Adaptive Fireworks Algorithm Research Articles

Fault Detection of Turntable Bearing of Engineering Lifting Machinery Based on Adaptive Fireworks Algorithm

Fault Detection of Turntable Bearing of Engineering Lifting Machinery Based on Adaptive Fireworks Algorithm

A 3D Image Registration Method for Laparoscopic Liver Surgery Navigation

At present, laparoscopic augmented reality (AR) navigation has been applied to minimally invasive abdominal surgery, which can help doctors to see the location of blood vessels and tumors in organs, so as to perform precise surgery operations. Image registration is the process of optimally mapping one or more images to the target image, and it is also the core of laparoscopic AR navigation. The key is how to shorten the registration time and optimize the registration accuracy. We have studied the three-dimensional (3D) image registration technology in laparoscopic liver surgery navigation and proposed a new registration method combining rough registration and fine registration. First, the adaptive fireworks algorithm (AFWA) is applied to rough registration, and then the optimized iterative closest point (ICP) algorithm is applied to fine registration. We proposed a method that is validated by the computed tomography (CT) dataset 3D-IRCADb-01. Experimental results show that our method is superior to other registration methods based on stochastic optimization algorithms in terms of registration time and accuracy.

Defect Data Association Analysis of the Secondary System Based on AFWA-H-Mine

The fault data of the secondary system of smart substations hide some information that the association analysis algorithm can mine. The convergence speed of the Apriori algorithm and FP-growth algorithm is slow, and there is a lack of indicators to evaluate the correlation of association rules and the method to determine the parameter threshold. In this paper, the H-mine algorithm is used to realize the fast mining of fault data. The algorithm can traverse data faster by using the data structure of the H-struct. This paper also sets the lift and CF value to screen the association rules with good correlation. When setting the three key parameters of association analysis, namely, support threshold, confidence threshold, and lift threshold, an objective function composed of weighted average lift, CF value, and data coverage rate was selected, and the adaptive fireworks algorithm was used to optimize the parameters in the association analysis. In particular, the rule screening strategy is introduced in fault cause analysis in this paper. By eliminating rules with high similarity, derived signals in association rules are eliminated to the greatest extent to improve the readability of rules and ensure easy understanding of results.

Denim-fabric-polishing robot size optimization based on global spatial dexterity

Abstract. This paper presents a novel method to make denim-fabric-polishing robots perform their primary task flexibly and efficiently within a limited workspace. Link lengths are optimized based on an adaptive fireworks algorithm to improve the comprehensive dexterity index. A forward kinematics analysis of the denim-fabric-polishing robot is conducted via the D–H method; the workspace is analyzed according to the needs at hand to determine the range of motion of each joint. To solve the movement condition number of the Jacobian matrix, the concept of low-condition-number probability is established, and a comprehensive dexterity indicator is constructed. The influence of the robot's size on the condition number and comprehensive dexterity index is determined. Finally, the adaptive fireworks algorithm is used to establish the objective optimization function by integrating the dexterity index and other performance indicators. The optimization results show that when the comprehensive dexterity index is taken as the optimization objective, the dexterity comprehensive index and other performance indices of the robot are the lowest; that is, the robot is more flexible. Compared with the traditional genetic algorithm and particle swarm algorithm, the adaptive fireworks algorithm proposed in this paper has better solving speed and solving precision. The optimized workspace of the robot meets the requirements of the polishing task. The design also yields a sufficiently flexible, efficient, and effective robot.

Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling

At the present stage, China’s energy development has the following characteristics: continuous development of new energy technology, continuous expansion of comprehensive energy system scale, and wide application of multi-energy coupling technology. Under the new situation, the accurate prediction of power load is the key to alleviate the problem that the planning and dispatching of the current power system is more complex and more demanding than the traditional power system. Therefore, firstly, this paper designs the calculation method of the power load demand of the grid under the multi-energy coupling mode, aiming at the important role of the grid in the power dispatching in the comprehensive energy system. This load calculation method for regional power grid operating load forecasting is proposed for the first time, which takes the total regional load demand and multi-energy coupling into consideration. Then, according to the participants and typical models in the multi-energy coupling mode, the key factors affecting the load in the multi-energy coupling mode are analyzed. At this stage, we fully consider the supply side resources and the demand side resources, innovatively extract the energy system structure characteristics under the condition of multi-energy coupling technology, and design a key factor index system for this mode. Finally, a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is proposed, to carry out load forecasting for multi-energy coupling scenarios. Aiming at the complexity energy system analysis and prediction accuracy improvement of multi-energy coupling scenarios, this method applies minimal redundancy maximal relevance model to the selection of key factors in scenario analysis. It is also the first time that adaptive fireworks algorithm is applied to the optimization of adaptive fireworks algorithm, and the results show that the model optimization effect is good. In the case of A region quarterly load forecasting in southwest China, the average absolute percentage error of a least squares support vector machine optimized by the minimal redundancy maximal relevance model and the adaptive fireworks algorithm (mRMR-AFWA-LSSVM) is 2.08%, which means that this model has a high forecasting accuracy.

A Quick Deployment Method for Sonar Buoy Detection Under the Overview Situation of Underwater Cluster Targets

Aiming at the typical problems of deploying sonar buoys in appointed sea area, this paper summarizes two problems existing in previous studies and puts forward a quick deployment method for sonar buoys detection under the overview situation of underwater cluster targets. Firstly, considering the influence of an underwater target course on target strength, the overlapping coefficient ”buoy group” mode is introduced to deploy the array. And combining with the random distribution law of underwater targets in the exploration area, the mathematical optimization model for sonar buoys detection under the overview situation of underwater cluster targets is established. Then, the fitness function corresponding to the buoy deployment optimization model is defined, and the adaptive fireworks algorithm is used to solve the optimization problem for obtaining the sonar buoys deployment scheme. Finally, through the comparison and analysis of results for the seven group simulation experiments, the conclusions that are beneficial to improve the detection efficiency of the sonar buoy deployment are obtained. The proposed method can provide useful support for underwater cluster multi-target detection and the problem of counterattack underwater cluster multi-platform.

Improved Iterative Closest Point(ICP) 3D point cloud registration algorithm based on point cloud filtering and adaptive fireworks for coarse registration

ABSTRACTAiming at the problem of long computation time and poor registration accuracy in the current three-dimensional point cloud registration problem, this paper presents a k-dimensional Tree(KD-tree) improved ICP algorithm(KD-tree_ICP) that combines point cloud filtering and adaptive fireworks algorithms for coarse registration. On the basis of the typical KD-tree improved ICP algorithm, the point cloud filtering process and adaptive firework coarse registration process are added. Firstly, the point cloud data acquired by the 3D laser scanner is filtered. And then the adaptive fireworks algorithm is used to perform coarse registration on the filtered point cloud data. Next, the KD-tree_ICP algorithm is used to perform accurate registration on the basis of coarse registration, and the obtained translation and rotation relations are applied to the original point cloud data to obtain the result after registration. Finally, 3D point clouds of physical models of five statues are used for experimental verification, including error analysis, stability analysis and comparison with other algorithms. The experimental results show that the method proposed in this paper has greatly improved the calculation speed and accuracy, and the algorithm is stable and reliable, which can also be applied to the reconstruction of 3D building models, restoration of cultural relics, precision machining and other fields.

Aerial Image Segmentation with Clustering Using Fireworks Algorithm

Aerial images has different data characteristics when compared to other types of images. An aerial image usually contains small insignificant objects that can cause errors in the unsupervised segmentation method. K-means clustering, one of the widely used unsupervised image segmentation methods, is highly vulnerable to local optima. In this study, Adaptive Fireworks Algorithm (AFWA) is proposed as an alternative to the K-means algorithm in optimizing the clustering process in the cluster-based segmentation method. AFWA is then applied to perform aerial image segmentation and the results are compared with K-means. Based on the comparison using Probabilistic Rand Index (PRI) and Variation of Information (VI) evaluation metrics, AFWA produces an overall better segmentation quality.

