Traditional Optimization Algorithms Research Articles

Enhancing the Lifetime of WSN Using a Modified Ant Colony Optimization Algorithm

Wireless sensor networks (WSNs) have been extensively used in various fields, such as health, defense, education, and industrial applications, to collect and transmit environmental data to the base station. However, energy efficiency is a significant challenge in WSNs, as data transmission is typically limited to a single route, leading to excessive energy consumption by the nodes along that route. This can lead to a decrease in the network's overall efficiency and effectiveness. To address this issue, this study aims to extend the lifespan of WSNs by optimizing route selection based on three variables: residual node energy, distance to the base station, and number of shared neighbors. In this paper, the authors propose three systematic approaches, namely Energy-Aware ACO Routing (EACO), Cost-Effective ACO Routing (CEACO), and Cost-Efficient Node Replacement Strategies ACO (CERACO), to enhance the lifetime of WSNs. These approaches consider various factors such as cost, energy consumption, replacement, and reliability. The paper provides a practical guide for researchers and practitioners to overcome the challenges related to energy efficiency and cost-effectiveness in WSNs. Experimental results demonstrate that the first dead node occurs later with the proposed methods than with the traditional Ant Colony Optimization (ACO) algorithm.

Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research.

A novel optimally tuned super twisting sliding mode controller for active and reactive power control in grid‐interfaced photovoltaic system

AbstractIn photovoltaic (PV) systems, inverters play a crucial role for supplying electricity to meet the demand while maintaining power quality. For a local load connected to a grid‐interfaced photovoltaic (GIPV) system, active and reactive power control is necessary at the distribution level. Thus, the foremost purpose of this article is to get the best optimally designed robust controller for control of active and reactive power. A GIPV system with Improved Arithmetic Optimisation Algorithm (IAOA)‐based Super Twisting Sliding Mode Controller (ST‐SMC) methodology has been proposed in this article for active and reactive power management. The conventional PI controller in the GIPV system that is most frequently used has considerable undershoot and a long settling period. PI controller tuning parameters were also changed to account for the wide change in the reference pattern. Therefore, STSMC and SMC are used for ensuring robustness against external disturbances. The conventional SMC comes out to have a chattering issue. Furthermore, the proposed IAOA technique is validated through some benchmark functions. The proposed IAOA technique outperforms Particle Swarm Optimisation (PSO), Forensic Based Investigation (FBI), and Traditional Arithmetic Optimisation Algorithm (TAOA) in terms of the number of iterations and accurately achieving optimal solutions for active and reactive power control. The results show that the proposed IAOA‐based STSMC technique has an improved performance of settling time and undershoot for active and reactive power control. This article also presents stability analysis and robustness test of the above mentioned controllers to illustrate the effectiveness of each optimally designed controller. A 40 kW GIPV system performance is evaluated using the MATLAB environment, and the results are validated in a real‐time simulator platform OPAL‐RT 4510.

Improved reinforcement learning-based real-time energy scheduling for prosumer with elastic loads in smart grid

In this study, we investigate the dynamic energy-scheduling problem of a prosumer (producer/consumer) with an energy storage device (EDS) and elastic loads. The goal is to develop efficient real-time scheduling strategies for prosumers, and to minimise their total long-term costs (i.e. cost of energy purchased from the external grid and depreciation cost of the EDS). The challenges are twofold: the uncertainty of the energy output of the prosumer and the time-coupling constraints of the EDS and elastic loads. To address these challenges, we first describe the dynamic energy-scheduling problem as a Markov decision process. Then, an approximate state dual-agent Q-learning algorithm is proposed to solve the optimal dynamic scheduling problem by improving the model-free reinforcement learning(RL) method. Compared with the traditional RL method, the proposed algorithm reduces the system-state dimensions and exhibits improved performance. The proposed algorithm can only be assisted by mutual interactions between the environment with a reward feedback mechanism to dynamically respond to uncertain changes in the environments, without modelling or predicting the system environments. Finally, extensive empirical evaluations using real-world traces are conducted to study the effectiveness of the proposed algorithm. The results show that the proposed algorithm can reduce the total cost of the prosumer by up to 6.3%, 11.7% and 22.4% compared with the traditional RL method, Lyapunov optimisation and greedy algorithm, respectively.

