Optimization Performance Research Articles

Enhanced Grey Wolf Optimization Algorithm for Mobile Robot Path Planning

In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. The HI-GWO algorithm introduces several key improvements to overcome these limitations and enhance performance. To enhance the population diversity and improve the initialization process, Gauss chaotic mapping is applied to generate the initial population. A novel nonlinear convergence factor is designed to strike a balance between global exploration and local exploitation capabilities. This factor enables the algorithm to effectively explore the solution space while exploiting the promising regions to refine the search. Furthermore, an adaptive position update strategy is developed by combining Levy flight and golden sine. This strategy enhances the algorithm’s solution accuracy, global search capability, and search speed. Levy flight allows longer jumps to explore distant regions, while golden sine guides the search towards the most promising areas. Extensive simulations on 16 standard benchmark functions demonstrate the effectiveness of the proposed HI-GWO algorithm. The results indicate that the HI-GWO algorithm outperforms other state-of-the-art intelligent algorithms in terms of optimization performance. Moreover, the performance of the HI-GWO algorithm is evaluated in a real-world path planning experiment, where a comparison with the traditional grey wolf algorithm and ant colony algorithm validates the superior efficiency of the improved algorithm. It exhibits excellent optimization ability, robust global search capability, high convergence accuracy, and enhanced robustness in diverse and complex scenarios. The proposed HI-GWO algorithm contributes to advancing the field of mobile robot path planning by providing a more effective and efficient optimization approach. Its improvements in convergence accuracy, iteration speed, and robustness make it a promising choice for various practical applications.

A dual surrogate assisted evolutionary algorithm based on parallel search for expensive multi/many-objective optimization

Numerous optimization problems in the real world involve multi-objective and computationally expensive simulations (i.e., expensive multi-objective optimization problems). This paper purposes a dual surrogates-assisted evolutionary algorithm (SAEA) based on parallel search, termed DSAEA-PS, for this issue. Approximation and classification are two main implementation forms of surrogate models, but the existing methods of expensive multi-objective optimization only apply one kind of them, and scarce works have paid attention to combining approximation and classification to improve the optimization performance. In the proposed algorithm, to enhance the prediction accuracy and reliability, both the approximation model and classification model are applied to cooperate to provide the quality and uncertainty information of candidate solutions. Meanwhile, the parallel search based on heterogeneous multi-objective evolutionary algorithms is introduced for better exploration of the decision space. In addition, combined with the strengthened dominance relation (SDR), a sampling strategy that comprehensively considers the quality of candidate solutions and their uncertainty information is proposed. Experimental results with five peer competitors on a set of widely-used benchmark problems demonstrate the ability of DSAEA-PS. Furthermore, DSAEA-PS is adopted for a five-objective blended-wing-body underwater glider design problem that involves time-consuming simulations of fluid dynamics and structural strength. A series of high-performance solutions obtained from DSAEA-PS verifies its effectiveness on engineering applications.

Tailored Iterated Greedy metaheuristic for a scheduling problem in metal 3D printing

This article contributes to the additive manufacturing-based production planning literature by developing a Mixed-Integer Linear Programming (MILP) formulation for the Identical Parallel 3D-Printing Machines Scheduling Problem considering batching, multiple build platforms of restricted sizes, and sequence-independent setup times. Besides, a customized metaheuristic, named the Tailored Iterated Greedy (TIG) Algorithm is developed to solve the new optimization problem. TIG's performance is evaluated through extensive numerical analysis and using a new testbed. It is shown that the customized computational mechanisms improve the optimization performance; statistical analysis is supportive of the significance of the resulting improvements. The developed mathematical model and optimization algorithm can be considered the basis for future developments in the optimization literature of additive manufacturing.

On-time and energy-saving train operation strategy based on improved AGA multi-objective optimization

On-time and energy-saving train operation is important for the sustainable development of rail transit. As for the problems of traction energy consumption and on-time arrival at stations faced by trains in rail transit, an optimization strategy of energy-saving speed curves of trains based on an improved adaptive genetic algorithm (AGA) was proposed in this paper. First, weight coefficients of operation time and energy consumption were designed through an analytic hierarchy process, and an optimization model that targets train operation time and energy consumption was established according to a basic train operation model with constraints such as speed limits and precise train stopping. Then, on-time and energy-saving speed curves of trains were generated based on the improved AGA. Finally, a simulation was carried out with actual rail transit lines. The results show that the proposed method has strong efficiency for energy conservation and better optimization performance than the simple genetic algorithm in solving train trajectory optimization problem.

