optimization and a Simulated Annealing Algorithm

With the rapid development of computer hardware and the emergence of the parallel calculation of diffraction fields, a breakthrough has been made in terms of the limitation of the unacceptable amount of computational cost to design diffractive optical elements (DOEs), and more accurate global search algorithms can be introduced to the design of complex DOEs and holographic projections instead of traditional iterative algorithms. In this paper, a hybrid algorithm which combines particle swarm optimization (PSO) with a simulated annealing (SA) algorithm is proposed for the designing of DOEs and projecting holographic images with less noise. PSO is used to reduce the invalid disturbance in SA, and SA can jump out from local extreme points to find the global extreme points. Compared with the traditional Gerchberg–Saxton (GS) algorithm, the simulation and experimental results demonstrate that the proposed SA–PSO hybrid algorithm can improve uniformity by more than 10%.

Using memetic algorithm for robustness testing of contract-based software models

Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms.

A Comparison of Metaheuristic Algorithms for Solving the Piezoresistive Inverse Problem in Self-Sensing Materials

Nanofiller-modified materials have been widely explored for self-sensing in diverse venues spanning civil, aerospace, robotic, and even biomedical applications. Key to self-sensing is the fact that these materials are piezoresistive – their electrical conductivity is influenced by deformation and damage. Considerable research has aimed at leveraging this for intrinsic self-sensing. However, prevailing techniques provide no information about the underlying mechanical state of the material. Instead, damage and strain must be indirectly inferred from conductivity changes. The cause of this limitation is that recovering mechanics from electrical measurements is an ill-posed, under-determined, and multi-modal inverse problem. As such, global search algorithms coupled with physically motivated constraints must be used to obtain a solution. Previously, it was demonstrated that genetic algorithms (GAs) provide a suitable means of solving the displacement-from-conductivity problem. These algorithms, while undoubtedly powerful, suffer from artifacts in the strain solutions and variability in the solutions between successive searches. The goal of this work is to explore other commonly used global search algorithms which can be used to solve the displacement-from-conductivity inverse problem. In addition to GAs, we focus on two prevailing types of metaheuristic global search algorithms: simulated annealing and particle swarm optimization. Each algorithm is tested on experimental data where a self-sensing nanocomposite was deformed and electrical impedance tomography was used to image the conductivity change. The results for each algorithm are compared to standard finite element simulations and experimental observations via digital image correlation. A comparison is then drawn between the three algorithms in terms of solution quality, variability, accuracy, and computational efficiency.

A hybrid achromatic metalens

Metalenses, ultra-thin optical elements that focus light using subwavelength structures, have been the subject of a number of recent investigations. Compared to their refractive counterparts, metalenses offer reduced size and weight, and new functionality such as polarization control. However, metalenses that correct chromatic aberration also suffer from markedly reduced focusing efficiency. Here we introduce a Hybrid Achromatic Metalens (HAML) that overcomes this trade-off and offers improved focusing efficiency over a broad wavelength range from 1000–1800 nm. HAMLs can be designed by combining recursive ray-tracing and simulated phase libraries rather than computationally intensive global search algorithms. Moreover, HAMLs can be fabricated in low-refractive index materials using multi-photon lithography for customization or using molding for mass production. HAMLs demonstrated diffraction limited performance for numerical apertures of 0.27, 0.11, and 0.06, with average focusing efficiencies greater than 60% and maximum efficiencies up to 80%. A more complex design, the air-spaced HAML, introduces a gap between elements to enable even larger diameters and numerical apertures.

Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates

Manufacturing industry reflects a country’s productivity level and occupies an important share in the national economy of developed countries in the world. Jobshop scheduling (JSS) model originates from modern manufacturing, in which a number of tasks are executed individually on a series of processors following their preset processing routes. This study addresses a JSS problem with the criterion of minimizing total quadratic completion time (TQCT), where each task is available at its own release date. Constructive heuristic and meta-heuristic algorithms are introduced to handle different scale instances as the problem is NP-hard. Given that the shortest-processing-time (SPT)-based heuristic and dense scheduling rule are effective for the TQCT criterion and the JSS problem, respectively, an innovative heuristic combining SPT and dense scheduling rule is put forward to provide feasible solutions for large-scale instances. A preemptive single-machine-based lower bound is designed to estimate the optimal schedule and reveal the performance of the heuristic. Differential evolution algorithm is a global search algorithm on the basis of population, which has the advantages of simple structure, strong robustness, fast convergence, and easy implementation. Therefore, a hybrid discrete differential evolution (HDDE) algorithm is presented to obtain near-optimal solutions for medium-scale instances, where multi-point insertion and a local search scheme enhance the quality of final solutions. The superiority of the HDDE algorithm is highlighted by contrast experiments with population-based meta-heuristics, i.e., ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). Average gaps 45.62, 63.38 and 188.46 between HDDE with ACO, PSO and GA, respectively, are demonstrated by the numerical results with benchmark data, which reveals the domination of the proposed HDDE algorithm.

A Deep Cycle Limit Learning Machine Method for Urban Expressway Traffic Incident Detection

In Beijing, Shanghai, Hangzhou, and other cities in China, traffic congestion caused by traffic incidents also accounts for 50% to 75% of the total traffic congestion on expressways. Therefore, it is of great significance to study an accurate and timely automatic traffic incident detection algorithm for ensuring the operation efficiency of expressways and improving the level of road safety. At present, many effective automatic event detection algorithms have been proposed, but the existing algorithms usually take the original traffic flow parameters as input variables, ignoring the construction of feature variable sets and the screening of important feature variables. This paper presents an automatic event detection algorithm based on deep cycle limit learning machine. The traffic flow, speed, and occupancy of downstream urban expressway are extracted as input values of the deep-loop neural network. The initial connection weights and output thresholds of the deep-loop neural network are optimized by using the improved particle swarm optimization (PSO) algorithm for global search. The higher classification accuracy of the extreme learning machine is trained, and the generalization performance of the extreme learning machine is improved. In addition, the extreme learning machine is used as a learning unit for unsupervised learning layer by layer. Finally, the microwave detector data of Tangqiao viaduct in Hangzhou are used to verify the experiment and compared with LSTM, CNN, gradient-enhanced regression tree, SVM, BPNN, and other methods. The results show that the algorithm can transfer low-level features layer by layer to form a more complete feature representation, retaining more original input information. It can save expensive computing resources and reduce the complexity of the model. Moreover, the detection accuracy of the algorithm is high, the detection rate is higher than 98%, and the false alarm rate is lower than 3%. It is better than LSTM, CNN, gradient-enhanced regression tree, and other algorithms. It is suitable for urban expressway traffic incident detection.

An Improved Lagrange Particle Swarm Optimization Algorithm and Its Application in Multiple Fault Diagnosis

The fault rate in equipment increases significantly along with the service life of the equipment, especially for multiple fault. Typically, the Bayesian theory is used to construct the model of faults, and intelligent algorithm is used to solve the model. Lagrangian relaxation algorithm can be adopted to solve multiple fault diagnosis models. But the mathematical derivation process may be complex, while the updating method for Lagrangian multiplier is limited and it may fall into a local optimal solution. The particle swarm optimization (PSO) algorithm is a global search algorithm. In this paper, an improved Lagrange-particle swarm optimization algorithm is proposed. The updating of the Lagrangian multipliers is with the PSO algorithm for global searching. The difference between the upper and lower bounds is proposed to construct the fitness function of PSO. The multiple fault diagnosis model can be solved by the improved Lagrange-particle swarm optimization algorithm. Experiment on a case study of sensor data-based multiple fault diagnosis verifies the effectiveness and robustness of the proposed method.

Certified Efficient Global Roundness Evaluation

We propose and analyze a global search algorithm for the computation of the minimum zone sphericity (circularity) error of a given set. The formulation is valid in any dimension and covers both finite sets of data points as well as infinite sets like polygonal chains or triangulations of surfaces. We derive theoretical estimates for the cost to reach a desired accuracy, and validate the performance of the algorithm with various numerical experiments.

