Effective Optimization Algorithm Research Articles

Differential Privacy-Preserving IoT Data Sharing Through Enhanced PSO

ABSTRACT With the proliferation of Internet of Things (IoT) devices and the generation of massive amounts of sensitive data, preserving privacy while enabling data sharing has become a critical concern. In this research, we propose a novel approach for achieving differential privacy in IoT data sharing through an improved Particle Swarm Optimization (PSO) algorithm. Our objective is to find the optimal configuration of privacy-preserving mechanisms that maximizes privacy while maintaining data utility. The research begins with a comprehensive overview of differential privacy in IoT data sharing, highlighting the limitations of existing optimization algorithms. We then present our proposed improvements to the traditional PSO algorithm, including the design of a suitable fitness function, the use of a dynamic inertia weight, exploration of different neighborhood topologies, and adaptive acceleration coefficients. We define a set of performance metrics, including privacy metrics (e.g. ε-differential privacy parameter) and utility measures (e.g. accuracy, utility loss), to assess the algorithm’s effectiveness. The results of our experiments demonstrate that the improved PSO algorithm achieves higher privacy guarantees while maintaining competitive levels of data utility compared to existing approaches. The proposed algorithm exhibits faster convergence, better exploration of the search space, and improved scalability. Our research contributes to the field of privacy-preserving IoT data sharing by providing an efficient and effective optimization algorithm that enables secure and privacy-aware data sharing while facilitating valuable insights and analysis.

A novel single-parameter continuously differentiable filled function for global optimization

ABSTRACT The filled function approach is an effective global optimization method. The properties of the filled function directly affect the efficiency of the algorithm and have been discussed by many scholars, resulting in various new forms of filled functions. However, most of existing filled functions are not continuously differentiable and have parameters that are difficult to adjust. To remedy this problem, a new continuously differentiable filled function with an easily adjustable parameter is constructed. According to the theoretical analysis of the filled function, a new easy-to-implement filled function algorithm is designed utilizing the efficient local search algorithm based on gradient information. Numerical experiments show that the proposed filled function algorithm is a feasible and effective global optimization algorithm with less computational cost.

Fast unsupervised multi-modal hashing based on piecewise learning

Unsupervised hashing has been extensively applied in large-scale multi-modal retrieval by mapping original data from heterogeneous modalities into unified binary codes. However, there still remain challenges especially how to balance the individual modality-specific representations and common representation preserving intrinsic linkages among heterogeneous modalities. In this paper, we propose a novel fast Unsupervised Multi-modal Hashing based on Piecewise Learning, denoted as UMHPL, to deal with the mentioned issue. Initially, we formulate the problem as matrix factorization to derive the individual modality-specific latent representations and common latent representation with consensus matrices in a brief time. To maintain the integrality of multi-modal data, we integrate them by adaptive weight factors and nuclear norm minimization. Subsequently, we establish a connection between the individual modality-specific latent representations and common latent representation based on the piecewise hash learning framework to reinforce the discriminative competency of model, which leads the hash codes more compact. Finally, an effective discrete optimization algorithm in mathematical logic and functional analysis is proposed. Comprehensive experiments on Wiki, MIRFlirck, NUS-WIDE, and MSCOCO datasets demonstrate the superior performance of UMHPL to state-of-the-art hashing methods.

Robust Tensor Learning for Multi-View Spectral Clustering

Tensor-based multi-view spectral clustering methods are promising in practical clustering applications. However, most of the existing methods adopt the ℓ2,1 norm to depict the sparsity of the error matrix, and they usually ignore the global structure embedded in each single view, compromising the clustering performance. Here, we design a robust tensor learning method for multi-view spectral clustering (RTL-MSC), which employs the weighted tensor nuclear norm to regularize the essential tensor for exploiting the high-order correlations underlying multiple views and adopts the nuclear norm to constrain each frontal slice of the essential tensor as the block diagonal matrix. Simultaneously, a novel column-wise sparse norm, namely, ℓ2,p, is defined in RTL-MSC to measure the error tensor, making it sparser than the one derived by the ℓ2,1 norm. We design an effective optimization algorithm to solve the proposed model. Experiments on three widely used datasets demonstrate the superiority of our method.

Hydraulic-based optimization algorithm for the design of stormwater drainage networks

Stormwater drainage networks are designed to reduce the risk of rainwater damage to the served area. The purpose of optimizing a stormwater drainage system is to reduce overall construction costs and to meet hydraulic design requirements. Currently, designs that rely on software or manual calculations are limited by the available time and the designer’s capabilities. In fact, manual optimization for large networks consumes a lot of time and effort, and there is no guarantee that the optimal design is reached, also it is subject to human errors. In recent years, several researchers have focused on creating optimization design algorithms specifically for sewer and storm networks, such as genetic algorithm (GA), linear programming (LP), heuristic programming (HP),…etc. However, these studies were limited to covering one or two design parameters and constraints. Additionally, in some studies, the hydraulic performance of the designed network was not treated in a proper way, especially the water surface profile effects. So, the main objective of the study is to develop an effective hydraulic-based optimization algorithm (HBOA) that can dynamically get the optimal design with minimum total cost for a given storm network layout and meet all hydraulic requirements. To achieve this, a MATLAB code is created and coupled with SewerGEMS software that automatically simulates all expected optimization scenarios based on network hydraulic performance. The HBOA is validated economically and hydraulically using two benchmark examples from the literature. According to the economic validation, the total network cost generated by HBOA was the lowest when compared to the optimization methods found in the literature. During the hydraulic evaluation, it was observed that the optimization algorithm (GA-HP) used in the literature for the benchmark examples does not meet the hydraulic requirements where the networks are flooded, whereas HBOA meets the hydraulic requirements with minimal overall network cost. Also, the HBOA is applied to four real stormwater drainage networks that were already designed, constructed, and optimized manually. The four redesigned real cases using HBOA revealed a cost reduction of about 15% compared to the original designs, while consuming a few hours for the design and optimization processes. Finally, the developed HBOA is a robust, time-efficient, and cost-effective optimization and hydraulic design tool which could be used in the design of stormwater drainage networks with different design constraints with minimal human interference.

Infection-induced cascading failures – impact and mitigation

In the context of epidemic spreading, many intricate dynamical patterns can emerge due to the cooperation of different types of pathogens or the interaction between the disease spread and other failure propagation mechanism. To unravel such patterns, simulation frameworks are usually adopted, but they are computationally demanding on big networks and subject to large statistical uncertainty. Here, we study the two-layer spreading processes on unidirectionally dependent networks, where the spreading infection of diseases or malware in one layer can trigger cascading failures in another layer and lead to secondary disasters, e.g., disrupting public services, supply chains, or power distribution. We utilize a dynamic message-passing method to devise efficient algorithms for inferring the system states, which allows one to investigate systematically the nature of complex intertwined spreading processes and evaluate their impact. Based on such dynamic message-passing framework and optimal control, we further develop an effective optimization algorithm for mitigating network failures.

Dual-level feature assessment for unsupervised multi-view feature selection with latent space learning

Recently, numerous unsupervised multi-view feature selection methods have been presented. However, these methods assess the significance of data features in each view individually or jointly evaluate the significance of data features across multiple views by concatenating data features from all data views. So, they disregard either inter-view or intra-view feature connections of data. Moreover, they ignore the complementary information from multiple insufficient views, each of which only captures partial information of multi-view data and is insufficient to characterize data distribution. To address these problems, this paper proposes a model named Dual-level Feature Assessment for Unsupervised Multi-view Feature Selection with Latent Space Learning (DFA-LSL). It first investigates the underlying complementary information from multiple views in a latent space while simultaneously learning latent representations for data. Compared with the original data representations, the latent representations contain the entire information of multi-view data. Then, it explores connections and distinguishing ability of features at two levels, namely the inter-view and intra-view levels, by jointly assessing the significance of features in the latent space and individually assessing the significance of features in each view concurrently. Following that, an effective optimization algorithm is presented. Extensive experiments demonstrate that the proposed work surpasses several state-of-the-art models.

