Multidimensional Particle Swarm Optimization Research Articles

Multi-UAVs task allocation method based on MPSO-SA-DQN

Multi-UAVs play an important role in the battlefield. Although many methods are proposed to solve the Multi-UAV task allocation, there still existing the problems of complex time constraints and uncertain solution space. The reason is that multi-UAVs usually face changing environmental factors. Aiming at solving such problem, this paper proposes a multi-UAV task assignment method based on Deep Q-based evolutionary reinforcement learning algorithms (MPSO-SA-DQN). Specifically, this method builds a multi-agent training framework based on the deep evolutionary reinforcement learning mechanism and SA-DQN. Its aim is to improve the global exploration and optimization capabilities of multi-agents. At the same time, the multi-dimensional particle swarm optimization algorithm is introduced to optimize the state space. Based on task priority mapping, the MPSO-SA-DQN algorithm framework is proposed. As a result, multi-agents can optimize the execution state in real time in the environment interaction. Besides, it also has the ability to reach optimal state and maximum reward. According to the characteristics of multi-UAV global task assignment, this paper designs a priority state space autoencoder strategy and global task feature. A multi-UAVs tasks allocation and iterative optimization method based on MPSO-SA-DQN algorithm is proposed, so as to continuously optimize the task allocation scheme. The simulation results show that the multi-UAV task allocation method based on MPSO-SA-DQN can effectively solve the problem of uncertainty in the optimal solution space of task allocation. At the same time, the algorithm achieves faster convergence result, and a good prospect of promotion in the field of UAV swarm cooperative task planning.

Computation Offloading Based on a Distributed Overlay Network Cache-Sharing Mechanism in Multi-Access Edge Computing

Multi-access edge computing (MEC) enhances service quality for users and reduces computational overhead by migrating workloads and application data to the network edge. However, current solutions for task offloading and cache replacement in edge scenarios are constrained by factors such as communication bandwidth, wireless network coverage, and limited storage capacity of edge devices, making it challenging to achieve high cache reuse and lower system energy consumption. To address these issues, a framework leveraging cooperative edge servers deployed in wireless access networks across different geographical regions is designed. Specifically, we propose the Distributed Edge Service Caching and Offloading (DESCO) network architecture and design a decentralized resource-sharing algorithm based on consistent hashing, named Cache Chord. Subsequently, based on DESCO and aiming to minimize overall user energy consumption while maintaining user latency constraints, we introduce the real-time computation offloading (RCO) problem and transform RCO into a multi-player static game, prove the existence of Nash equilibrium solutions, and solve it using a multi-dimensional particle swarm optimization algorithm. Finally, simulation results demonstrate that the proposed solution reduces the average energy consumption by over 27% in the DESCO network compared to existing algorithms.

The reduction of noise and vibration of composite panels in aircraft through multi-dimensional particle swarm optimization

The reduction of noise and vibrations on aircraft poses a unique challenge. This paper explores the use of multi-dimensional Particle Swarm Optimization (PSO) to reduce vibrations and transmitted noise in composite fuselage panels excited by turbulent boundary layer flow. Turbulent boundary layer Power Spectral Density (PSD) data is used to simulate the excitation of simply supported aircraft panels. A comparison is made between isotropic aluminum 2024-T3 panels and optimized composite panels of varying composition for the assessment of success in noise transmission reduction as well as structural function. The algorithm for PSO is implemented in Python and iterates through composites with varying thickness, ply makeup, ply orientation, and materials. Python allows for complex algorithms to be easily interfaced with Finite Element Analysis (FEA) programs. In the current study, ANSYS is used to determine the spectral response of the panels subject to turbulent flow using the superposition of the modes of vibration. During the optimization process, optimization parameters are updated in each iteration based on the success of reducing the spectral response of the panel without compromising its structural integrity or increasing its weight beyond a reasonable threshold.