Hybrid multi-objective orbit-raising optimization with operational constraints

The optimal design of orbit raising trajectories is formulated within a multi-objective hybrid optimal control framework. The spacecraft can be equipped with chemical, electric or combined chemical-electric propulsion systems. The model incorporates realistic effects of the space environment and complex operational constraints. An automated solution strategy, based on two sequential steps, is proposed for this problem. In the first step, operational constraints are not enforced and the control law of the electric engine is parameterized by a Lyapunov function. A heuristic global search algorithm selects the propulsion system and optimizes the guidance law. Approximate Pareto-optimal solutions are obtained trading off propellant mass, time of flight and solar-cell degradation. In the second step, candidate solutions are deemed as initial guesses to solve the nonlinear programming problem resulting from direct transcription of the operationally constrained problem. The proposed approach is applied to two transfer scenarios to the geostationary orbit. Results show the effectiveness of the methodology to generate not only rapid performance estimates for preliminary trade studies, but also accurate calculations for the detailed design. Additionally, it is identified that the application of operational restrictions causes minor penalties in the objective function.

Optimal rotations with declining discount rate: incorporating thinning revenues and crop formation costs in a cross-European comparison

Schedules of declining discount rates have been advocated, and adopted by several European governments. They undermine classical solutions to forest economics problems, especially optimal rotation. Adapting classical first-order conditions created problems of local optimisation. A global search algorithm allowed inclusion of initial costs and thinning revenues. It produced results according with expectations – lengthening rotations as time progressed – and results paralleling those for constant discount rates – shorter rotations for high productivity and unthinned crops, and with zero crop formation costs. Apparent anomalies in the pattern of rotations are the due result of opportunity costs from later rotations, which increase as discount rate declines. Sometimes the solution oscillates, usually owing to steps in the discount schedule or irregular profile of felling revenues. Inspection allows the most profitable result to be identified.

Artificial fish swarm optimization algorithm for power system state estimation

In this paper, the power system state estimation (SE) problem is formulated as a general non-linear programming problem with equality constraints and boundary limits on the state variables. The proposed SE problem is solved using an evolutionary based Artificial Fish Swarm Optimization Algorithm (AFSOA). The AFSOA is a global search algorithm based on the characteristics of fish swarm and its autonomous model. The detailed algorithm with its flow chart is presented in this paper. To show the effectiveness of the proposed SE approach, six bus test system is considered. The obtained results are compared with other algorithms reported in the literature.

Modification of geometrical and electronic structures of anionic and neutral silicon clusters by double-doped tantalum atoms

In this work, structural evolution, geometrical and electronic structures of Ta2Sin − and Ta2Sin (n = 3–21) clusters are investigated by joint Crystal structure AnaLYsis by Particle Swarm Optimisation (CALYPSO) global search algorithm and quantum chemical calculations. n = 21 is confirmed as the critical size of forming a Ta2-endohedral silicon cage-like structure for both anionic and neutral Ta2Sin clusters. It is found that the two Ta atoms tend to occupy the high coordination sites and stay close as a whole to bond with the Sin frames, as a result, the global minima of Ta2Sin − /0 (n = 3–21) clusters can be all viewed as a central Ta–Ta bonding axis interacted with the surrounding Si atoms. Ta2Sin − /0 (n = 3−13) clusters are majorly dominated by the bipyramid or prism-based geometries and Ta2Sin − /0 (n = 14−18) clusters are primarily governed by the polyhedral cage-like geometries, whereas Ta2Sin − /0 (n = 19−21) clusters are mainly controlled by the prolate geometries. In particular, Ta2Si3 − has a D 3h symmetric trigonal bipyramid structure, and Ta2Si6 − adopts C 2h symmetric chair-shaped structure and Ta2Si12 − is a C 6v symmetric Ta-face-capped hexagonal antiprism with the other Ta atom at the interior of Si12 cage. The odd-even alternations for the relative stabilities of Ta2Sin − /0 are observed.