A novel hippo swarm optimization: for solving high-dimensional problems and engineering design problems

Abstract In recent years, scholars have developed and enhanced optimization algorithms to tackle high-dimensional optimization and engineering challenges. The primary challenge of high-dimensional optimization lies in striking a balance between exploring a wide search space and focusing on specific regions. Meanwhile, engineering design problems are intricate and come with various constraints. This research introduces a novel approach called Hippo Swarm Optimization (HSO), inspired by the behavior of hippos, designed to address high-dimensional optimization problems and real-world engineering challenges. HSO encompasses four distinct search strategies based on the behavior of hippos in different scenarios: starvation search, alpha search, margination, and competition. To assess the effectiveness of HSO, we conducted experiments using the CEC2017 test set, featuring the highest dimensional problems, CEC2022 and four constrained engineering problems. In parallel, we employed 14 established optimization algorithms as a control group. The experimental outcomes reveal that HSO outperforms the 14 well-known optimization algorithms, achieving first average ranking out of them in CEC2017 and CEC2022. Across the four classical engineering design problems, HSO consistently delivers the best results. These results substantiate HSO as a highly effective optimization algorithm for both high-dimensional optimization and engineering challenges.

Optimized Intrusion Detection System in Fog Computing Environment Using Automatic Termination-based Whale Optimization with ELM

In fog computing, computing resources are deployed at the network edge, which can include routers, switches, gateways, and even end-user devices. Fog computing focuses on running computations and storing data directly on or near the fog devices themselves. The data processing occurs locally on the device, reducing the reliance on network connectivity and allowing for faster response times. However, the conventional intrusion detection system (IDS) failed to provide security during the data transfer between fog nodes to cloud, fog data centres. So, this work implemented the optimized IDS in fog computing environment (OIDS-FCE) using advanced naturally inspired optimization algorithms with extreme learning. Initially, the data preprocessing operation maintains the uniform characteristics in the dataset by normalizing the columns. Then, comprehensive learning particle swarm based effective seeker optimization (CLPS-ESO) algorithm extracts the intrusion specific features by analyzing the internal patterns of all rows, columns. In addition, automatic termination-based whale optimization algorithm (ATWOA) selects the best intrusion features from CLPS-ESO resultant features using correlation analysis. Finally, the hybrid extreme learning machine (HELM) classifies the varies instruction types from ATWOA optimal features. The simulation results show that the proposed OIDS-FCE achieved 98.52% accuracy, 96.38% precision, 95.50% of recall, and 95.90% of F1-score using UNSW-NB dataset, which are higher than other artificial intelligence IDS models.

A novel population robustness-based switching response framework for solving dynamic multi-objective problems

In this paper, a novel population robustness-based switching response framework (PR-SRF) is proposed to develop effective dynamic multi-objective optimization algorithm (DMOA), which integrates different response strategies to comprehensively cope with the dynamic behaviors. In particular, the population robustness is described by the quantification of how severely the environmental changes affect current population, which is timely graded as three levels of weak, strong, and normal to enable the adaptive switch of three different responses of diversity-enhancement, diversity-maintenance, and the knowledge-transfer, respectively. In this way, associations between the adopted responses and the changing environments are successfully established, thereby facilitating more intelligent decision when handling the dynamic behaviors. According to the benchmark evaluation results, the proposed PR-SRF-DMOA yields better comprehensive performance than several other DMOAs with popular response strategies, and it also outperforms another three DMOAs with hybrid responses, which demonstrates the great competitiveness of our algorithm. In addition, ablation study proves that the proposed PR-SRF can sufficiently exploit the merits of different responses, which effectively alleviates the negative knowledge transfer in extremely fluctuating environments, thereby providing valuable references for the development of evolutionary transfer optimization (ETO) algorithms.

