Advanced Optimization Algorithms Research Articles

A two-stage federated optimization algorithm for privacy computing in Internet of Things

With the advent of the Internet of things (IoT) era, federated learning plays an important role in breaking through traditional data barriers and effectively realizing data privacy and security in the process of sharing. However, the demand of the practical problems makes the algorithm still have great challenges in effectively balancing various factors, such as privacy security, accuracy, computing efficiency and so on. To challenge this problem, a two-stage federated optimization algorithm based on robust and multitasking learning is designed. In optimization client local model stage, an adaptive weight assignment mechanism is adopted to guide robust learning based on multiple untrusted sources data, which aims to ensure the credibility and robustness of the client model and obtain a reliable client model. To address the information leakage problem during the server–client global model optimization stage, a privacy patch layer is added to the client local model in multitask learning and maintain its privacy parameters stored on the client model during the global model parameter aggregation process, which aims to meet the personalized requirements and performance requirements of protecting the model privacy. To effectively measure the performance of our algorithm, two extensive experiments are carried out to verify the robustness and accuracy of model under different datasets, respectively. In addition, simulation results show that our algorithm successfully suppresses the impact of corrupted or irrelevant sources on performance, and its performance is better than the other two robust distributed learning baseline methods in the client local model optimization stage. At the same time, our algorithm achieves better accuracy performance than other advanced personalized optimization algorithms in the server–client global model optimization stage. Finally, it achieves a good balance between robustness, computational efficiency and model privacy protection.

AWOA: An Advanced Whale Optimization Algorithm for Signal Detection in Underwater Magnetic Induction Multi-Input–Multi-Output Systems

With the increasing exploitation and use of marine resources, the limitations of acoustic, optical, and radio frequency technologies for underwater communications have become increasingly apparent. Magnetic induction (MI) is a new communication technology that enables wireless data transmission via magnetic field coupling between transmitting and receiving coils. MI offers advantages such as channel stability, small antenna size, and no multi-path loss. Multi-input–multi-output (MIMO) is a multi-antenna technology that significantly increases system capacity and spectrum utilization without increasing bandwidth. The whale optimization algorithm (WOA) is a well-known bio-inspired algorithm that mimics the hunting behavior of whales to optimize swarm intelligence. This paper proposes a model for an underwater MIMO communication system based on magnetic induction. We then construct a signal detection algorithm for MI-MIMO systems using the advanced whale optimization algorithm (AWOA) and conduct simulation experiments to compare the performance and complexity of three standard signal detection algorithms: zero-forcing (ZF), minimum mean square error (MMSE), and maximum likelihood (ML). The experimental results show that AWOA achieves suboptimal results, as its bit error rate (BER) is close to that of the ML algorithm. Furthermore, the complexity of AWOA is comparable to that of the MMSE strategy. This work supports the development of a high-performance MI-based underwater communication system.

Predicting building energy utilization with energy plus simulation and advanced sea lion optimization algorithm based on Elman neural network

SummaryThere are different methods for modeling a structure and the building with the heat from the exterior and interior sources for evaluating a good performance, predicting energy utilization. Various methods, differing from simple regression to techniques, which are according to the physical ideology, could be utilized in simulation. There is a prevalent explanation for whole these methods, in which the input parameters are according to an actual database when they are accessible, or else, the energy utilization assessment would be underestimated or over‐estimated. In this investigation, two procedures have been presented that one is according to Elman neural network based on advanced sea lion optimization algorithm and the other one is according to physical ideology, that is, energy plus, which is prediction equipment for predicting building energy utilization. It can be seen that both methods can be proper for energy utilization prediction. In addition, an analysis that is parametric is implemented for the reflected structure on energy plus for evaluating the impact of some variables like the occupation of the building profile and weather database on predicting.

Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm

This paper proposes a novel optimization method for solving real-world optimization problems. It is inspired by a cooperative human phenomenon named the mountaineering team-based optimization (MTBO) algorithm. Proposed for the first time, the MTBO algorithm is mathematically modeled to achieve a robust optimization algorithm based on the social behavior and human cooperation needed in considering the natural phenomena to reach a mountaintop, which represents the optimal global solution. To solve optimization problems, the proposed MTBO algorithm captures the phases of the regular and guided movement of climbers based on the leader’s experience, obstacles against reaching the peak and getting stuck in local optimality, and the coordination and social cooperation of the group to save members from natural hazards. The performance of the MTBO algorithm was tested with 30 known CEC 2014 test functions, as well as on classical engineering design problems, and the results were compared with that of well-known methods. It is shown that the MTBO algorithm is very competitive in comparison with state-of-art metaheuristic methods. The superiority of the proposed MTBO algorithm is further confirmed by statistical validation, as well as the Wilcoxon signed-rank test with advanced optimization algorithms. Compared to the other algorithms, the MTBO algorithm is more robust, easier to implement, exhibits effective optimization performance for a wide range of real-world test functions, and attains faster convergence to optimal global solutions.

Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling.

In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-SA) in terms of average cost and metric makespan. Experimentally, it was concluded that the proposed advanced frog optimization algorithm can schedule tasks to the VMs more effectively as compared with other scheduling methods with a makespan of 6, average cost of 4, and fitness of 10.

A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning

Path planning technology is an important guarantee for the safe navigation of autonomous underwater vehicle (AUV) in water, and it is also an important indicator of the intelligence of autonomous underwater vehicle. Aiming at the path planning problem of AUV in complex environments, this article presents a reinforcement learning-based tuna swarm optimization algorithm called the QLTSO. In this algorithm, individuals are independent of each other, and the choice of each individual strategy is decided by reinforcement learning. Four strategies are set for each individual in the algorithm: spiral foraging, parabolic foraging, optimization adjustment and ESOS strategy. Finally, the cubic B-spline curve is used to smooth the path so that the autonomous underwater vehicle can better track the path. To verify the superiority of the QLTSO algorithm, the algorithm is compared with other advanced optimization algorithms. The simulation results show that the QLTSO algorithm can plan safe and effective AUV navigation paths in a variety of two-dimensional and three-dimensional complex environments with better convergence and robustness, and the planning success rate is up to 100%, which is an effective AUV path planning algorithm.

Boosting adversarial attacks with future momentum and future transformation

The transferability of adversarial examples under the black-box attack setting has attracted extensive attention from the community. Advanced optimization algorithms are one of the most successful ways to improve the transferability among all methods proposed recently. However, existing advanced optimization algorithms either can only slightly enhance the transferability of adversarial examples or take a large amount of computation time. We propose the future momentum and future transformation (FMFT) method to balance the transferability and computation overhead. The FMFT method incorporates two parts, future momentum (FM) and future transformation (FT). FM is inspired by the looking ahead property and updates adversarial examples with the future N-th step momentum during each iteration. FT, on the other hand, makes use of the input transformations during the progress of future momentum calculation to obtain a more robust gradient and reduce computation overhead. Additionally, we propose a new input transformation called random block scaling, which divides the image into various blocks and then scales the blocks differently. Empirical evaluations on the standard ImageNet dataset demonstrate the superiority of our FMFT.

Branch-and-bound algorithm for optimal sparse canonical correlation analysis

Canonical correlation analysis (CCA) is a family of multivariate statistical methods for extracting mutual information contained in multiple datasets. To improve the interpretability of CCA, here we focus on the mixed-integer optimization (MIO) approach to sparse estimation. This approach was first proposed for sparse linear regression in the 1970s, but it has recently received renewed attention due to advances in optimization algorithms and computer hardware. To exactly solve an MIO problem for optimal sparse CCA estimation, we propose a branch-and-bound algorithm based on the generalized eigenvalue problem for computing effective lower and upper bounds. We prove that our algorithm finds a solution with guaranteed optimality in terms of the canonical correlation. Computational results demonstrate that our method is much faster than direct application of optimization software to the MIO problem. Moreover, our method can provide better-quality solutions than can forward stepwise selection and L1-regularized estimation in terms of generalization performance. These results enhance the potential of optimal sparse estimation in multivariate statistical analyses.

Mpc-based motion planning and control enables smarter and safer autonomous marine vehicles: perspectives and a tutorial survey

Autonomous marine vehicles (AMVs) have received considerable attention in the past few decades, mainly because they play essential roles in broad marine applications such as environmental monitoring and resource exploration. Recent advances in the field of communication technologies, perception capability, computational power and advanced optimization algorithms have stimulated new interest in the development of AMVs. In order to deploy the constrained AMVs in the complex dynamic maritime environment, it is crucial to enhance the guidance and control capabilities through effective and practical planning, and control algorithms. Model predictive control (MPC) has been exceptionally successful in different fields due to its ability to sys-tematically handle constraints while optimizing control performance. This paper aims to provide a review of recent progress in the context of motion planning and control for AMVs from the perceptive of MPC. Finally, future research trends and directions in this substantial research area of AMVs are highlighted.

Modeling and optimization of multi-altitude leo satellite networks using cox point processes

This research focuses on the modeling and optimization of multi-altitude Low Earth Orbit (LEO) satellite networks using Cox point processes to achieve efficient coverage and performance analysis. LEO satellite networks have gained attention for their potential to provide global connectivity with reduced latency and increased network capacity. Accurately modeling the spatial distribution of satellites at different altitudes and optimizing their deployment pose significant challenges. This research proposes a mathematical framework based on Cox point processes to capture the randomness and irregularity of satellite deployments. Optimization algorithms, such as genetic algorithms, are employed to determine the optimal satellite locations, altitude allocation, and network parameters. Performance analysis considers metrics such as coverage probability, signal strength, interference levels, capacity, and quality of service. The research contributes to the development of advanced modeling techniques, optimization algorithms, and performance analysis frameworks, enabling efficient coverage and performance optimization in multi-altitude LEO satellite networks. The numerical examples and discussions illustrate the effectiveness and potential of the proposed approach in enhancing the design and operation of satellite communication systems

Design as Exploration

There are an increasing number of optimal-design paradigms used in architectural design nowadays. In these paradigms, a design task is formulated, or partially formulated, as an optimization problem. Multi-Disciplinary Optimization and Multi-Objective Optimization, as two important optimal-design paradigms, have shown their great potential in improving the performances of complex buildings in recent decades. Nevertheless, current paradigms for ill‑defined conceptual architectural design still lack ways to ensure the achievement of a reliable optimization problem, which hinders reliable design solutions despite the use of advanced optimization algorithms. To address this problem, it is necessary to shift the focus from Optimization Problem Solving to Optimization Problem Formulation. This research particularly focuses on knowledge‑supported, dynamic and interactive Optimization Problem Re-Formulation in order to construct a new Multi‑Objective and Multi-Disciplinary Optimization (MOMDO) method suitable for use in ill‑defined conceptual architectural design. The proposed method consists of two subtype methods: Non‑dynamic, Interactive Re-formulation method (Subtype-I) and Dynamic, Interactive Re‑formulation method (Subtype-II), which can be used to explore design space in a convergent and divergent manner respectively. To support the re-formulation, various kinds of information and knowledge need to be extracted by utilizing different computational techniques, such as advanced sampling algorithms, Self-Organizing Map, Hierarchical Clustering, Smoothing Spline Analysis of Variance, Two-Level Variable Structure and modular programming. Moreover, a software workflow that can provide these computational techniques is developed; it integrates McNeel’s Grasshopper, ESTECO's modeFRONTIER and simulation software tools Daysim, EnergyPlus and Karamba3D. With the support of this software workflow, the proposed method is demonstrated via two case studies concerning the conceptual design of indoor sports halls.

Calibration and evaluation of higher order continuum model for non- lane based mixed traffic environment

ABSTRACT .The present study aims to calibrate a novel multi-class non-lane-based continuum traffic flow model by applying advanced global search optimization algorithms such as Hybrid Search (Combination of genetic algorithm and Nelder Mead) and Generalized Pattern Search. To represent vehicular behavior in non-lane-based mixed traffic environment, the new continuum model is deduced from two-sided lateral gap car following theory along with few empirical observations. The newness of the selected model is that it mimics complex overtaking behavior of vehicles, effect of slow-moving vehicles on traffic stream and driver’s anticipation behavior. The search algorithms considered in this study have been tested and compared using the real-world data. From the analysis, it is observed that the proposed algorithms are more accurate in minimizing the cost function whereas convergence speed is also found to be better. The calibrated parameters are validated using platoon dispersion characteristics of vehicles and alternative transport policy measures.

A novel approach to using modern portfolio theory

Since their inception, modern portfolio theory (MPT) and the Sharpe ratio have been among the most popular investment methodologies. Although MPT has shortcomings, it effectively uses market sentiment to predict low-risk, high-earning portfolios. Our study reviews the current practice of using the Sharpe ratio or its derivative, the Sortino ratio, and suggests that investors could earn higher returns using Sterling and Treynor ratios, instead. We find that these two ratios offer higher-performing portfolios, and their statistical distributions have indicators that assist investors in determining when to use them. These new methods outperform current indexes and funds and are more robust than the capital asset pricing model used to evaluate investment performance. We conclude by suggesting additional research with different Sterling and Treynor ratios and advanced optimization algorithms.

