Single Optimization Algorithm Research Articles

Design optimization of automotive carbon fiber composite material floor laminate based on PSO-BFO algorithm

In response to the current challenges of low precision and efficiency in the optimization of composite material layups, the insufficient lightweight of automotive body floors, and the high cost of carbon fiber composites, this study introduces an optimized design method for carbon fiber composite flooring layups. Based on implicit parametric technology, a hybrid PSO-BFO (Particle Swarm Optimization-Bacterial Foraging Optimization) algorithm is employed. This approach achieves an integrated optimization of materials, processes, and structures, thereby balancing and reducing costs. The SFE-CONCEPT is utilized to establish an implicit parameterization model for the body floor, which is validated through experiments and finite element simulation analysis. Concept design and modeling of the carbon fiber composite material floor laminate are performed. Continuous variable optimization is employed to determine the thickness, tile shape, and number of layers for the front, middle, and rear floors. A continuous variable discretization rounding strategy is used to obtain the discrete layer numbers for each laminate orientation of the composite material floor. The continuous fiber lamination strategy is applied to create different shared lamination regions. The PSO-BFO hybrid optimization method is proposed to optimize the lamination sequence as a multi-objective optimization, addressing the challenges of discrete lamination sequence, explosive combinations, and multiple variables in the optimization design of carbon fiber composite material floor laminates. The optimization results demonstrate improvements of 34.4% in floor quality M, 6.0% in static bending stiffness BS, and 5.3% in lightweight coefficient QLX using the proposed PSO-BFO method. PSO-BFO and PSO-GA (Particle Swarm Optimization-Genetic Algorithm) methods are more capable of obtaining global optimal solutions for complex optimization problems than single optimization algorithms. Still, the results obtained by the PSO-BFO method are more balanced.

Indoor Infrared Sensor Layout Optimization for Elderly Monitoring Based on Fused Genetic Gray Wolf Optimization (FGGWO) Algorithm.

With the increasing aging of the global population, the efficiency and accuracy of the elderly monitoring system become crucial. In this paper, a sensor layout optimization method, the Fusion Genetic Gray Wolf Optimization (FGGWO) algorithm, is proposed which utilizes the global search capability of Genetic Algorithm (GA) and the local search capability of Gray Wolf Optimization algorithm (GWO) to improve the efficiency and accuracy of the sensor layout in elderly monitoring systems. It does so by optimizing the indoor infrared sensor layout in the elderly monitoring system to improve the efficiency and coverage of the sensor layout in the elderly monitoring system. Test results show that the FGGWO algorithm is superior to the single optimization algorithm in monitoring coverage, accuracy, and system efficiency. In addition, the algorithm is able to effectively avoid the local optimum problem commonly found in traditional methods and to reduce the number of sensors used, while maintaining high monitoring accuracy. The flexibility and adaptability of the algorithm bode well for its potential application in a wide range of intelligent surveillance scenarios. Future research will explore how deep learning techniques can be integrated into the FGGWO algorithm to further enhance the system's adaptive and real-time response capabilities.

Operator-free HPLC automated method development guided by Bayesian optimization

Automated, closed-loop HPLC method optimization using single and multi-objective Bayesian optimization algorithms.

Research on Path Planning for Unmanned Surface Vessels Based on AIS Data

To address the issues of poor endpoint convergence and suboptimal path quality in global path planning for unmanned surface vessels (USVs), this paper proposes an endpoint-convergence oriented improved genetic algorithm based on AIS data. Firstly, utilizing a genetic algorithm with an endpoint-convergence objective function, a set of paths with strong endpoint convergence is selected. Secondly, to overcome the limitations of a single optimization algorithm, a multi-objective path planning approach is employed using a genetic algorithm, thereby improving the distance and trajectory smoothness in global path planning for unmanned ships. Finally, the planned paths are evaluated by comparing them with the real paths from AIS data. The simulation results demonstrate that the proposed method outperforms other traditional algorithms in terms of average path turn count and turn angle, reducing them by an average of 35.41% and 35.72%, respectively. Moreover, the proposed method exhibits the smallest error compared to the real trajectories, with an average reduction of 18.26%. These results validate the effectiveness and rationality of the proposed approach in improving path quality, reducing path turn count, and achieving better alignment with actual trajectories.