Multi-objective optimization strategy for parallel tuning of multiple fractional order controllers in a PMSG based WECS

In this paper, a novel multi-objective optimization strategy is proposed for the parallel tuning of six fractional order controllers used in regulation loops of a PMSG based wind energy conversion system connected to the electric grid. The nonlinear nature of the WECS components has made controllers design challenging. To enhance the transient response of system variables, Fractional Order Proportional Integral (FOPI) controllers are considered as they are more suitable for such nonlinear physical systems. The additional parameters introduced by FOPI are normally tuned using a single objective, multi-dimensional particle swarm optimization algorithm. However, the inter-dependence of regulation loops introduces additional complexity that is solved in this work using a multi-objective optimization strategy based on a succession of Single-Objective PSO and Multi-Objective PSO. A simulation study has been conducted in order to demonstrate the higher performance and superior tracking accuracy of the proposed multi-objective optimization strategy in variable wind speed operating conditions.

Robust LQR control based on multidimensional particle swarm optimization for enhancing the dynamic performance of poultry house system

Robust LQR control based on multidimensional particle swarm optimization for enhancing the dynamic performance of poultry house system

Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control

<span>When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy uses a two hidden layer deep feedforward network (DFN), where the one-step secant algorithm is chosen for initializing the DFN parameters. All the design steps of the proposed adaptive controller are described. The multidimensional particle swarm optimization (PSO) algorithm is used for tuning the DFN parameters. Then, using two simulation efficiency tests, a comparison between the proposed PSO-based APID-DFN, the (non-optimized) APID-DFN, the feedforward network APID, and the fixed-parameter PID controllers proves much efficiency of the proposed adaptive controller. The results illustrate that the proposed PSO-based APID-DFN controller can ensure good quadrotor system stabilization and achieve minimum overshoot and faster convergence speed for all quadrotor motions. Thus, the proposed control strategy could be considered an additional intelligent method-based adaptive control for unmanned aerial vehicles.</span>

Multi-GNSS inter-system model for complex environments based on optimal state estimation

When calibrating inter-system biases (ISB), especially the fractional part of inter-system phase biases (F-ISPB), a multi-GNSS inter-system model can effectively improve positioning performance under a complex environment. Usually, the F-ISPB is estimated after fixing the intra-system ambiguities. However, this approach seems inapplicable when it is difficult to obtain intra-system ambiguities under a complex environment. A multi-dimensional particle filter (PF)-based F-ISPB estimate method has been proposed to overcome the problem. Nevertheless, the multi-dimensional PF involves a great quantity of computations. In this contribution, four state optimal estimate-based F-ISPB handling schemes are proposed: step-by-step PF, step-by-step particle swarm optimization (PSO), multi-dimensional PF, and multi-dimensional PSO-based F-ISPB estimate methods. Two baselines were selected to investigate the F-ISPB estimate performance in both open and complex environments. The results show that due to the potential of the wrong F-ISPB to bring about the maximum ratio for a long time during the initial stage, the step-by-step PF method can achieve better performance than step-by-step PSO. Besides, the two-dimensional results show that all of the F-ISPB still cannot be extracted under complex environments by multi-dimensional PSO. Furthermore, compared with step-by-step PF, the multi-dimensional PF method costs too much to obtain the right value. For example, in the two-dimensional case, the step-by-step PF searches 200 times for each epoch, while the two-dimensional PF requires 40 000 times for each epoch, so it is difficult for receivers to provide hardware support for this method. In addition, the step-by-step PF can obtain the right F-ISPB with about 100 epochs no matter what scenario. Thus, under challenging observation scenarios, a step-by-step PF method is recommended to extract the F-ISPB.