An effective surrogate model assisted algorithm for multi-objective optimization: application to wind farm layout design

Due to the intricate and diverse nature of industrial systems, traditional optimization algorithms require a significant amount of time to search for the optimal solution throughout the entire design space, making them unsuitable for meeting practical industrial demands. To address this issue, we propose a novel approach that combines surrogate models with optimization algorithms. Firstly, we introduce the Sparse Gaussian Process regression (SGP) into the surrogate model, proposing the SGP surrogate-assisted optimization method. This approach effectively overcomes the computational expense caused by the large amount of data required in Gaussian Process model. Secondly, we use grid partitioning to divide the optimization problem into multiple regions, and utilize the multi-objective particle swarm optimization algorithm to optimize particles in each region. By combining the advantages of grid partitioning and particle swarm optimization, which overcome the limitations of traditional optimization algorithms in handling multi-objective problems. Lastly, the effectiveness and robustness of the proposed method are verified through three types of 12 test functions and a wind farm layout optimization case study. The results show that the combination of meshing and SGP surrogate enables more accurate identification of optimal solutions, thereby improving the accuracy and speed of the optimization results. Additionally, the method demonstrates its applicability to a variety of complex multi-objective optimization problems.

A Kriging-based decoupled non-probability reliability-based design optimization scheme for piezoelectric PID control systems

When dealing with optimization problems, the introduction of uncertainty will greatly increase the difficulty of solving the problem. The traditional reliability-based design optimization (RBDO) algorithm coupling uncertainty parameters and design variables, and solve optimization with optimization algorithms. The traditional RBDO scheme is easy to understand and realize, but it will greatly increase the calculation time, and it often does not get ideal results when solving some problems with high time complexity. The Kriging-based decoupled non-probability reliability-based design optimization (K-BRBDO) scheme proposed in this paper decouples uncertainty parameters and design variables, establishes a decoupled optimization system based on Kriging model, and proposes a new adaptive learning strategy which involves two stages of enrichment to improve the accuracy of the surrogate model in the region of interest. Finally, the effectiveness and accuracy of the optimization scheme are verified by two numerical examples and an engineering example.

Model optimization of a high-power commercial PEMFC system via an improved grey wolf optimization method

Proton exchange membrane fuel cell (PEMFC) models are conventionally established with a set of parameters identified under steady-state operating conditions. However, such an approach is insufficient to accurately capture the dynamic characteristics of multi-parameter changes in real-world scenarios. This paper develops a semi-empirical model for a 110-kW commercial PEMFC system based on its dynamic operation data to remedy the defects. To improve the fitting accuracy of the semi-empirical PEMFC model, an improved grey wolf optimization (IGWO) algorithm is proposed for model parameter identification. The IGWO algorithm adopts chaotic mapping to optimize the initial population distribution, and a random walk strategy is incorporated to boost the local search ability of the traditional grey wolf optimization (GWO) algorithm. The effectiveness of this IGWO algorithm in optimizing the semi-empirical model is experimentally verified on the 110-kW PEMFC system under highly dynamic operating conditions. Results show that the proposed IGWO algorithm can effectively identify the semi-empirical model’s parameters, establishing a stable and robust model that outperforms those based on traditional metaheuristic algorithms such as GWO, particle swarm optimization, and genetic algorithm. The demonstrated improvement renders it as better suited for optimizing PEMFC semi-empirical models under real-world operating conditions.

A novel hybrid particle swarm optimization with marine predators

Particle swarm optimization algorithms are often applied to solve optimization problems. However, the traditional particle swarm optimization algorithm has a single search method and is less capable of exploration and exploitation when solving high-dimensional and complex problems. In this paper, a hybrid particle swarm optimization algorithm for marine predators (HMPPSO) is proposed by combining the multi-stage search strategy of marine predators algorithm (MPA). We divide the search process of HMPPSO into three stages: the first stage uses Brownian motion for exploration; the middle stage divides the population into two parts, the first half remains responsible for exploration through Brownian motion. Besides, in the second half of the population, a random wandering strategy is proposed to randomly select five particles so that it can control the update of particles to prevent falling into the local optimal solution; the learning strategy is improved in the later stage. The control parameters are used to adjust the strategy, while making the single case learning method and the example averaging method alternate. This strategy accelerates the convergence of the algorithm while enhancing the diversity of the algorithm in the later stage and improving the development ability of the algorithm. In addition, the population diversity is enhanced using chaotic initialization and opposition-based learning strategies. The algorithm in this paper is applied to different types of CEC2017 benchmark test functions and four multi-dimensional non-linear structural design optimization problems. Compared with other recent algorithms, the results show that the performance of HMPPSO is significantly better than other algorithms.

Performance prediction and optimization of a hybrid renewable-energy-based multigeneration system using machine learning

This paper proposes an innovative multi-generation hybrid renewable energy system (HRES). The proposed HRES is optimized using the recently developed equilibrium optimizer algorithm and the efficacy of this algorithm is investigated by comparing the optimized sizing with results obtained from more traditional optimization algorithms, i.e., gray wolf optimizer, lightning search algorithm, and artificial bee colony. The results indicate that using the equilibrium optimizer algorithm outperforms conventional optimization algorithms in minimizing the levelized cost of electricity to $0.83 per kWh. This proves applicability of this modern optimization algorithm in improving HRES, which is a novelty of this research. Moreover, machine learning is implemented to predict the exergy efficiency of the proposed HRES, which provides insight into the performance and sustainability of the system. A thermodynamic database is established to train the machine learning model using secondary data. Using the multi-layer perceptron class, the exergy efficiency of the system is predicted; the trained model is able to deliver a prediction with an R-Squared value of 0.98. Thus, the appropriateness of machine learning algorithms in predicting the performance of HRES is verified. This research will, therefore, pave the way towards expanding real-world application of HRES's. This study provides insights into the optimization and economic and environmental benefits of hybrid renewable energy systems and inform policymakers on how to incentivize their implementation. As the study is performed for a specific location, exploring the feasibility of implementing the proposed system in other locations is recommended for future research.

A Hybrid Particle Swarm Optimization with Butterfly Optimization Algorithm Based Maximum Power Point Tracking for Photovoltaic Array under Partial Shading Conditions

The key objective of this paper is to develop a photovoltaic (PV) maximum power point tracking (MPPT) algorithm based on particle swarm optimization–butterfly optimization algorithm (PSO-BOA) that is adapted for partial shading conditions (PSCs). Generally, conventional MPPT techniques are often unable to accurately locate the global maximum power point (GMPP) generated by partial shading in PV systems. As a result, a significant decrease in power output occurs. The traditional particle swarm optimization (PSO) algorithm traps the local maxima point easily, while the butterfly optimization algorithm (BOA) has slow convergence speed and large oscillations during its use in research. To address the limitations of the aforementioned PSO and BOA algorithms, the MPPT strategy of PV systems combining PSO-BOA is presented, which can ameliorate the efficiency and accuracy in PSCs. In this paper, the control parameter of sensory modality in the BOA can be acquired based on logistic mapping, and the self-adaptive adjustment of the inertial weight of the PSO algorithm is designed. According to the simulation findings, the suggested method is more suitable than PSO and BOA with respect to intricate shading-induced variations in irradiance and changes in external temperatures. The average tracking time is less than 0.5 s, and the tracking accuracy is not less than 99.94%. Especially under sudden variations in irradiance and temperature conditions, the tracking time of the PSO-BOA algorithm is only 49.70% of that of the PSO algorithm and 55.63% of that of the BOA. Therefore, the MPPT method presented has the ability to improve the oscillations and result in less convergence speed, which in turn accurately tracks the GMPP.