Non-gradient-based simultaneous strategy for bifurcation parameter detection

This paper proposes a non-gradient-based simultaneous strategy for detecting bifurcation parameters in dynamical systems. The proposed method uses a single optimization algorithm with two interdependent objective functions, one for a periodic condition and the other for a bifurcation condition. In addition, a novel approach to easily detect a two-parameter bifurcation diagram is presented. A comparison between two population-based search algorithms, particle swarm optimization (PSO) and differential evolution (DE), shows that DE outperforms PSO in bifurcation parameter search. This work provides the first simultaneous strategy capable of directly detecting a two-parameter bifurcation diagram without requiring careful initialization, regardless of the stability of the periodic point. The proposed algorithm is simple, easy to understand, and computationally efficient, making it a powerful and widely applicable tool for accurate bifurcation parameter detection.

A framework for enhancing industrial soft sensor learning models

Refinery industrial processes are very complex with nonlinear dynamics resulting from varying feedstock characteristics and also from changes in product prioritization. Along these processes, there are key properties of intermediate compounds that must be monitored and controlled since they directly affect the quality of the end products commercialized by these manufacturers. However, most of these properties can only be measured through time-consuming and expensive laboratory analysis, which is impossible to obtain in high frequencies, as required to properly monitor them. In this sense, developing soft sensors is the most common way to obtain high-frequency estimations for these measurements, helping advanced control systems to establish the correct setpoints for temperatures, pressures, and other sensors along the refining process, controlling the quality of end products. Since the amount of labeled data is scarce, most academic research has focused on employing semi- supervised learning strategies to develop machine learning (ML) models as soft sensors. Our research, on the other hand, goes in another direction. We aim to elaborate a framework that leverages the knowledge of domain experts and employs data augmentation techniques to build an enhanced fully labeled dataset that could be fed to any supervised ML algorithm to generate a quality soft sensor. We applied our framework together with Automated ML to train a model capable of predicting a specific key property associated with the production of Naphtha compounds in a refinery: the ASTM 95% distillation temperature of the Heavy Naphtha. Although our framework is model agnostic, we opted by using Automated ML for the optimization strategy, since it applies a diverse set of models to the dataset, reducing the bias of utilizing a single optimization algorithm. We evaluated the proposed framework on a case study carried out in an industrial refinery in Brazil, where the previous model in production for estimating the ASTM 95% distillation temperature of the Heavy Naphtha was based entirely on the physicochemical knowledge of the process. By adopting our framework with Automated ML, we were capable of improving the R2 score by 120%. The resulting ML model is currently operating in real-time inside the refinery, leading to significant economic gains.

A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations.

This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.

Denoising method of machine tool vibration signal based on variational mode decomposition and Whale-Tabu optimization algorithm

The noise from other sources is inevitably mixed in the vibration information of CNC machine tools obtained using the sensors. In this work, a de-noising method based on joint analysis is proposed. The variational mode decomposition (VMD), correlation analysis (CA), and wavelet threshold (WT) denoising are used to denoise the original signal. First, VMD decomposes noisy signals into multiple intrinsic mode functions (IMFs). The penalty factor and decomposition level of VMD parameters are selected by the optimization algorithm by combining the whale optimization algorithm (WOA) and tabu search (TS). The minimum permutation entropy of IMF is used as the fitness function of the proposed fusion algorithm. Then, the IMF is divided into three categories by using the cross-correlation number. They include the pure components, signals containing noise, and complete noise components. Then, the WT method is used to further denoise the signals, and signal reconstruction is performed with the pure component to obtain the denoised signal. This joint analysis denoising method is named TS-WOA-VMD-CA-WT. The simulation results show that the fusion optimization algorithm proposed in this work has better performance as compared to the single optimization algorithm. It performs effectively when applied to the actual machine tool vibration signal denoising. Therefore, the proposed TS-WOA-VMD-CA-WT method is superior to other existing denoising techniques and has good generality, which is expected to be popularized and applied more widely.