Multi-dimensional particle swarm optimization for robust blind image watermarking using intertwining logistic map and hybrid domain

Robust blind watermarking has become a vital means of copyright protection, and this paper presents a new optimal robust and blind watermarking method of grayscale images based on intertwining logistic map and a variant of particle swarm optimization (PSO) in a hybrid domain. In the proposed approach, firstly a host image is decomposed by discrete wavelet transform, and discrete cosine transform (DCT) is applied to insensitive LH and HL subbands according to human visual model. Then, optimum frequency spectra in the DCT domain are chosen to form a feature matrix for improving the robustness and transparency of watermark. Finally, a shuffled watermark image using the chaotic logistic map is inserted by modifying the largest singular values of a feature matrix pair in the singular value decomposition domain. An improved version of PSO is employed to perform multi-dimensional optimization for selection of the most qualified DCT coefficients and estimation of watermark embedding strength in terms of their significant influence on imperceptibility and robustness. The security of the proposed method is provided by intertwining logistic map. Experimental results on standard test images indicate that PSO searches efficiently optimal values of watermark embedding strength and the most suitable DCT subbands, and the proposed watermarking algorithm performs much better than the other compared schemes in imperceptibility and robustness objectives.

Estimation of planes of a rock mass in a gallery wall from point cloud data based on MD PSO

LiDAR (laser imaging detection and ranging) has been developed to measure the distance of the mesh of points on an object with a high level of accuracy. It provides high-resolution point cloud data as a result of distance measurement. Detailed 3D shapes of objects can be estimated from point cloud data. LiDAR has been used to identify discontinuities in a rock mass of a tunnel gallery wall. To identify discontinuities, it is necessary to approximate the rock mass surface with small planes. Normal vectors of the planes are important to identify discontinuities. We developed an algorithm for estimation of planes based on multi-dimensional particle swarm optimization (MD PSO) from point cloud data. Point cloud data were segmented into bounding boxes and grouped into clusters by MD PSO. Planes were estimated using the least squares method for point cloud data in the respective clusters. The newly developed algorithm based on MD PSO was evaluated using point cloud data obtained from a gallery wall. Evaluation was carried out in comparison with the previous developed variable-box segmentation (VBS) algorithm. The MD PSO-based algorithm showed a 7% higher accuracy than that of the VBS algorithm.

Simultaneous Identification of Model Structure and the Associated Parameters for Linear Systems Based on Particle Swarm Optimization

In this study, a novel easy‐to‐use meta‐heuristic method for simultaneous identification of model structure and the associated parameters for linear systems is developed. This is achieved via a constrained multidimensional particle swarm optimization (PSO) mechanism developed by hybridizing two main methodologies: one for negating the limit for fixing the particle’s dimensions within the PSO process and another for enhancing the exploration ability of the particles by adopting a cyclic neighborhood topology of the swarm. This optimizer consecutively searches the dimensional optimum of particles and then the positional optimum in the search space, whose dimension is specified by the explored optimal dimension. The dimensional optimum provides the optimal model structure, while the positional optimum provides the optimal model parameters. Typical numerical examples are considered for evaluation purposes, which clearly demonstrate that the proposed PSO scheme provides novel and powerful impetus with remarkable reliability toward simultaneous identification of model structure and unknown model parameters. Furthermore, identification experiments are conducted on a magnetic levitation system and a robotic manipulator with joint flexibility to demonstrate the effectiveness of the proposed strategy in practical applications.

Multi-agent architecture for Multi‐objective optimization of Flexible Neural Tree

In this paper, a multi-agent system is introduced to parallelize the Flexible Beta Basis Function Neural Network (FBBFNT)’ training as a response to the time cost challenge. Different agents are formed; a Structure Agent is designed for the FBBFNT structure optimization and a variable set of Parameter Agents is used for the FBBFNT parameter optimization. The main objectives of the FBBFNT learning process were the accuracy and the structure complexity. With the proposed multi-agent system, the main purpose is to reach a good balance between these objectives. For that, a multi-objective context was adopted which based on Pareto dominance. The agents use two algorithms: the Pareto dominance Extended Genetic Programming (PEGP) and the Pareto Multi-Dimensional Particle Swarm Optimization (PMD_PSO) algorithms for the structure and parameter optimization, respectively. The proposed system is called Pareto Multi-Agent Flexible Neural Tree (PMA_FNT).To assess the effectiveness of PMA_FNT, four benchmark real datasets of classification are tested. The results compared with some classifiers published in the literature.

Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering.

In training radial basis function neural networks (RBFNNs), the locations of Gaussian neurons are commonly determined by clustering. Training inputs can be clustered on a fully unsupervised manner (input clustering), or some supervision can be introduced, for example, by concatenating the input vectors with weighted output vectors (input-output clustering). In this paper, we propose to apply clustering separately for each class (class-specific clustering). The idea has been used in some previous works, but without evaluating the benefits of the approach. We compare the class-specific, input, and input-output clustering approaches in terms of classification performance and computational efficiency when training RBFNNs. To accomplish this objective, we apply three different clustering algorithms and conduct experiments on 25 benchmark data sets. We show that the class-specific approach significantly reduces the overall complexity of the clustering, and our experimental results demonstrate that it can also lead to a significant gain in the classification performance, especially for the networks with a relatively few Gaussian neurons. Among other applied clustering algorithms, we combine, for the first time, a dynamic evolutionary optimization method, multidimensional particle swarm optimization, and the class-specific clustering to optimize the number of cluster centroids and their locations.

Automated patient-specific classification of long-term Electroencephalography

This paper presents a novel systematic approach for patient-specific classification of long-term Electroencephalography (EEG). The goal is to extract the seizure sections with a high accuracy to ease the Neurologist’s burden of inspecting such long-term EEG data. We aim to achieve this using the minimum feedback from the Neurologist. To accomplish this, we use the majority of the state-of-the-art features proposed in this domain for evolving a collective network of binary classifiers (CNBC) using multi-dimensional particle swarm optimization (MD PSO). Multiple CNBCs are then used to form a CNBC ensemble (CNBC-E), which aggregates epileptic seizure frames from the classification map of each CNBC in order to maximize the sensitivity rate. Finally, a morphological filter forms the final epileptic segments while filtering out the outliers in the form of classification noise. The proposed system is fully generic, which does not require any a priori information about the patient such as the list of relevant EEG channels. The results of the classification experiments, which are performed over the benchmark CHB-MIT scalp long-term EEG database show that the proposed system can achieve all the aforementioned objectives and exhibits a significantly superior performance compared to several other state-of-the-art methods. Using a limited training dataset that is formed by less than 2min of seizure and 24min of non-seizure data on the average taken from the early 25% section of the EEG record of each patient, the proposed system establishes an average sensitivity rate above 89% along with an average specificity rate above 93% over the test set.

Automated exploration of datapath and unrolling factor during power–performance tradeoff in architectural synthesis using multi-dimensional PSO algorithm

A novel algorithm for automated simultaneous exploration of datapath and Unrolling Factor (UF) during power–performance tradeoff in High Level Synthesis (HLS) using multi-dimensional particle swarm optimization (PSO) (termed as ‘M-PSO’) for control and data flow graphs (CDFGs) is presented. The major contributions of the proposed algorithm are as follows: (a) simultaneous exploration of datapath and loop UF through an integrated multi-dimensional particle encoding process using swarm intelligence; (b) an estimation model for computation of execution delay of a loop unrolled CDFG (based on a resource configuration visited) without requiring to tediously unroll the entire CDFG for the specified loop value in most cases; (c) balancing the tradeoff between power–performance metrics as well as control states and execution delay during loop unrolling; (d) sensitivity analysis of PSO parameter such as swarm size on the impact of exploration time and Quality of Results (QoR) of the proposed design space exploration (DSE) process. This analysis presented would assist the designer in pre-tuning the PSO parameters to an optimum value for achieving efficient exploration results within a quick runtime; (e) analysis of design metrics such as power, execution time and number of control steps of the global best particle found in every iteration with respect to increase/decrease in unrolling factor.The proposed approach when tested on a variety of data flow graphs (DFGs) and CDFGs indicated an average improvement in QoR of >28% and reduction in runtime of >94% compared to recent works.