Research on Wind Turbine Fault Detection Based on the Fusion of ASL-CatBoost and TtRSA.

The internal structure of wind turbines is intricate and precise, although the challenging working conditions often give rise to various operational faults. This study aims to address the limitations of traditional machine learning algorithms in wind turbine fault detection and the imbalance of positive and negative samples in the fault detection dataset. To achieve the real-time detection of wind turbine group faults and to capture wind turbine fault state information, an enhanced ASL-CatBoost algorithm is proposed. Additionally, a crawling animal search algorithm that incorporates the Tent chaotic mapping and t-distribution mutation strategy is introduced to assess the sensitivity of the ASL-CatBoost algorithm toward hyperparameters and the difficulty of manual hyperparameter setting. The effectiveness of the proposed hyperparameter optimization strategy, termed the TtRSA algorithm, is demonstrated through a comparison of traditional intelligent optimization algorithms using 11 benchmark test functions. When applied to the hyperparameter optimization of the ASL-CatBoost algorithm, the TtRSA-ASL-CatBoost algorithm exhibits notable enhancements in accuracy, recall, and other performance measures compared with the ASL-CatBoost algorithm and other ensemble learning algorithms. The experimental results affirm that the proposed algorithm model improvement strategy effectively enhances the wind turbine fault detection classification recognition rate.

Optimum design of planar quasi‐Yagi antenna for wearable Internet of Things (IoT) applications

AbstractThis paper presents the design of a novel dual‐band quasi‐Yagi antenna for on‐body communications. The proposed antenna is designed on 0.05 mm thin microwave laminate and is designed to cover the 2.45 and 3.5 GHz spectrum to meet the needs of on‐body Internet of Things networks. Microstrip to slot‐line transition is used for the excitation of the feeder element. The conventional feeder of the quai‐Yagi is replaced with F‐shaped radiators to obtain a dual‐band response. In addition, a pair of directors are used to enhance the antenna gain. The proposed quasi‐Yagi antenna is optimized using the spider monkey algorithm. The chosen algorithm is used to synthesize the dimensions of the dual‐band radiator and is preferred to obtain quick convergence, unlike the traditional optimization algorithms. The optimized planar quasi antenna offers 5% bandwidth in both operating bands with a measured gain of 5.7 and 5.8 dBi, respectively. The optimization time is also considerably reduced in comparison with traditional optimization algorithms. The simulated and optimized antenna is fabricated and tested.

Optimal Configuration of Reactive Power in Distribution Network with Power Electronic Transformer

Traditional means of reactive power regulation are limited to adjusting the ratio of the On-Load Tap Changer or changing the access position of the reactive power compensation device and its access capacity. In order to save the cost of putting in reactive power compensation devices, this paper proposes to use the characteristics of the reactive power output function on both sides of the power electronic transformer to regulate the system reactive power distribution. For this purpose, a reactive power optimization model including distributed power supplies and power electronic transformers is constructed with the objectives of minimizing voltage excursions, maximizing voltage stability margins and minimizing active losses in the network. At the same time, based on the characteristics of decision variables, cross-feedback hybrid optimization algorithm is used to solve different types of variables, and an improved wolf pack algorithm using Tent chaotic map and Levy flight strategy is proposed to improve the solution efficiency of the algorithm. The experimental results conclude that the proposed model and algorithm are valid, and the algorithm has significant advantages in terms of model solution time and global search capability when compared with the traditional Wolf Pack algorithms and Particle Swarm Optimization algorithms.

Cooperative control of multiple intersections combining agent and chaotic particle swarm optimization

The regional intersection can be regarded as an agent model, that should be controlled and optimized. The traditional particle swarm optimization algorithm has been enhanced through optimization techniques and the inclusion of chaos algorithms and agent interactive and cooperative control technology, resulting in the development of a chaotic particle swarm optimization algorithm. Multi-agent is applied to the collaborative control of intersections to realize the collaborative decision-making of multi-intersections. The state and action of intersection signal control agent are defined to reduce the dimension of state-action space and speed up the convergence rate. The proposed method takes vehicle delay, vehicle queue length and regional access ratio as optimization objectives, implements regional traffic signal control strategy and improves overall traffic efficiency. By constructing a simulation environment based on Paramics software, the effectiveness of the regional traffic signal control method based on chaotic particle swarm optimization algorithm is verified, and the convergence efficiency and signal timing optimization have interactive effects.

Regularization Strategy Aided Robust Unsupervised Learning for Wireless Resource Allocation

Unsupervised learning (UL) is widely used in the wireless resource allocation problems due to its lower computational complexity and better performance compared with traditional optimization algorithms. Since wireless resource allocation problems usually have several constraints, primal-dual learning based UL framework are widely adopted. However, the primal-dual learning approach has the problem of oscillation around the constraint threshold while training and there may be serious constraint violations when deployment. In addition, although the output of the neural network can also be restricted to the feasible region by the penalty function method, the optimality of such training methods cannot be guaranteed. In this paper, we combine the primal dual learning method with the penalty function method and propose a regularized unsupervised learning (RUL) framework to enhance the robustness of the primal-dual learning based UL framework. In the proposed RUL framework, we use regularization techniques to improve the robustness of primal-dual learning by reducing the risk of constraint violations while training. A quadratic penalty term is introduced into the Lagrangian function of the wireless optimization problem where the constraints can be equivalent to equality constraints to form its augmented Lagrangian function. In the simulation, we give a simple point to point power optimization problem as an example to show that the proposed RUL can improve the robustness of constraint convergence, and can also accelerate training speed.

Thermal layout optimization for 3D stacked multichip modules

To satisfy the industry’s demand for high storage density in devices of the same size, the multichip stacking packaging technology has emerged. However, 3D stacked multichip modules (MCMs) can lead to higher power and heat flow density, highlighting thermal issues in the packaging structure. This paper utilizes a high-order finite element method (FEM) for efficient simulation of integrated circuits’ thermal problems. Additionally, we propose a particle swarm optimization (PSO) scheme based on dynamic strategies to overcome traditional optimization algorithm limitations, including slow convergence and difficulties in obtaining global optimal solutions. We conducted thermal design optimization on the multichip stacking structure under convection boundary conditions to achieve optimal thermal performance. The proposed multichip stacking collaborative optimization scheme has fast convergence speed, and the optimized multichip layout exhibits minimal temperature differences and balanced heat distribution.

A Parallel distributed genetic algorithm using Apache Spark for flexible scheduling of multitasks in a cloud manufacturing environment

ABSTRACT Cloud manufacturing is one of the modern manufacturing systems that aim to control and manage production processes through a cloud platform to optimize the usage of resources and achieve customer objectives. Scheduling large-scale problems with diversity in the required tasks and the available services is a significant challenge for any researcher, especially with the possibility of recurring dynamic events, such as the arrival of new tasks. Thus, our first objective is to present a mixed-integer mathematical model that demonstrates the complexity of scheduling problems in a dynamic CMfg environment. We developed a parallel distributed genetic algorithm (PDGA) for optimizing large-scale scheduling problems on Apache Spark. The PDGA adopts a resilient distributed dataset (RDD) to improve the decomposition of the population and also enhance both the performance and the speed of the evolution process. The PDGA then updates the schedule by using a greedy strategy to improve the overall performance. To verify the effectiveness of the PDGA, we evaluated the algorithm on a benchmark and generated 8 large-scale problems based on this benchmark. The experiments show that the PDGA provides better performance and lower computational time compared to the traditional genetic algorithm and particle swarm optimization algorithm.

Adaptive Optimization Operation of Electric Vehicle Energy Replenishment Stations Considering the Degradation of Energy Storage Batteries

As the construction of supporting infrastructure for electric vehicles (EV) becomes more and more perfect, an energy replenishment station (ERS) involving photovoltaics (PV) that can provide charging and battery swapping services for electric vehicle owners comes into the vision of humanity. The operation optimization of each device in the ERS is conducive to improving the service capacity of the ERS, extending the service life of the energy storage batteries (ESB), and enhancing the economic benefits of the ERS. However, traditional model-based optimization algorithms cannot fully consider the stochastic nature of EV owners’ charging and battery swapping demands, the uncertainty of PV output, and the complex operating characteristics of ESB. Therefore, we propose a deep reinforcement learning-based adaptive optimal operation method for ERS considering ESB’s losses. Firstly, a mathematical model of each device in the ERS is established, and a refined energy storage model is introduced to describe ESB’s capacity degradation and efficiency decay. Secondly, to solve the dimensional disaster problem, the state space and action space selection method, and the charging strategy of batteries in the battery swapping station (BSS) are proposed to apply to the ERS, thus modeling the ERS optimization operation problem as a Markov decision process. Then, the solution is performed using a neural network-based proximal policy optimization (PPO) algorithm, consisting of a recurrent neural network that extracts information about the PV outflow trend and a deep neural network used to generate a control policy. Finally, the effectiveness of the proposed method is verified by simulation calculations, which not only enable adaptive decision-making under different PV output scenarios, but also consider the availability of EV battery swapping services, energy storage losses, and the economic benefits of the ERS.

Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm

To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant colony optimization (SEACO) algorithm to achieve fast, accurate, real-time interactive and high-quality learning path recommendations. Consequently, an online personalized learning path optimization model with a time window was constructed first. This model not only considers the learning order of the recommended learning resources, but also further takes the review behavior pattern of learners into consideration, which improves the quality of the learning path recommendation. Then, this study constructed a SEACO algorithm suitable for online personalized learning path recommendation, from the perspective of optimal learning path prediction, which predicts path pheromone evolution by mining historical data, injecting the domain knowledge of learning path prediction that can achieve best learning effects extracted from domain experts and reducing invalid search, thus improving the speed and accuracy of learning path optimization. A simulation experiment was carried out on the proposed online personalized learning path recommendation model by using the real leaner learning behavior data set from the British “Open University” platform. The results illustrate that the performance of the proposed online personalized learning path recommendation model, based on the SEACO algorithm for improving the optimization speed and accuracy of the learning path, is better than traditional ACO algorithm, and it can quickly and accurately recommend the most suitable learning path according to the changing needs of learners in a limited time.

Water resource management based on wireless virtualization in wastewater reclamation and utilization in dryland botanical garden

Abstract The research on the management and distribution of renewable water resources mostly adopts the method of combining the intelligent optimization algorithm and the target planning model. In this paper, four water distribution users are used as categories, and the problem of renewable water resources allocation is transformed into a classification problem, which is included in the category of wireless virtualization processing problems, and wireless virtualization is selected to support the allocation of renewable water resources. Experiments show that the water shortage of the water distribution scheme obtained by the method in this paper is lower than that of the optimal water distribution scheme of the traditional intelligent optimization algorithm. It can also be seen from the water distribution cost and water shortage of a single user that the water distribution effect of the method in this paper is better than that of the traditional intelligent optimization algorithm (P<0.05). It shows that the research on the management and distribution method of renewable water resources based on wireless virtualization not only provides new ideas for dealing with configuration problems but also has important significance for alleviating the shortage of water resources in our country and the management and utilization of renewable water resources.