3D Path Optimisation of Unmanned Aerial Vehicles Using Q Learning-Controlled GWO-AOA

Unmanned Aerial Vehicles (UAVs) or drones introduced for military applications are gaining popularity in several other fields as well such as security and surveillance, due to their ability to perform repetitive and tedious tasks in hazardous environments. Their increased demand created the requirement for enabling the UAVs to traverse independently through the Three Dimensional (3D) flight environment consisting of various obstacles which have been efficiently addressed by metaheuristics in past literature. However, not a single optimization algorithms can solve all kind of optimization problem effectively. Therefore, there is dire need to integrate metaheuristic for general acceptability. To address this issue, in this paper, a novel reinforcement learning controlled Grey Wolf Optimisation-Archimedes Optimisation Algorithm (QGA) has been exhaustively introduced and exhaustively validated firstly on 22 benchmark functions and then, utilized to obtain the optimum flyable path without collision for UAVs in three dimensional environment. The performance of the developed QGA has been compared against the various metaheuristics. The simulation experimental results reveal that the QGA algorithm acquire a feasible and effective flyable path more efficiently in complicated environment.

Enhancing Feature Selection Accuracy using Butterfly and Lion Optimization Algorithm with Specific Reference to Psychiatric Disorder Detection & Diagnosis

As the complexity of medical computing increases the use of intelligent methods based on methods of soft computing also increases. During current decade this intelligent computing involves various meta-heuristic algorithms for Optimization. Many new meta-heuristic algorithms are proposed in last few years. The dimension of this data has also wide. Feature selection processes play an important role in these types of wide data. In intelligent computation feature selection is important phase after the pre-processing phase. The success of any model depends on how better optimization algorithms is used. Sometime single optimization algorithms are not enough in order to produce better result. In this paper meta-heuristic algorithm like butterfly optimization algorithm and enhanced lion optimization algorithm are used to show better accuracy in feature selection. The study focuses on nature based integrated meta-heuristic algorithm like Butterfly Optimization and lion-based optimization. Also, in this paper various other Optimization algorithms are analyzed. The study shows how integrated methods are useful to enhance the accuracy of any computing model to solve Complex problems. Here experimental result has shown by proposing and hybrid model for two major psychiatric disorders one is known as autism spectrum and second one is Parkinson's disease.

Optimization of double-layer perforated breakwater based on risk assessment of uncertainties

In this work, the optimal dimensions of a double-layer perforated breakwater were determined by considering the risk of uncertainties in marine conditions, including wave height and wavelength. To do so, the CVaR-NINP technique combines Conditional Value-at-Risk (CVaR) and Nonlinear Interval Number Programming (NINP), which are useful in dealing with discrete interval uncertainties and probabilistic. Based on experimental data, two Extreme Learning Machine (ELM) models were developed to simulate the hydraulic behavior of the breakwater. To increase accuracy and performance, the parameters of these two models were optimized using single and multi-objective optimization algorithms. The obtained results indicate that the non-dominated sorting genetic algorithm (NSGA-II) exhibited better performance in optimizing ELM. Subsequently, optimized ELM, which better modeled the hydraulic performance of perforated breakwater, was selected to link to the NSGA-III algorithm to determine the trade-off between the defined objective functions based on the CVaR-NINP technique, namely, minimize CVaR of (Ct), minimize the radius of the interval number of (Ct), minimize CVaR of (Cr), minimize the radius of the interval number of (Cr). Pareto optimal solutions, obtained from NSGA-III, using the multi-attribute decision-making (MADM) method, also called the R-method, were ranked and applied to select the best solutions.

An ensemble multi-swarm teaching–learning-based optimization algorithm for function optimization and image segmentation

Intelligent optimization algorithms are widely utilized to deal with complex optimization problems in various areas. However, a single intelligent optimization algorithm cannot handle well more and more complex optimization problems. The ensemble strategy can integrate several different operators and algorithms by using some appropriate strategies and maybe obtain better optimization performance. In this paper, a new ensemble multi-swarm method based on teaching–learning-based optimization (EMTLBO) was proposed by integrating three different algorithms including the original teaching–learning-based optimization algorithm, its variant with neighborhood search and the variant with differential evolution. In EMTLBO, a new evaluating mechanism based on the fitness-based and diversity-based metrics (FDEM) for each sub-swarm was proposed to evaluate the optimization performance after a continuous generation interval. Moreover, an algorithm matching mechanism based on ranking for sub-swarms (AMM) is adapted to re-divide the population into three sub-swarms and match a suitable algorithm for each sub-swarm so as to increase the whole optimization performance. Furthermore, the experimental results on CEC2014 and CEC2017 test suits verify the feasibility and optimization performance of EMTLBO. Finally, the proposed algorithm is extended to optimize the segmentation thresholds of images and the segmentation performances on different benchmark images show that EMTLBO has good performance in most cases.

An ant colony genetic fusion routing algorithm based on soft define network

AbstractAiming at the problem that there are many paths in data forwarding in soft define network (SDN) network, and the optimal path is difficult to find, combined with the advantages of ant colony algorithm and Genetic algorithm (GA), a routing control strategy based on the ant colony genetic fusion algorithm is proposed. This algorithm absorbs the advantages of high speed in the early stage of GA search and high efficiency in the late stage of ant colony algorithm search; the improved ant colony genetic fusion algorithm is applied to the SDN routing process for simulation tests. The simulation experiment shows that the throughput and link utilization of the proposed algorithm are about 30% higher than that of the single optimal particle swarm optimization algorithm.

A self-adapting algorithm for many-objective optimization

An increase in computing power available from modern processors and GPUs has allowed more complex design optimization problems to be solved. This increase in complexity typically results in a difficult optimization problem with a highly non-linear objective function topology. In this work a robust optimization algorithm is presented capable of efficiently traversing higher-dimensional objective function space to find Pareto optimal solutions. It is well known that no single optimization algorithm performs best for all problems. Therefore, the developed method, a many-objective hybrid optimizer (MOHO), uses five constitutive algorithms and actively switches among them throughout the optimization process to accelerate the convergence, avoid local minima and arrive at a diverse set of optimal designs. MOHO monitors the progress made by each of the five algorithms and allows the best performing algorithm more attempts at finding the optimum. The MOHO algorithm was tested on 13 unconstrained and five constrained analytical test problems, of up to 15 objectives, from the DTLZ and WFG test suite. The MOHO algorithm performed, on average, better than the five constitutive algorithms in 52% of the unconstrained DTLZ+WFG problems and in 70% of the constrained DTLZ test problems.

Critical factors for the use of machine learning to predict lake surface water temperature

Models based on Machine Learning (ML) are pervading all fields of science and practical applications, including the problem of forecasting water temperature in lakes, a crucial variable for ecosystems and a proxy of climate change. Here, we review the most used ML algorithms in this field and highlight some physical constraints that should be carefully considered when adopting a black-box approach. To illustrate them, we refer to an artificial case study representing a temperate lake simulated by means of a physically based model, for which we take full control of input and output variables, and restrict the analysis to lake surface water temperature (LSWT). Three main factors are relevant for a successful prediction of LSWT by means of ML models: the choice of the predictors (mostly, meteorological variables), their pre-processing (we tested three approaches), and the specific ML algorithm (nine different algorithms). We show that selecting the suitable physical inputs plays the most important role. In our case study, which is the product of a numerical model and not a real lake, the minimum amount of information that is needed to obtain acceptable results is to consider air temperature (AT) and day of the year. The use of additional predictors does not substantially improve the performances (the relative improvement of RMSE was 7.75% for the test data set). We also demonstrate that better results than the normal case are obtained by either pre-processing air temperature data averaging them over a time window or including values from previous days as inputs in the model. Considering the recent history of the forcing (AT) allows one to comply with the physical fact that the large water mass makes lakes acting as “filters” in their thermal response (thus, influenced also by AT from previous days), which changes depending on the lake’s depth. Eventually, we did not find a definite answer about a single optimal ML algorithm when using the same inputs (although artificial neural network had slightly better results), suggesting that the insight into the physical dynamics is still the most important factor for a successful exploitation of ML.

Accommodation of Multishaft Gas Turbine Switching Control Gain Tuning Problem to Inlet Guide Vane Position

Abstract This article aims to discuss the influence of compressor inlet guide vane (IGV) position on gas turbine switching control system gain tuning problem. The distinction between IGV and normally reckoned working conditions is differentiated, and an improved double-layer linear parameter varying (LPV) model is proposed to estimate the protected parameters under various IGV positions. Controller gain tuning is conducted with single and multi-objective intellectual optimization algorithms. Simulation results reveal that normally used multi-objective optimization procedure is unnecessary and time-consuming. While with the comprehensive indicator introduced in this paper, the calculation burden can be greatly eased. This improvement is especially advantageous when tuning work is carried out under multiple IGV positions.