Multi-strategy Improved Seagull Optimization Algorithm

Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.

Fault diagnosis research of laser gyroscope based on optimized-kernel extreme learning machine

Traditional diagnostic models for laser gyroscopes, widely which are commonly employed as high-precision angular velocity sensors in aerospace applications, often encounter challenges in terms of reliability and accuracy. These challenges arise from difficulties in feature extraction, high computational costs, and lengthy training times. In light of these challenges, the present study proposes a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method utilizes Wavelet Packet Decomposition (WPD) to efficiently extract features from the laser gyroscope signal, which are then used as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis. Afterward, we utilize the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for improved optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches. Additionally, it offers the advantages of shorter training time and increased precision.

Preventive maintenance optimization for key components of subway train bogie with consideration of failure risk

With the acceleration of urbanization, the number of urban rail transit trains has increased dramatically, and the economy and safety of subway trains have become important standards. As an essential subsystem of the subway train, the bogie has a long maintenance process and serious failure consequences. So, it is essential to formulate a reasonable maintenance strategy to ensure its operation. To solve the above problems, a preventive maintenance (PM) strategy for key components of train bogie is established with consideration of failure risk in this paper. Firstly, the failure risk factors of the bogie components are scored and weighted, and the failure risk cost model and PM cost model are established. Next, a Weibull distribution parameter estimation method for bogie components based on the improved beluga whale optimization algorithm (IBWO) is proposed, providing a theoretical basis for PM decision-making optimization. Then, the Lévy flight is introduced into particle swarm optimization to enhance the optimization performance of the model and obtain the optimal solution. Finally, the bogie key components of Nanning Metro line 1 are selected as a case study. The results show that the IBWO algorithm has strong applicability and feasibility, and can accurately calculate the Weibull parameter values of the bogie key components. Compared with the PM plan without considering the failure risk, the proposed method is more economical and safer, which can provide necessary theoretical support for the maintenance decision optimization of urban rail transit train components.

A novel heuristic algorithm for solving engineering optimization and real-world problems: People identity attributes-based information-learning search optimization

With the scale and dimension of engineering optimization and real-world problems increasing, it will be difficult to find the optimum solutions. This paper proposes a novel people identity attributes-based heuristic technology, named the People Identity Attributes-based Information-learning Search Optimization (ISO), inspired by people’s psychological assessment and learning behaviors for information resources based on the social status-based identity attribute and the self-survival demands-based identity attribute. For the former, a four-level information delivery mechanism is constructed through leader, manager, executor, and freelancer, emphasizing global optimization. For the latter, the staged transforming model based on random accumulation and directed induction behaviors are constructed according to self-survival demands, emphasizing global optimization and exploration. The dual identity attributes-based search strategy emphasizes psychological assessment and selection for the information loss, including the learning behavior for two kinds of information resources with different identity attributes, and the recovering behavior for the information loss by constructing the information resilience equation, which emphasizes local optimization and exploitation. This paper qualitatively analyzes the swarm behavior, search history, and the exploration and exploitation capabilities of ISO. The optimization performances are quantitatively analyzed for ISO and 9 competitive algorithms on 39 benchmark tests, including the convergence, solution accuracy, robustness, sensitivity, significance, statistical investigation-based Wilcoxon test and Friedman test. The scalability of ISO is investigated on CEC2017 (30Dim, 50Dim, 100Dim) and the latest CEC2022 (10Dim, 20Dim) suites. The results reveal that compared to other competitive algorithms, ISO possesses best computing performance with ranking the first in all competitors. In addition, the proposed ISO and 12 competitors consider 10 constraint engineering optimization problems and the real application of path planning with multiple obstacles, suggesting that ISO possesses significant optimization performance.

Bayesian damage identification of an unsymmetrical frame structure with an improved PSO algorithm

With the development of artificial intelligence technology, intelligent algorithms are widely utilized in the field of probabilistic model updating to solve the optimization problems. In this study, the opposition-based learning based on population centroid and boundary rebound strategy was incorporated into PSO to improve the optimization performance on structural damage detection, and the damage identification of an unsymmetrical frame based on Bayesian model updating with the improved PSO algorithm is proposed for the first time. Six benchmark functions were firstly employed to test the optimization performance of the improved PSO algorithm, which overcomes the premature convergence of the original PSO algorithm in which trapped particles often fall into local optimum. Then three damage scenarios of a laboratory unsymmetrical frame were utilized to validate the effectiveness of the proposed method in structural damage identification. The encouraging experimental results exhibit that the proposed method not only can successfully identify the damage locations and extents, but the associated uncertainties of the identified results can be quantified by calculating the posterior probability density functions of the uncertain model parameters. In addition, the effects of the quantities of measured information on the posterior uncertainties of the model parameters were investigated, and the uncertainty analysis results reveal that the posterior uncertainties associated with the identified model parameters are sensitive to the amount of measurement used in model updating. The proposed method is expected to help in damage identification and condition assessment of the practical engineering structures.

Analog circuit sizing based on Evolutionary Algorithms and deep learning

The use of machine learning-based techniques has expanded to many areas that require optimization. One of such area is Integrated Circuit (IC) design and sizing optimization, where we propose a new framework that combines Deep Neural Networks (DNNs) and Evolutionary Algorithm (EA) for analog IC sizing automation. Our optimization framework incorporates EA, numerical performance evaluation, and DNN models to optimize the analog circuit sizing process. The main steps of the proposed optimization procedure can be summarized as follows: Firstly, we use EA and numerical performance evaluation to increase efficiency and produce highly accurate results for global optimization. Secondly, we incorporate a local search to enhance the performance of global optimization, with DNN models being used to evaluate the performance of analog circuits during this phase. Lastly, we employ DNN model evaluations instead of numerical performance evaluation for ranking candidate designs, which significantly speeds up the local minimum search. These successive steps make our method original and stand out perfectly from existing procedures published in the literature. Our framework is deployed on industrial-scale circuits with strict design specifications. Experimental results demonstrate that it achieves better results and is two times faster in the design process compared to traditional global optimization methods that use sequential SPICE simulation calls for local optimization.

Novel strategies based on a gradient boosting regression tree predictor for dynamic multi-objective optimization

Prediction-based dynamic multiobjective optimization evolutionary algorithms have become one of the mainstream methods for solving dynamic multiobjective optimization problems. However, the unknown nonlinear relationships in sequential environments bring great challenge to the construction of prediction model and the inevitable error between the historical obtained solutions and the real pareto-optimal solutions troubles the prediction accuracy. In this paper, a gradient boosting regression tree based dynamic multiobjective optimization evolutionary algorithm is proposed, called MOEA/D-XGB. The time series solutions formed by subspace decomposition are trained in the XGBoost-based predictor to produce a high-quality initial population when environmental change occurs. And a novel population promotion strategy based on generalized additive model is proposed to improve the quality of historical obtained solutions. The performance comparisons with five state-of-the-art algorithms have shown that MOEA/D-XGB achieves best performance in 59 out of 70 experiments on MIGD and 60 out of 70 experiments on MHV, respectively, which demonstrates that the proposed design is capable of significantly improving the performance of dynamic multiobjective optimization.

Optimization performance of viscose rayon derived flexible porous carbon cloths in relation to their CO2 capture, CO2/CH4 separation and methanol adsorption

From the practical perspective, innovative porous carbon material is needed for gas adsorption and separation. Here, flexible porous carbon cloths are successfully prepared from viscose rayon cloth via controllable adjusted KOH activation. In order to optimize such flexible porous carbon cloths, the relationship between KOH activation condition and the porous carbon cloths property have been systematically studied. The effect factors on CO2 capture, CO2/CH4 separation and methanol adsorption have been analyzed in depth. Due to the largest narrow micropore volume (PV1 nm), ACC-240–800 exhibits the best CO2 uptake capacity of 4.11 mmol g−1 under ambient condition. Based on the ideal adsorbed solution theory model, the ACC-240–700 exhibits the highest selectivity of 3.73, 3.13 and 2.65 for CO2/CH4 mixing proportion of 30/70, 46/60 and 50/50, respectively, at 25 °C and 1 bar, owing to the synergetic result of O&N content and PV1 nm. The CO2/CH4 separation performance on ACC-240–700 is analyzed and confirmed by Qst calculation for CO2 and CH4, and dynamic breakthrough experiments with equimolar binary CO2/CH4 (50/50) mixture. The determining factors on methanol adsorption is mainly the porosity structure. The best methanol adsorption uptake of 12.87 mmol g−1 is observed on ACC-240–800 at 25 °C and 95 mbar, because of the largest values of SBET, Vmicro and PV1mn. The flexible porous carbon cloths provided in this work can act as effective adsorbent for carbon capture, low-quality nature gas upgrading and methanol adsorption in the industrial processes.