Research on Intelligent Minefield Attack Decision Based on Adaptive Fireworks Algorithm

The decision of intelligent minefield attacking tank forces is a complex multi-constraint and multi-objective nonlinear optimization problem. Aiming at the common defects of commonly used intelligent algorithms and combining with the characteristics of fireworks algorithm, this paper proposed an adaptive fireworks algorithm to deal with it. In this paper, we first established the mathematical model of this problem and transformed the model into an unconstrained single-objective extremum by using the external penalty function method. Furthermore, the adaptive fireworks algorithm is used to solve the model. In order to verify the superiority of adaptive fireworks algorithm to deal with this problem, experimental results show that the adaptive fireworks algorithm has faster convergence speed and shorter computation time than the other algorithms, and the results can intuitively describe the reasonable task allocation scheme of the complex situation, which provides a foundation for studying the force control.

Reentry Trajectory Optimization for a Hypersonic Vehicle Based on an Improved Adaptive Fireworks Algorithm

Generation of optimal reentry trajectory for a hypersonic vehicle (HV) satisfying both boundary conditions and path constraints is a challenging task. As a relatively new swarm intelligent algorithm, an adaptive fireworks algorithm (AFWA) has exhibited promising performance on some optimization problems. However, with respect to the optimal reentry trajectory generation under constraints, the AFWA may fall into local optimum, since the individuals including fireworks and sparks are not well informed by the whole swarm. In this paper, we propose an improved AFWA to generate the optimal reentry trajectory under constraints. First, via the Chebyshev polynomial interpolation, the trajectory optimization problem with infinite dimensions is transformed to a nonlinear programming problem (NLP) with finite dimension, and the scope of angle of attack (AOA) is obtained by path constraints to reduce the difficulty of the optimization. To solve the problem, an improved AFWA with a new mutation strategy is developed, where the fireworks can learn from more individuals by the new mutation operator. This strategy significantly enhances the interactions between the fireworks and sparks and thus increases the diversity of population and improves the global search capability. Besides, a constraint-handling technique based on an adaptive penalty function and distance measure is developed to deal with multiple constraints. The numerical simulations of two reentry scenarios for HV demonstrate the validity and effectiveness of the proposed improved AFWA optimization method, when compared with other optimization methods.

Parameter identification of concrete dams using swarm intelligence algorithm

PurposeParameter identification is an important issue in structural health monitoring and damage identification for concrete dams. The purpose of this paper is to introduce a novel adaptive fireworks algorithm (AFWA) into inverse analysis of parameter identification.Design/methodology/approachSwarm intelligence algorithms and finite element analysis are integrated to identify parameters of hydraulic structures. Three swarm intelligence algorithms: AFWA, standard particle swarm optimization (SPSO) and artificial bee colony algorithm (ABC) are adopted to make a comparative study. These algorithms are introduced briefly and then tested by four standard benchmark functions. Inverse analysis methods based on AFWA, SPSO and ABC are adopted to identify Young’s modulus of a concrete gravity dam and a concrete arch dam.FindingsNumerical results show that swarm intelligence algorithms are powerful tools for parameter identification of concrete structures. The proposed AFWA-based inverse analysis algorithm for concrete dams is promising in terms of accuracy and efficiency.Originality/valueFireworks algorithm is applied for inverse analysis of hydraulic structures for the first time, and the problem of parameter selection in AFWA is studied.

A best firework updating information guided adaptive fireworks algorithm

As a new variant of swarm intelligence algorithm, fireworks algorithm (FWA) has significant performance on solving single objective problems, and has been applied broadly on a number of fields. To further improve its performance, a best firework updating information guided adaptive fireworks algorithm (PgAFWA) is proposed, in which the evolving process is guided by the direction from previous best firework to the current best firework from two aspects: amplifying the explosion amplitude on the direction that the best firework is updated, and making more sparks which are generated by the best firework distributed on this direction to further enhance the exploring ability on it. Numerical experiment on CEC2015 test suite was implemented to verify performance of the proposed algorithm. The experiment results indicated that the PgAFWA outperformed the compared algorithms in terms of both convergence speed and solving quality.

Opposition-Based Adaptive Fireworks Algorithm

A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost.