Optimization Algorithm Research Articles

Predicting Sleep Disorders: Leveraging Sleep Health and Lifestyle Data with Dipper Throated Optimization Algorithm for Feature Selection and Logistic Regression for Classification

Predicting Sleep Disorders: Leveraging Sleep Health and Lifestyle Data with Dipper Throated Optimization Algorithm for Feature Selection and Logistic Regression for Classification

Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm

Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm

Feature Selection Risk Factors Cervical Cancer Using Hybrid Methods Random Forest and FOX-Inspired Optimization Algorithm

Feature Selection Risk Factors Cervical Cancer Using Hybrid Methods Random Forest and FOX-Inspired Optimization Algorithm

Improved Osprey Optimization Algorithm with Multi-Strategy Fusion

Improved Osprey Optimization Algorithm with Multi-Strategy Fusion

Automated Layout Design of Hydraulic Components With Constraints on Flow Channels

Abstract The lightweight design of hydraulic equipment has always been of vital interest. Additive manufacturing (AM) technology can meet the manufacturing requirements of heteroideus and lightweight hydraulic equipment. However, traditional layout optimization often cannot satisfy the functional constraints of hydraulic components. This article proposes a design method of function-based automatic layout optimization for hydraulic components to solve this problem. The proposed method combines multi-component layout optimization with flow-up channel path planning and uses the triangular mesh model of hydraulic components directly as layout units. The spatial pose of the layout unit is used as the gene sequence for a genetic algorithm (GA). To meet the functional constraints, this study also proposes a fast, accurate collision detection algorithm for irregular 3D models and the generating strategy for follow-up flow channels. Here, the volume of the layout units, the total centroid radius of the layout plan, the length of flow channels, and the pressure loss are taken as the objective functions, and an automatic layout optimization algorithm for hydraulic components is developed. By optimizing the initial layout plan of an aviation electro-hydrostatic actuator (EHA), the characteristic volume of the optimized layout is reduced by 30.68% and the total length of the flow channels is decreased by 39.53%, demonstrating the efficiency of this method for lightweight hydraulic equipment design.

Performance response analysis and optimization for integrated renewable energy systems using biomass and heat pumps: a multi-objective approach

The focus of this study is to optimize the exploration of biomass-driven multi-energy systems, which include combined heat, power, and gas generation. The objective is to enhance the thermal, environmental, and economic performance indicators of the system. The optimization objectives encompass the quantities of internal combustion engines and air source heat pumps, as well as the dimensions of tanks utilized for anaerobic fermentation. A mathematical model was developed to optimize multiple objectives for combined heat, power, and gas generation systems by employing multi-objective intelligent optimization algorithms. The validation and analysis were conducted using rural residences in Lanzhou, Gansu Province, China, as a case study. The sensitivity analysis of biomass gasification combined heat and power systems was conducted from both technical and cost perspectives, examining the dynamic impact characteristics on the outcomes of multi-objective optimization. The findings indicate that the annual energy-saving rate of the optimized combined generation system decreased from 3.62% to -6.78%, while the growth in carbon emissions reduction rate increased from 76.05 to 81.38%, and the annual cost-saving rate grew from 0.97 to 14.96%. The power generation efficiency of the cogeneration station and hydraulic retention time were found to have a significant impact on the multi-objective optimization results of the combined generation system among the technical parameters. The unit cost of anaerobic fermentation tanks had a more significant impact on the multi-objective optimization results in terms of cost parameters, compared to the price of biogas residue.

Maximizing Energy Efficiency and System Throughput usinga Self-Adaptive Penalty Function with a Whale Optimization Algorithm in Social-Aware Device-to-Device Communications

Maximizing Energy Efficiency and System Throughput usinga Self-Adaptive Penalty Function with a Whale Optimization Algorithm in Social-Aware Device-to-Device Communications

A Neuroadaptive Position-Sensorless Robust Control for Permanent Magnet Synchronous Motor Drive System with Uncertain Disturbance

The Permanent Magnet Synchronous Motor (PMSM) drive system is extensively utilized in high-precision positioning applications that demand superior dynamic performance across various operating conditions. Given the non-linear characteristics of the PMSM, a neuroadaptive sensorless controller based on B-spline neural networks is proposed to determine the control signals necessary for achieving the desired performance. The proposed control technique considers the system’s non-linearities and can be adapted to varying operating conditions, all while maintaining a low computational cost suitable for real-time operation. The introduced neuroadaptive controller is evaluated under conditions of uncertainty, and its performance is compared to that of a conventional PI controller optimized using the Whale Optimization Algorithm (WOA). The results demonstrate the viability of the proposed approach.

A hybrid model based on complementary ensemble empirical mode decomposition, sample entropy and long short-term memory neural network for the prediction of time series signals in NPPs

Accurate and reliable predictions are fundamental for the condition monitoring and maintenance of components and systems in nuclear power plants (NPPs). In this work, we propose a hybrid condition prediction approach based on the combination of complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SampEn) and optimized long short-term memory (LSTM) neural network. Firstly, the CEEMD decomposes the time series signals into multiple subsequences called intrinsic mode functions (IMFs), by doing so, the complexity of the time series signals can be reduced, and this facilitates the accurate prediction of the original signals. Then, in order to reduce the calculation cost of prediction models for subsequences, SampEn is used to measure the complexities of the IMFs, and the IMFs whose values of SampEn are lower than the average are aggregated into a new component. Finally, the LSTM with the hyperparameters optimized by the Bayesian optimization algorithm (BOA) is used to perform the prediction of each component. The prediction results of the original signals are reconstructed by synthesizing the predictions of all components. The proposed hybrid prediction model is utilized on the time series signals collected from an NPP. The results obtained show that the proposed approach can capture the characteristics of the signals and has better performance in prediction accuracy than other models.

Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem

With the growth of personalized demand and the continuous improvement in social productivity, the large-scale and few-variety centralized production model is gradually transitioning towards a personalized model of small batches and multiple varieties, which makes the manufacturing process of the job shop increasingly complex. Furthermore, disruptive events such as machinery failures and rush orders in the job shop increase the uncertainty and variability of the production environment. Traditional scheduling methods are usually based on fixed rules and heuristic algorithms, which are difficult to adapt to constantly changing production environments and demands. This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more effectively, this paper proposes a Reinforcement Learning (RL) optimization algorithm integrating long short-term memory (LSTM) neural network and proximal policy optimization (PPO). It can dynamically adjust scheduling strategies according to the changing production environment, achieving comprehensive status awareness of the job shop environment to make optimal scheduling decisions. First, a state-aware network framework based on LSTM-PPO is proposed to achieve real-time perception of job shop state changes. Then, the state and action space of the job shop are described within the context of the state-aware network framework. Finally, an experimental environment is established to verify the algorithm’s effectiveness. Training the LSTM-PPO algorithm makes it feasible to achieve better performance than other scheduling methods. By comparing the initial planning time with the actual completion time of the rescheduling decision under different dynamic disturbances, the efficiency of the proposed algorithm is verified for the dynamic JSP

Adaptive ECG Signal Denoising Algorithm Based on the Improved Whale Optimization Algorithm

Adaptive ECG Signal Denoising Algorithm Based on the Improved Whale Optimization Algorithm

Equipment control algorithm for cooling plant based on graph theory and path planning

This paper presents a novel path planning-based start-stop control algorithm for equipment in cooling plants designed to address the limitations of traditional serial start-stop control logic. The algorithm employs graph theory and path planning techniques to abstract the cooling plant schematic into a directed graph, enabling the selection of appropriate control paths and facilitating effective start-stop control decisions for the equipment. A key feature is the dynamic updating of the graph after each operation, which enables adaptive control target adjustments and prevents control blocking issues. Validation with real case scenarios demonstrates the effectiveness of the algorithm in automatic start-stop control, identifying equipment deviations, and handling faulty equipment switching. The algorithm also has the ability to handle some incorrectly turned on equipment, shutting down redundant equipment in the cooling plant system that is not in efficient operation. This algorithm shows the potential as an underlying execution algorithm of an advanced optimization algorithm to accelerate the implementation of advanced optimization algorithms.

Optimization Algorithm for Flattop Beam Shaping Using Analytically Initial Phase

Optimization Algorithm for Flattop Beam Shaping Using Analytically Initial Phase

Reliability Genetic Algorithms Optimization RGAO Approach Based FORM and Monte Carlo Simulation: Application for Bridge Structures

Reliability Genetic Algorithms Optimization RGAO Approach Based FORM and Monte Carlo Simulation: Application for Bridge Structures

Artificial Algae Algorithm for Clustering of Benchmark Datasets

The best solution found for a problem under specific circumstances is called optimization. Algorithms for optimization can make the best use of the information at their disposal. Numerous optimization algorithms have been created thus far by researchers, and most of these algorithms are based on the characteristics of naturally occurring biological organisms. Optimization algorithms have proven to be highly effective in numerous fields, including finance, engineering, and medical. Apart from these applications, they have also been employed in data mining techniques including clustering and classification. In many different domains, the clustering method is widely applied. Finding the optimum cluster centers is the most crucial step in the clustering process. In this study, the Artificial Algae Algorithm (AAA) is used to perform the clustering procedure by using 12 datasets that were taken from the UCI Machine Learning Repository. For every dataset, the squared distance values between the cluster centers and the data were computed in order to assess the effectiveness of AAA. The study evaluated AAA's performance against that of the ALO, DEA, MFO, PSO, TSA, and WOA algorithms. The clustering performance of AAA on benchmark datasets was measured to be better than the performance of other algorithms.