Comparative strategies of domestic and foreign ideological and political education in universities in the context of big data

Abstract The comparison and study of ideological and political education methods in Chinese and American universities can enable us to improve ideological and political education methods further. In this paper, we use the global optimization performance of the genetic algorithm to overcome the disadvantage that the K-means algorithm is sensitive to initial values and tends to converge only to local optimum points and propose an improved K-means algorithm based on the genetic algorithm. Meanwhile, the combination with the simulated annealing algorithm eliminates the problem that the K-means algorithm is sensitive to isolated points, makes the algorithm less susceptible to extreme data, and improves the analysis ability of the algorithm. In analyzing the content of ideological and political education in China and the United States, the main contents of ideological and political education in American colleges and universities include civic education, patriotic education and religious education. The first content of ideological and political education in Chinese schools is “mental health education”, accounting for 94.44%, and the second is “moral quality education”, accounting for 75.97%. The second is “ideal and belief education” and “education on life and values”, accounting for 73.23% and 72.70%, respectively. Therefore, American colleges and universities cultivate responsible citizens with the American spirit, while Chinese Civic Education cultivates socialist builders who serve the people.

Cubature Kalman Optimizer versus Teaching Learning Based Optimization: A Performance Comparison based on CEC2014 Test Suite

This paper compares a new Cubature Kalman Optimizer performance against the Teaching Learning Based Optimization in solving the CEC2014 test suite. The Cubature Kalman Optimizer is inspired by the estimation algorithm named Cubature Kalman filter, while the Teaching Learning Based Optimization is inspired by the teaching-learning process in a classroom. Both algorithms can be characterized as a parameter-less nature. Graphical analysis based on convergence curve shows that Cubature Kalman Optimizer has better exploration than Teaching Learning Based Optimization in the first half of the total iteration that make it able to find better solution. On the other hand, for boxplot, both algorithms show comparative based on consistency. Meanwhile, statistical analysis shows that the Cubature Kalman Optimizer algorithm is a promising approach compared to Teaching Learning Based Optimization.

Solving combined economic and emission dispatch problems using reinforcement learning-based adaptive differential evolution algorithm

Nowadays, economic and environmental concerns in production have become increasingly significant. To address these issues, the Combined Economic and Emission Dispatch (CEED) problem has been introduced to optimize the power generation process by considering fuel cost and emitted substances. However, due to the nonlinearity and nonconvexity of the objective function, the optimization of CEED remains a challenge. In this paper, we develop a Reinforcement Learning-based Adaptive Differential Evolution (RLADE) algorithm to enhance the optimization performance. The mutation strategy and crossover probability of RLADE are optimized using Reinforcement Learning (RL) to respectively ensure better convergence speed and searchability. Additionally, two modifications of RL, namely the adaptive population size-based state division and fitness-ranking-based reward mechanism, are proposed to improve the accuracy of state division and reward calculation in RL. The experiments conducted in this paper consider two objective formulation methods of CEED problems, namely the quadratic and cubic criterion functions. The mean values and standard deviations of the obtained solutions were utilized to assess the performance of RLADE, as well as other comparative algorithms, namely DE algorithm and two RL-based DE variants. The results clearly demonstrate that RLADE surpasses its counterparts with proportion of 100%, 85.7%, and 100% for the 6-unit and 11-unit quadratic CEED problems, as well as cubic criterion functions, in terms of both search accuracy and convergence ability. Furthermore, the significance of RLADE's superiority is confirmed through the Wilcoxon's signed rank test.

Cascading failure model and resilience enhancement scheme of space information networks

Space information networks provide global users with high-speed Internet services. The failure of a small number of satellites can trigger cascading failures that can bring down the network. Therefore, enhancing the network’s resilience under cascading failures is crucial. In this paper, we establish a cascading failure model for space information networks for the first time. The gravity model-based load calculation method, entity failure mechanisms, and new survivability measures are presented. The load unbalances of satellites are the leading cause of cascading failures, and the layout of the gateways determines the final load distribution. Therefore, a load-oriented gateway layout optimization model is developed. In order to obtain better optimization performance, a gateway layout solution algorithm based on the cuckoo search is given. Exhaustive simulations are carried out in a case satellite constellation, and the results show that the proposed approach is reasonable and able to enhance the resilience of the network under cascading failures.