An evolutionary feature synthesis approach for content-based audio retrieval

A vast amount of audio features have been proposed in the literature to characterize the content of audio signals. In order to overcome specific problems related to the existing features (such as lack of discriminative power), as well as to reduce the need for manual feature selection, in this article, we propose an evolutionary feature synthesis technique with a built-in feature selection scheme. The proposed synthesis process searches for optimal linear/nonlinear operators and feature weights from a pre-defined multi-dimensional search space to generate a highly discriminative set of new (artificial) features. The evolutionary search process is based on a stochastic optimization approach in which a multi-dimensional particle swarm optimization algorithm, along with fractional global best formation and heterogeneous particle behavior techniques, is applied. Unlike many existing feature generation approaches, the dimensionality of the synthesized feature vector is also searched and optimized within a set range in order to better meet the varying requirements set by many practical applications and classifiers. The new features generated by the proposed synthesis approach are compared with typical low-level audio features in several classification and retrieval tasks. The results demonstrate a clear improvement of up to 15–20% in average retrieval performance. Moreover, the proposed synthesis technique surpasses the synthesis performance of evolutionary artificial neural networks, exhibiting a considerable capability to accurately distinguish among different audio classes.

Multidimensional particle swarm optimization-based unsupervised planar segmentation algorithm of unorganized point clouds

This paper presents an unsupervised planar segmentation algorithm of unorganized point clouds based on multidimensional (MD) particle swarm optimization (PSO). A robust objective function of the unsupervised planar segmentation is established according to clustering distances of PSO clustering algorithm and inliers of random sample consensus (RANSAC) method. After that, MD PSO algorithm is adopted to optimize the objective function, where the optimal number and positions of the segmented planar patches are sought simultaneously. In order not to get trapped in local optima, a modification strategy of the global best (GB) position of swarm in each dimension is added to the MD PSO algorithm. Thus the unsupervised planar segmentation of point clouds is realized. Experimental results demonstrate the high planar segmentation accuracy of the proposed algorithm.

Evolutionary RBF classifier for polarimetric SAR images

In this paper, a robust radial basis function (RBF) network based classifier is proposed for polarimetric synthetic aperture radar (SAR) images. The proposed feature extraction process utilizes the covariance matrix elements, the H/α/A decomposition based features combined with the backscattering power (span), and the gray level co-occurrence matrix (GLCM) based texture features, which are projected onto a lower dimensional feature space using principal components analysis. For the classifier training, both conventional backpropagation (BP) and multidimensional particle swarm optimization (MD-PSO) based dynamic clustering are explored. By combining complete polarimetric covariance matrix and eigenvalue decomposition based pixel values with textural information (contrast, correlation, energy, and homogeneity) in the feature set, and employing automated evolutionary RBF classifier for the pattern recognition unit, the overall classification performance is shown to be significantly improved. An experimental study is performed using the fully polarimetric San Francisco Bay and Flevoland data sets acquired by the NASA/Jet Propulsion Laboratory Airborne SAR (AIRSAR) at L-band to evaluate the performance of the proposed classifier. Classification results (in terms of confusion matrix, overall accuracy and classification map) compared with the major state of the art algorithms demonstrate the effectiveness of the proposed RBF network classifier.

Multi-dimensional particle swarm optimization in dynamic environments

Particle swarm optimization (PSO) was proposed as an optimization technique for static environments; however, many real problems are dynamic, meaning that the environment and the characteristics of the global optimum can change in time. In this paper, we adapt recent techniques, which successfully address several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the pre-mature convergence problem and improve the rate of PSO’s convergence to the global optimum, Fractional Global Best Formation (FGBF) technique is used. FGBF basically collects all the best dimensional components and fractionally creates an artificial Global Best particle ( aGB) that has the potential to be a better “guide” than the PSO’s native gbest particle. To establish follow-up of local optima, we then introduce a novel multi-swarm algorithm, which enables each swarm to converge to a different optimum and use FGBF technique distinctively. Finally for the multi-dimensional dynamic environments where the optimum dimension also changes in time, we utilize a recent PSO technique, the multi-dimensional (MD) PSO, which re-forms the native structure of the swarm particles in such a way that they can make inter-dimensional passes with a dedicated dimensional PSO process. Therefore, in a multi-dimensional search space where the optimum dimension is unknown, swarm particles can seek for both positional and dimensional optima. This eventually pushes the frontier of the optimization problems in dynamic environments towards a global search in a multi-dimensional space, where there exists a multi-modal problem possibly in each dimension. We investigated both standalone and mutual applications of the proposed methods over the moving peaks benchmark (MPB), which originally simulates a dynamic environment in a unique (fixed) dimension. MPB is appropriately extended to accomplish the simulation of a multi-dimensional dynamic system, which contains dynamic environments active in several dimensions. An extensive set of experiments show that in traditional MPB application domain, FGBF technique applied with multi-swarms exhibits an impressive speed gain and tracks the global peak with the minimum error so far achieved with respect to the other competitive PSO-based methods. When applied over the extended MPB, MD PSO with FGBF can find optimum dimension and provide the (near-) optimal solution in this dimension.

Stochastic approximation driven particle swarm optimization with simultaneous perturbation – Who will guide the guide?

The need for solving multi-modal optimization problems in high dimensions is pervasive in many practical applications. Particle swarm optimization (PSO) is attracting an ever-growing attention and more than ever it has found many application areas for many challenging optimization problems. It is, however, a known fact that PSO has a severe drawback in the update of its global best ( gbest) particle, which has a crucial role of guiding the rest of the swarm. In this paper, we propose two efficient solutions to remedy this problem using a stochastic approximation (SA) technique. In the first approach, gbest is updated (moved) with respect to a global estimation of the gradient of the underlying (error) surface or function and hence can avoid getting trapped into a local optimum. The second approach is based on the formation of an alternative or artificial global best particle, the so-called aGB, which can replace the native gbest particle for a better guidance, the decision of which is held by a fair competition between the two. For this purpose we use simultaneous perturbation stochastic approximation (SPSA) for its low cost. Since SPSA is applied only to the gbest (not to the entire swarm), both approaches result thus in a negligible overhead cost for the entire PSO process. Both approaches are shown to significantly improve the performance of PSO over a wide range of non-linear functions, especially if SPSA parameters are well selected to fit the problem at hand. A major finding of the paper is that even if the SPSA parameters are not tuned well, results of SA-driven (SAD) PSO are still better than the best of PSO and SPSA. Since the problem of poor gbest update persists in the recently proposed extension of PSO, called multi-dimensional PSO (MD-PSO), both approaches are also integrated into MD-PSO and tested over a set of unsupervised data clustering applications. As in the basic PSO application, experimental results show that the proposed approaches significantly improved the quality of the MD-PSO clustering as measured by a validity index function. Furthermore, the proposed approaches are generic as they can be used with other PSO variants and applicable to a wide range of problems.

Multidimensional Fitness Function DPSO Algorithm for Analog Test Point Selection

A novel multidimensional fitness function discrete particle swarm optimization algorithm is proposed to optimize analog test point selection. The proposed method uses fault isolation rate and the number of test points to formulate a multidimensional fitness function to search the global minimal test point set, and an elitist set is used to get more than one possible best solution in the described approach. The efficiency of the proposed method is proven by the same experiments used to verify other methods for optimal test points. Results show that the proposed algorithm in this paper cannot only reduce the computation complexity but also shorten the time consumption. It is particularly useful for large-scale analog circuits.