Chameleon Swarm Algorithm Assisted Optimization of U-Slot Patch Antenna for Quad-Band Applications

Existing optimization algorithms are insufficient in the face of problems with difficult measurement functions as well as a large number of design parameters. Therefore, to achieve such challenging applications, the Chameleon swarm algorithm and its variants, which belong to the family of meta-heuristic algorithms that have not been used in any antenna optimization study before, are used together with 3 different objective functions fitted from mathematical models. Then, a U-slot antenna application with 12 different variable design parameters with resonance frequencies of 3.5 GHz, 3.7 GHz, 5.2 GHz and 5.8 GHz is considered as a multidimensional and single-objective optimization problem. In this study, first of all, the success of the algorithm is reinforced by comparing the performance with other commonly used single and multi-objective optimization algorithms. In addition, the results obtained with different population parameters, weight coefficients, objective functions and variant models were compared. All these processes are compared within themselves, and the antenna results of the most successful result are displayed as 3D electromagnetic simulation. The results show that the optimization processes proposed for an antenna designer are a safe, practical and efficient solution for multidimensional and single-objective antenna optimization applications. In addition, any optimization problem with a large number of variable design parameters can undoubtedly be adapted to the Chameleon swarm algorithm.

Hybrid identification method of coupled viscoplastic-damage constitutive parameters based on BP neural network and genetic algorithm

The constitutive model based on the theoretical framework of coupled viscoplastic-damage involves calibration of multiple and high coupling parameters. The inverse calibration by genetic algorithm (GA) with global search ability has some challenges as the dependence on the selection of the initial population, massive computation, and convergence inconsistency. To obtain statistical knowledge from state data to avoid subjective experience, a hybrid identification method based on the BP neural network and GA is proposed. A coupled viscoplastic-damage constitutive model based on the thermal deformation and microstructure evolution is established. The parameters in the model are determined based on the hybrid identification method. Two types of aluminum alloy sheets are selected to test the generalization, and mean square errors (RMSE) are 2.46 and 4.89, respectively. The results indicate that this method has higher accuracy than the inverse calibration method based on single optimization algorithm.

Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer

Wind energy is one of the most promising solutions to energy crisis and environmental pollution, so it is being developed rapidly. Wind turbine power curves (WTPCs) play an important role in wind energy assessment, turbine condition monitoring, and power grid dispatching. However, there are two challenges in WTPC modeling: model selection and parameter optimization. Many parametric and non-parametric models have been developed to characterize WTPCs, but none can always perform the best due to the complex wind regimes. In this paper, considering the simple structure and interpretability of model parameters, a set of parametric WTPC models is constructed to adapt to the variability of wind regimes, and the optimal candidate will be selected from the set according to their performances. As to the process of parameter optimization, a novel loss function, which considers the asymmetric error characteristic of WTPC modeling, is proposed, and a hybrid intelligent optimization method named GWO-BSA, which makes full use of the advantages of grey wolf optimizer and backtracking search algorithm, is designed. Finally, a novel WTPC modeling strategy, which combines the candidate model set, error characteristic-based loss function, and GWO-BSA, is proposed to obtain better power curves. Experimental results show that (1) GWO-BSA shows faster convergence speed and higher optimization accuracy than single optimization algorithms; (2) the proposed error characteristic-based loss function has better performance than the commonly used symmetric loss functions; and (3) compared with some popular artificial intelligence-based models, the designed WTPC modeling strategy produces better WTPCs under different wind regimes.

A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction. The reason is that it is indeed challenging to incorporate effectively multiple regularizations in a single MRF optimization algorithm. As a result most of these methods are not robust to noise especially when the sequence length is short. In this paper, we propose a family of new methods where spatial and low-rank regularizations, in addition to the Bloch manifold regularization, are applied on the images. We show on digital phantom and NIST phantom scans, as well as volunteer scans that the proposed methods bring significant improvement in the quality of the estimated tissue maps.