An improved sparrow search algorithm based on quantum computations and multi-strategy enhancement

Aiming at the defects of the sparrow search algorithm (SSA), such as a deficient optimization accuracy and low search efficiency, the sparrow search algorithm based on quantum computations and multi-strategy enhancement (QMESSA) is proposed. Firstly, based on a diversified initial population strategy, an improved circle chaotic mapping theory was proposed, and an initial population with more randomness and diversity was obtained by combining quantum computations with a quantum gate mutation mechanism. Secondly, using an enhanced search strategy, an adaptive T-distribution and a new position update formula were constructed to accelerate the convergence and enhance its variability. Finally, a dynamic evolution formula was designed for the precision elimination mechanism. Based on this, individuals with poor fitness are replaced by new individuals generated using this formula. In addition, a new boundary control strategy was proposed. The convergence of QMESSA was systematically proven, and the proposed algorithm was tested on 24 benchmark functions and CEC 2017 functions. The experiment results as well as the results of a Wilcoxon rank-sum test show that QMESSA achieves a better comprehensive performance than SSA and other advanced optimization algorithms. Finally, the superiority of QMESSA was verified for several classical practical application problems. The source code of QMESSA is available at https://ww2.mathworks.cn/matlabcentral/fileexchange/120013-project1-0.

Multi-objective optimization of metal hydride hydrogen storage tank with phase change material

• Transient simulation and optimization of a metal hydride storage tank. • Application of a phase change material to the metal hydride tank for heat recovery. • Appropriate selection of design parameters for the optimization purpose. • Consideration of hydrogen to metal ratio and the absorption time as the objective functions. • Finding the design parameters that reasonably satisfies both objective functions. The use of hydrogen as a future energy source to supply world fuel is essential. The storage problem is one of the main challenges facing the use of hydrogen as a fuel. In this study, we model and optimize the transient operation of a 2D metal hydride storage tank. A phase change material (PCM) is attached to a metal hydride tank for heat recovery during hydrogen absorption and heat supply during desorption. Four parameters, porosity and diameter of the metal hydride tank, initial temperature and melting temperature of the PCM, are selected to optimize hydrogen absorption. An advanced optimization algorithm is applied to increase the hydrogen to metal ratio and decrease the absorption reaction time. The hydrogen to metal ratio and the absorption time, when system pressure equals equilibrium pressure, are considered as the two objective functions. The optimization and appropriate selection of design parameters result in a significant increase in the hydrogen to metal ratio. For better understanding, five particular points in the ranges of high, mediocre and low values of hydrogen to metal ratio are selected on the optimized Pareto front. The effects of the different parameters on the objective functions are discussed for the optimal points. A final (global) optimum point is obtained based on the LINMAP method that reasonably satisfies both objective functions and the results in this point are presented and discussed separately.

A General and Automatic RMS Current Oriented Optimal Design Tool for LLC Resonant Converters

LLC resonant converters are used in renewable energy applications to achieve high power efficiency conversions between different energy sources, buses, and energy storage elements. The traditional design methods for LLC resonant converters are based on a simple frequency-domain analysis (FDA). However, the accuracy of FDA is not satisfactory, especially at wide voltage range applications. To overcome the drawbacks of the traditional FDA method in wide-range applications, in this article, the time-domain analysis (TDA) is adopted to achieve accurate analysis of LLC converters. Efficiency is normally selected as the optimization objective, where the circuit components and accurate power loss models are required beforehand. To achieve a more general optimal design method for LLC converters, a simple root-mean-square (rms) current and TDA-based optimal design method is proposed. The optimal design is achieved by minimizing the converter rms current and ensuring some key design considerations. Moreover, an automatic design tool for the proposed rms-current-based optimal design method is developed, and advanced optimization algorithms are introduced to improve the optimization speed. A 2.5-kW experimental prototype is built using the optimal circuit parameters. Experimental results under different operating conditions are demonstrated, and the results are consistent with the theoretical analysis. Efficiency comparisons between the proposed optimal design method and conventional design method are made. The proposed optimal design method can improve the converter efficiency by a maximum value of 2.14%.

Dimensional synthesis of four-bar mechanisms using Rao algorithms and their variants

This paper presents the dimensional synthesis of four-bar mechanisms for path generation. The synthesis of the four-bar mechanisms is proposed using nine advanced optimization algorithms, namely the Rao-1 algorithm, Rao-2 algorithm, Rao-3 algorithm, self-adaptive multi-population (SAMP) Rao-1 algorithm, SAMP Rao-2 algorithm, SAMP Rao-3 algorithm, quasi-oppositional-based (QO) Rao-1 algorithm, QO Rao-2 algorithm, and QO Rao-3 algorithm. Six different cases of synthesis of a four-bar mechanism are considered. The brief literature review based on the synthesis of mechanisms is also presented. The designs of mechanisms obtained using Rao algorithms and their variants are compared with the designs acquired using other advanced optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, differential evolution (DE), imperialist competitive algorithm (ICA), hybrid GA-DE algorithm, simulated annealing (SA), Malaga university mechanism synthesis algorithm (MUMSA), IMMa optimization algorithm (IOA), hybrid Taguchi random coordinate search algorithm (HTRCA), hybrid GA and fuzzy logic (GA–FL) algorithm, ant-gradient method (AGM), teaching–learning-based optimization algorithm (TLBO), and hybrid Lagrange interpolation differential evolution algorithm (HLIDE). The comparison of results reveals the efficacy of Rao algorithms and their variants for synthesizing the four-bar mechanism.

A novel combined forecasting system based on advanced optimization algorithm - A study on optimal interval prediction of wind speed

Wind speed forecasting is becoming increasingly crucial for an environmentally friendly and sustainable economy because of the renewability and benefits of wind energy. Currently, many scholars have proposed various approaches to forecasting wind speed; however, due to the limitations of point prediction and drawbacks of traditional individual methods, it is challenging to acquire satisfactory results. Compared with the previous articles, this paper combines a data preprocessing strategy by fuzzification, interval estimation, and advanced optimization methods to enhance the accuracy of wind speed forecasting. Moreover, the weight coefficient allocated by a multi-objective algorithm is proved to reach Pareto optimal solution in theory. Based on the comparative experiments and discussion of the performance of the developed combined forecasting system and other control groups, it is revealed that the combined system not only outperforms for predicting wind speed with higher accuracy and stability but also enables a valid assessment of uncertainty than the former studies. It can be convinced that the developed predicting system is an appropriate and efficient tool for further practical applications in energy systems.

How to do (or not to do)… health resource allocations using constrained mathematical optimization.

Despite the push towards evidence-based health policy, decisions about how to allocate health resources are all too often made on the basis of political forces or a continuation of the status quo. This results in wastage in health systems and loss of potential population health. However, if health systems are to serve people best, then they must operate efficiently and equitably, and appropriate valuation methods are needed to determine how to do this. With the advances in computing power over the past few decades, advanced mathematical optimization algorithms can now be run on personal computers and can be used to provide comprehensive, evidence-based recommendations for policymakers on how to prioritize health spending considering policy objectives, interactions of interventions, real-world system constraints and budget envelopes. Such methods provide an invaluable complement to traditional or extended cost-effectiveness analyses or league tables. In this paper, we describe how such methods work, how policymakers and programme managers can access them and implement their recommendations and how they have changed health spending in the world to date.

Nonlinear system control analysis and optimization using advanced Pigeon-Inspired optimization algorithm

The Pigeon-Inspired optimization (PIO) algorithm is a novel intelligent optimization algorithm inspired by birds’ behavior as their travel. This; behavior modeled to be used for solving many optimization problems in different fields. However; it always suffers from unstable behavior when used with nonlinear; time-varying systems. In; this paper, this algorithm is adapted to calculate the optimum controller gains for roll and pitch channels in a guided tactical missile. The; vehicle model is presented in a nonlinear; form and then shown in a linearized form for the sake of an autopilot design. The PIO; algorithm is supported and accompanied by an adaptive algorithm to determine the initial states and constraints for the PIO algorithm to enhance the behavior of the optimization algorithm to speed up the convergence rate to reach an optimum and feasible solution. Also; an estimation function is incorporated to estimate model parameters variation such as dynamic pressure, stability derivatives, and mass properties. Meanwhile; a comparative analysis is carried out with original PIO and particle swarm optimization algorithms, utilizing a non-linear; model with the presence of noise source and disturbance to ensure the ability of the algorithm to make the autopilot robust and stable against several sources of uncertainties.