Superposed Atomic Representation for Robust High-Dimensional Data Recovery of Multiple Low-Dimensional Structures

This paper proposes a unified Superposed Atomic Representation (SAR) framework for high-dimensional data recovery with multiple low-dimensional structures. The data can be in various forms ranging from vectors to tensors. The goal of SAR is to recover different components from their sum, where each component has a low-dimensional structure, such as sparsity, low-rankness or be lying a low-dimensional subspace. Examples of SAR include, but not limited to, Robust Sparse Representation (RSR), Robust Principal Component Analysis (RPCA), Tensor RPCA (TRPCA), and Outlier Pursuit (OP). We establish the theoretical guarantee for SAR. To further improve SAR, we also develop a Weighted SAR (WSAR) framework by paying more attention and penalizing less on significant atoms of each component. An effective optimization algorithm is devised for WSAR and the convergence of the algorithm is rigorously proved. By leveraging WSAR as a general platform, several new methods are proposed for high-dimensional data recovery. The experiments on real data demonstrate the superiority of WSAR for various data recovery problems.

Optimizing the resource allocation in cyber physical energy systems based on cloud storage and IoT infrastructure

Given the prohibited operating zones, losses, and valve point effects in power systems, energy optimization analysis in such systems includes numerous non-convex and non-smooth parameters, such as economic dispatch problems. In addition, in this paper, to include all possible scenarios in economic dispatch problems, multi-fuel generators, and transmission losses are considered. However, these features make economic dispatch problems more complex from a non-convexity standpoint. In order to solve economic dispatch problems as an important consideration in power systems, this paper presents a modified robust, and effective optimization algorithm. Here, some modifications are carried out to tackle such a sophisticated problem and find the best solution, considering multiple fuels, valve point effect, large-scale systems, prohibited operating zones, and transmission losses. Moreover, a few complicated power systems including 6, 13, and 40 generators which are fed by one type of fuel, 10 generators with multiple fuels, and two large-scale cases comprised of 80 and 120 generators are analyzed by the proposed optimization algorithm. The effectiveness of the proposed method, in terms of accuracy, robustness, and convergence speed is evaluated, as well. Furthermore, this paper explores the integration of cloud storage and internet of things (IoT) to augment the adaptability of monitoring capabilities of the proposed method in handling non-convex energy resource management and allocation problems across various generator quantities and constraints. The results show the capability of the proposed algorithm for solving non-convex energy resource management and allocation problems irrespective of the number of generators and constraints. Based on the obtained results, the proposed method provides good results for both small and large systems. The proposed method, for example, always yields the best results for the system of 6 power plants with and without losses, which are $15,276.894 and $15,443.7967. Moreover, the improvements made in the proposed method have allowed the economic dispatch problem regarding multi-fuel power plants to be solved not only with optimal results ($623.83) but also in less than 35 iterations. Lastly, the difference between the best-obtained results ($121,412) and the worst-obtained results ($121,316.1992) for the system of 40 power plants is only about $4 which is quite acceptable.

Robust multi-view learning via M-estimator joint sparse representation

Recently, multi-view learning has achieved extraordinary success in many research areas such as pattern recognition and data mining. Most existing multi-view methods mainly focus on exploring the correlation information between different views and their performance may severely degrade in the presence of heavy noises and outliers. In this paper, we put forward a robust multi-view joint sparse representation (RMJSR) method for multi-view learning. Firstly, we design a novel multi-view Cauchy estimator based loss function originating from robust statistics to address complex noises and outliers in reality. Based on this, we leverage the ℓ1,q norm to enhance our model by encouraging the learned representation of multiple views to share the same sparsity pattern. Secondly, to explore the optimal solution for the RMJSR model, we devise an effective optimization algorithm based on the half-quadratic (HQ) theory and the alternating direction method of multipliers (ADMM) framework. Thirdly, we provide the theoretical guarantee for revealing the theoretical condition for the success of the proposed method. Further, we have also provided extensive analysis of the proposed method, including the optimality condition, convergence analysis, and complexity analysis. Extensive experimental results validate the effectiveness and robustness of the proposed method in comparison with state-of-the-art competitors. The source code is available at https://github.com/Huyutao7/RMJSRC.

New sparse regularization approach for extracting transient impulses from fault vibration signal of rotating machinery

Fault impulses induced by localized failure is critical for fault diagnosis and degradation prognostics of rotating machinery, however, weak transient fault impulses are always submerged in heavy noise and unrelated components. To overcome this bottleneck issue, a novel approach named smoothing sparse low-rank matrix (SSLRM) associated with asymmetric and singular value decomposition (SVD) penalty regularizers is proposed, for the first time. To be specific, the asymmetric and SVD penalty regularizers are utilized to alleviate the issues of underestimation deficiency of traditional sparse regularization, where the Parseval theorem in wavelet framework and smoothing operation are conducted, so as to enhance the sparsity of the estimated transient impulses while restraining the unrelated interference. Meanwhile, the strict convexity of the cost function is proved theatrically, and the effective optimization algorithm with fast convergence speed based upon the alternating direction method of multipliers (ADMM) is proposed through splitting the optimization function into two simple sub-parts. The simulated case and two experimental cases regarding bearings failure in corn thresher and gear-root crack failure in reducer are investigated for theoretical verification of the proposed approach, the results indicate that diagnosis accuracy and fault impulses amplitude of the algorithm are superior to the state-of-the-art benchmarks.

Joint Projection Learning and Tensor Decomposition-Based Incomplete Multiview Clustering.

Incomplete multiview clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn similarity subgraphs from original incomplete multiview data and seek complete graphs by exploring the incomplete subgraphs of each view for spectral clustering. However, the graphs constructed on the original high-dimensional data may be suboptimal due to feature redundancy and noise. Besides, previous methods generally ignored the graph noise caused by the interclass and intraclass structure variation during the transformation of incomplete graphs and complete graphs. To address these problems, we propose a novel joint projection learning and tensor decomposition (JPLTD)-based method for IMVC. Specifically, to alleviate the influence of redundant features and noise in high-dimensional data, JPLTD introduces an orthogonal projection matrix to project the high-dimensional features into a lower-dimensional space for compact feature learning. Meanwhile, based on the lower-dimensional space, the similarity graphs corresponding to instances of different views are learned, and JPLTD stacks these graphs into a third-order low-rank tensor to explore the high-order correlations across different views. We further consider the graph noise of projected data caused by missing samples and use a tensor-decomposition-based graph filter for robust clustering. JPLTD decomposes the original tensor into an intrinsic tensor and a sparse tensor. The intrinsic tensor models the true data similarities. An effective optimization algorithm is adopted to solve the JPLTD model. Comprehensive experiments on several benchmark datasets demonstrate that JPLTD outperforms the state-of-the-art methods. The code of JPLTD is available at https://github.com/weilvNJU/JPLTD.

Cross-View Approximation on Grassmann Manifold for Multiview Clustering.

In existing multiview clustering research, the comprehensive learning from multiview graph and feature spaces simultaneously remains insufficient when achieving a consistent clustering structure. In addition, a postprocessing step is often required. In light of these considerations, a cross-view approximation on Grassman manifold (CAGM) model is proposed to address inconsistencies within multiview adjacency matrices, feature matrices, and cross-view combinations from the two sources. The model uses a ratio-formed objective function, enabling parameter-free bidirectional fusion. Furthermore, the CAGM model incorporates a paired encoding mechanism to generate low-dimensional and orthogonal cross-view embeddings. Through the approximation of two measurable subspaces on the Grassmann manifold, the direct acquisition of the indicator matrix is realized. Furthermore, an effective optimization algorithm corresponding to the CAGM model is derived. Comprehensive experiments on four real-world datasets are conducted to substantiate the effectiveness of our proposed method.

Unsupervised feature selection with high-order similarity learning

Graph-based unsupervised feature selection methods have successfully processed high-dimensional data since they can effectively preserve data structure information. However, most existing methods are facing two challenges: (1) it is unreliable to exploit the intrinsic spatial structure from raw data because of the existence of redundant information and noise; (2) they only consider the first-order similar information but ignore the high-order similar information (i.e., samples with similar neighborhood network structure should be similar to each other). This study proposes an unsupervised feature selection with high-order similarity learning (HSL) to tackle the above problems. The projection matrix, first-order similar information, and high-order similar information are simultaneously learned in a unified framework, such that the intrinsic structure information can be exploited in the clean data that come from a latent low-dimensional embedding subspace. Specifically, we project raw data into a low-dimensional embedding subspace to effectively eliminate redundant information and noise. Moreover, we simultaneously consider the first-order similarity and the high-order similarity in the low-dimensional embedding space so that a better graph Laplacian matrix can be learned to fully preserve the intrinsic structure information of raw data and simultaneously utilize the label information. Furthermore, we design an effective optimization algorithm to tackle the proposed HSL. Comprehensive experiments on real-world datasets verify the superiority and effectiveness of HSL.

Robust Feature Extraction via ℓ∞-Norm Based Nonnegative Tucker Decomposition

Feature extraction plays an indispensable role in image and video technology. However, it is difficult for traditional matrix based feature extraction methods to handle massive multi-dimensional data. This, alongside with the ubiquitous uncertainty (noise) in real-world data, resulted in many robust tensor based feature extraction models. However, these existing models did not consider the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">worst-case</i> model performance (i.e., the largest fitting error among all samples), which is critically important from a robust optimization perspective. In this paper, we propose a novel robust feature extraction model via ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -norm based nonnegative Tucker decomposition. The model is to minimize the maximum sample fitting error so as to overcome the influence of data uncertainty. Although the new model is nonconvex and nonsmooth, we design an effective iterative optimization algorithm with theoretical guarantee on its convergence for it. The performance of the new model on five real-world benchmark object classification and face recognition datasets under various corruption scenarios are evaluated, and the experimental results show the excellence of the new model by comparing to many existing models.

A comprehensive comparative study on intelligence based optimization algorithms used for maximum power tracking in grid-PV systems

For maximum power point tracking (MPPT) in the solar Photovolatic (PV) system, the meta-heuristic optimization techniques have been widely applied in the last few decades. This is due to the fact that traditional MPPT methodologies are unable to monitor the global MPP in the face of shifting environmental factors. Hence, it is essential to use an intelligence based controlling algorithm for MPPT controlling. The main purpose of this study is to investigate and assess the effectiveness of three cutting-edge and distinctive optimization algorithms for MPPT controlling, including Mongoose Optimization (MO), Prairie Dog Optimization Algorithm (PDOA), and hybrid PDOA + MO. It also aims to select the most effective and sophisticated optimization algorithm to meet the grid systems' energy requirements. This research's original contribution is the implementation and performance evaluation of three alternative meta-heuristic models for MPPT controlling. The goal of this effort is to maximize the energy yield from photovoltaic systems in order to meet the energy demands of grid systems. Three different controlling strategies, including MO + MPPT, PDOA + MPPT, and MO + PDOA + MPPT, are used in this work to achieve this goal. To evaluate the effectiveness and improved performance outcomes, a number of parameters have been taken into account in this work, including time, error, power, THD, and others. Furthermore, using a comprehensive simulation and comparison study, the outcomes of the MO, PDOA, and hybrid PDOA + MO techniques have also been tested and confirmed in this work. Comparisons are also made between the peak, settling, and increasing times of the present and proposed regulatory models. The results and waveforms generated demonstrate that the hybrid PDOA + MO performs better than the other controlling models in terms of enhanced efficiency of 99.5 %, low rising time of 1.6 s, low peak time of 1.05 s, minimal settling time of 1.24 s, error rate of 0.48, response time of 0.005 s, and tracking time of 0.0019 s

Multi-level correlation learning for multi-view unsupervised feature selection

Recently, unsupervised multi-view feature selection has received a lot of interest. However, current methods facilitate feature selection by preserving the local consistency of multi-view data, which is defined based on similarities of data within each view, but ignore the global topological consistency in data, which is defined based on the cross-view topological similarities of data between views and is essential for revealing the distribution of multi-view data. In light of this, this paper proposes a novel multi-view unsupervised feature selection method with multi-level correlation learning, termed Multi-Level Correlation Learning for Multi-View Unsupervised Feature Selection (MLCL). It simultaneously derives the global topological correlation structure from the cross-view topological similarities of data and the local geometric correlation structure from the local similarities of data within each view, to take advantage of both global and local consistencies of multi-view data. An effective optimization algorithm is then developed to resolve the optimization problem for the proposed model. Extensive experiments on eight publicly available datasets show that the proposed MLCL outperforms several state-of-the-art unsupervised multi-view feature selection models.