Multi-objective firefly algorithm with adaptive region division

Aiming at the problems of single optimization strategy and poor comprehensive performance of MOFA, multi-objective firefly algorithm with adaptive region division is proposed in this paper. By leveraging the convergence index, our algorithm intelligently divides the dominant and non-dominant solution groups into three sub-regions, namely balance, exploration, and development areas, each with a distinct learning strategy that complements the strengths of fireflies. Specifically, fireflies in the balance area learn from global optimal particles with diversity to achieve a balanced exploration and development ability. Fireflies in the exploration area jointly learn from globally optimal particles with convergence and diversity, increasing the algorithm’s likelihood of discovering Pareto optimal solutions. Lastly, fireflies in the development area rapidly converge under the guidance of the globally optimal particle of convergence, thus improving the algorithm’s development ability. To further enhance the comprehensive optimization performance, we introduce a novel fusion index as an external archive update strategy that preserves solutions with superior convergence and diversity. Our experiments on 20 benchmark functions and a multi-objective optimization power flow example demonstrate that our algorithm outperforms other multi-objective optimization algorithms, highlighting its superior optimization performance.

A Linesearch-Based Algorithm for Topology and Generative Optimization of Switched Reluctance Machines

The switched reluctance machine is considered as the simplest machine in terms of the machine structure. The lack of permanent magnets on the rotating part enables the machine to operate at high speeds and reduces the thermal requirements of the machine. However, the double salient structure of the SRMs creates challenges such as high acoustic noise, vibrations and high machine torque ripples. Topology optimization is used in the literature to enhance the dynamic performance of the switched reluctance machines. This paper introduces a new ON/OFF optimization method based on the line search method to overcome the limitations of the conventional annealing-based ON/OFF optimization. The proposed method shows a faster convergence to optimal solutions than the conventional annealing-based ON/OFF method. The paper also compares the performance of the generative optimization and the topology optimization of a 6/14 switched reluctance machine with the proposed method and the conventional method. The two methods are applied to two different design domains of the machine for topology and generative optimization and the results are compared to the results of the annealing-based ON/OFF method. The results show the effectiveness of the newly proposed method.

Application of Gray Wolf Optimization Algorithm in Urban Electricity Load Forecasting Model

A combined prediction model based on long short-term memory neural network (LSTM) and convolutional neural network (CNN) is proposed in order to increase the prediction accuracy of short-term load. To address the issue that the gray wolf optimization (GWO) search process is prone to falling into local optimum. An improved grey wolf algorithm (IGWO) is proposed to update the convergence factor using the lower incomplete gamma function to improve the global optimization performance. The Dropout technique is used to improve the generalization ability of the model; the network layers are improved by increasing the initialization of weights; the model is trained using an adaptive moment estimation (Adam) optimizer, test data are input to the trained neural network model, and finally, the optimized model is used for prediction. The high prediction accuracy of the proposed method is demonstrated experimentally.

Data-Driven State Transition Algorithm for Fuzzy Chance-Constrained Dynamic Optimization.

Many actual industrial production processes are dynamic and uncertain. When uncertain information are described by subjective experience and experts' knowledge based on scanty or vague information, fuzzy uncertainty exists. Fuzzy chance-constrained dynamic programming are applicable to industrial production modeling accompanied by fuzzy uncertainty and dynamics, where constraints need not or cannot be completely satisfied. In this article, a fuzzy chance-constrained dynamic optimization (FCCDO) formulation on the basis of credibility theory is established, in which, the credibility is used to measure the fuzzy uncertainty level of constraints. To solve the FCCDO problem (FCCDOP), an improved fuzzy simulation technique based on Hammersley sequence sampling is raised to transform fuzzy chance constraints to their deterministic equivalents, and then a data-driven state transition algorithm (DDSTA) using deep neural networks (DNNs) is put forward to achieve a stable, global and robust optimization performance. Finally, the successful applications of the FCCDO method to industrial studies demonstrate its advantages.