Comparative analysis of accelerated gradient algorithms for convex optimization: high and super resolution ODE approach

We investigate convex differentiable optimization and explore the temporal discretization of damped inertial dynamics driven by the gradient of the objective function. This leads to three accelerated gradient algorithms: Nesterov Accelerated Gradient (NAG), Ravine Accelerated Gradient (RAG), and (IGAHD). The latter was introduced by discretizing inertial dynamics with Hessian-driven damping to attenuate inherent oscillations in inertial methods. By analysing the high-resolution ODEs of order p = 0,1,2 for these algorithms, we gain insights into their similarities and differences. All three algorithms share the same low-resolution ODE of order 0, which is the dynamic proposed as a continuous surrogate for (NAG). To differentiate Nesterov from Ravine, we refine the comparison and demonstrate distinct high-resolution ODEs of order 2 in h (termed super-resolution). The corresponding Taylor expansions in h reveal matching terms of order 1 but differing terms of order 2. To the best of our knowledge, this result is completely new and emphasizes the need to avoid confusion between the Ravine and Nesterov methods in the literature. We present numerical experiments to illustrate our theoretical results. Performance profiles, measuring the number of iterations, indicate that (IGAHD) outperforms both (NAG) and (RAG) methods. (RAG) exhibits a slight advantage over (NAG) in terms of the average number of iterations. When considering CPU-time, both (RAG) and (NAG) outperform (IGAHD). All three algorithms exhibit similar behaviour when evaluating based on gradient norms.

A Test Report Optimization Method Fusing Reinforcement Learning and Genetic Algorithms

Filtering high-variability and high-severity defect reports from large test report databases is a challenging task in crowdtesting. Traditional optimization algorithms based on clustering and distance techniques have made progress but are limited by initial parameter settings and significantly decrease in efficiency with an increasing number of reports. To address this issue, this paper proposes a method that integrates reinforcement learning with genetic algorithms for crowdsourced testing report optimization, called Reinforcement Learning-based Genetic Algorithm for Crowdsourced Testing Report Optimization (RLGA). Its core goal is to identify distinct, high-severity defect reports from a large set. The method uses genetic algorithms to generate the optimal report selection sequence and adjusts the crossover probability (Pc) and mutation probability (Pm) dynamically with reinforcement learning based on the population’s average fitness, best fitness, and diversity. The reinforcement learning component uses a hybrid SARSA and Q-Learning strategy to update the Q-value table, allowing the algorithm to learn quickly in early iterations and expand the search space later to avoid local optima, thereby improving efficiency. To validate the RLGA method, this paper uses four public datasets and compares RLGA with six classic methods. The results indicate that RLGA outperforms BDDIV in terms of execution time and is less sensitive to the total number of test reports. In terms of optimization objectives, the test reports selected by RLGA have higher levels of defect severity and diversity than those selected by the random choice, BDDIV, and TSE methods. Regarding population diversity, RLGA effectively enhances the uniformity and diversity of individuals compared to random initialization. In terms of convergence speed, RLGA is superior to the GA, GA-SARSA, and GA-Q methods.

Design of Shape Forming Elements for Architected Composites via Bayesian Optimization and Genetic Algorithms: A Concept Evaluation

Design of Shape Forming Elements for Architected Composites via Bayesian Optimization and Genetic Algorithms: A Concept Evaluation

A Study of the Rainwater Pumping Station Operation Plan to Reduce River Water Level

Urban flood damage is caused by various and complex effects such as inland flood and external water flooding, so research on this is necessary. This study presented a rainwater pumping station operation technique that can suppress the rise in water levels in discharge rivers based on rainfall and pump operation scenarios in order to reduce inland water flooding and simultaneously consider external water levels. The research method used genetic algorithm (GA) optimization techniques to calculate the optimal operating level for each pump, and based on this, an operating scenario was created to minimize pump operation. In addition, after constructing a rainfall scenario based on the probability rainfall in Seoul, an optimal pump operation technique that can reduce the external water level while minimizing the inundation of domestic water was proposed. The results of the study showed that as the rainfall intensity increases, the flooding increases and the peak depth of the runoff increases. In addition, as the pumping scenario changes from 1 to 11, the number of pumping operations decreases, which reduces the drainage volume of the pumps, which increases the flooding.

Secure Routing Using Hybrid Sine Cosine Reptile Search and Remora Optimization Algorithm for WSN

Secure Routing Using Hybrid Sine Cosine Reptile Search and Remora Optimization Algorithm for WSN