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A metaheuristic approach to optimal morphology in reconfigurable tiling robots

COMPLEX & INTELLIGENT SYSTEMS 2023年第5期9卷 5831-5850页

作者： Kalimuthu, Manivannan Pathmakumar, Thejus Hayat, Abdullah Aamir Elara, Mohan Rajesh Wood, Kristin Lee Singapore Univ Technol & Design ROAR Lab Engn Prod Dev 8 Somapah Rd Singapore 487372 Singapore Univ Colorado Denver Coll Engn Design & Comp 1200 Larimer StSte 3034 Denver CO 80204 USA

Reconfigurable robots are suitable for cleaning applications due to their high flexibility and ability to change shape according to environmental needs. However, continuous change in morphology is not an energy-efficient approach, with the limited battery capacity. This paper presents a metaheuristic-based framework to identify the optimal morphology of a reconfigurable robot, aiming to maximize the area coverage and minimize the energy consumption in the given map. The proposed approach exploits three different metaheuristic algorithms, namely, SMPSO, NSGA-II, and MACO, to generate the optimal morphology for every unique layout of a two-dimensional grid map by considering the path-length as the energy consumption. The novel feature of our approach is the implementation of the footprint-based Complete Coverage Path Planning (CCPP) adaptable for all possible configurations of reconfigurable robots. We demonstrate the proposed method in simulations and experiments using a Tetris-inspired robot with four blocks named Smorphi, which can reconfigure into an infinite number of configurations by varying its hinge angle. The optimum morphologies were identified for three settings, i.e., 2D indoor map with obstacles and free spaces. The optimum morphology is compared with the standard Tetris shapes in the simulation and the real-world experiment. The results show that the proposed framework efficiently produces non-dominated solutions for choosing the optimal energy-efficient morphologies.

关键词： Reconfigurable robots Path planning Area coverage metaheuristics algorithms Design principles

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Exploring Chaotic Binarization Rules in the Binary Fox Optimizer for Complex Optimization Problems

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SN Computer Science 2025年第3期6.0卷 1-20页

作者： Cisternas-Caneo, Felipe Crawford, Broderick Soto, Ricardo Barrera-García, José Becerra-Rozas, Marcelo Giachetti, Giovanni Pontificia Universidad Católica de Valparaíso Valparaíso Chile Universidad Andres Bello Santiago Chile

Combinatorial optimization problems, characterized by their inherent complexity and exponential search spaces, present significant challenges in achieving optimal solutions. Metaheuristic algorithms have emerged as versatile tools to address these challenges, with advancements incorporating hybrid approaches such as chaotic maps to enhance their stochastic behavior. This study investigates the application of the Fox Optimizer (FOX) to the Set Covering Problem (SCP), an NP-hard problem with extensive practical relevance. By implementing standard and chaotic binary configurations of FOX and comparing them with Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), this research evaluates the impact of binarization rules and chaotic maps on algorithmic performance. Experimental results demonstrate that elitist configurations combined with chaotic maps consistently achieve high-quality solutions, offering accelerated convergence and enhanced exploration capabilities. Additionally, execution time analysis highlights the computational efficiency of chaotic configurations, emphasizing their suitability for large-scale problems. This work underscores the importance of aligning algorithmic strategies with problem characteristics and opens avenues for future exploration of chaotic maps and adaptive methodologies in combinatorial optimization. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binarization rules Chaotic maps Combinatorial optimization Fox optimizer metaheuristics algorithms

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Pre-uniform measures in the artificial intelligence era

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CURRENT PSYCHOLOGY 2025年第9期44卷 7919-7933页

作者： Dong, Yixiao Univ Denver Dept Res Methods & Informat Sci Denver CO 80208 USA

Psychological measures allow researchers and psychological professionals to capture and quantify the latent attributes of human beings. Artificial intelligence (AI) techniques have been integrated into measurement practices to handle massive computational loads, automate repetitive procedures, and optimize decision-making based on psychometric evidence. The emerging applications of AI may inspire positive shifts in measurement norms. This study suggests a shift towards focusing on the format of developed measures or measurement products. Traditionally, measurement tools comprise a set of items selected by test developers based on previous studies, intended for uniform use unless rigorous revalidation is performed for scale modifications. This research highlights the challenges of using conventional uniform measures and proposes using pre-uniform measures with AI-derived applications to create optimized measurement solutions tailored to individual needs. It presents three illustrative examples involving various psychological constructs (i.e., creative activity engagement, exposure to racism, and openness personality trait) in different measurement contexts. Each example demonstrates how to apply a pre-uniform measure with an AI-derived method-such as metaheuristic algorithms, machine learning regularization, or large language models-to address a specific measurement objective or need. Furthermore, this work summarized a four-step implementation framework and discussed practical implications and future directions for advancing pre-uniform measures using AI applications.

关键词： Artificial intelligence Pre-uniform measures Machine learning metaheuristics algorithms Large language models Measurement solution

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Optimizing a Transportation System Using metaheuristics Approaches (EGD/GA/ACO): A Forest Vehicle Routing Case Study

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World Journal of Engineering and Technology 2024年第1期12卷 141-157页

作者： Hossein Havaeji Thien-My Dao Tony Wong Mechanical Engineering Department école de technologie supérieure Montreal Canada

The large-scale optimization problem requires some optimization techniques, and the metaheuristics approach is highly useful for solving difficult optimization problems in practice. The purpose of the research is to optimize the transportation system with the help of this approach. We selected forest vehicle routing data as the case study to minimize the total cost and the distance of the forest transportation system. Matlab software helps us find the best solution for this case by applying three algorithms of metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Extended Great Deluge (EGD). The results show that GA, compared to ACO and EGD, provides the best solution for the cost and the length of our case study. EGD is the second preferred approach, and ACO offers the last solution.

关键词： metaheuristics algorithms Transportation Costs Optimization Approach Cost Minimisation

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Steam players count prediction using GRU optimized by modified PSO 32

Steam players count prediction using GRU optimized by modifi...

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32nd Telecommunications Forum, TELFOR 2024

作者： Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Bacanin, Nebojsa Dubljanin, Dobrivoje Malisic, Snezana Singidunum University Faculty of Technical Sciences Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia University of Belgrade School of Electrical Engineering Belgrade Serbia University of Belgrade Technical Faculty in Bor Bor Serbia

ISBN: (纸本)9798350391053

Accurate prediction of Steam players count is vital for efficient resource management, informed decision-making, and ability to maximize the potential of each game over its entire lifecycle. From the business point of view, this can directly affect profitability, player retention and strategic planning. This paper examines potential of GRU tuned by an improved version of renowned particle swarm optimization algorithm. The proposed model was placed into comparative simulations with other potent optimization techniques, and obtained experimental outcomes clearly pointed out the supreme performance of the suggested technique. © 2024 IEEE.

关键词： GRU metaheuristics algorithms players count prediction PSO Steam

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Optimization of laser-texturing process parameters of Ti6Al4V alloys using metaheuristic algorithms

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2024年

作者： Urgun, Satilmis Yigit, Halil Fidan, Sinan Sinmazcelik, Tamer Canel, Timur Kocaeli Univ Fac Aeronaut & Astronaut Dept Aviat Elect & Elect Kocaeli Turkiye Kocaeli Univ Fac Technol Dept Informat Syst Engn Kocaeli Turkiye Kocaeli Univ Fac Aeronaut & Astronaut Dept Airframe & Powerplant Maintenance Kocaeli Turkiye Kocaeli Univ Fac Engn Dept Mech Engn Kocaeli Turkiye Kocaeli Univ Fac Arts & Sci Dept Phys Kocaeli Turkiye

Manufacturing microscale textures can reduce friction and wear compared with smooth surfaces. In laser surface texturing, the resulting texture geometry is directly affected by laser parameters such as laser power, scanning speed and overlap rate. Therefore, finding the optimal parameter settings to improve the process performance is crucial. One of the biggest challenges encountered during the optimization stage is accurately determining the functional relationship between surface quality and process parameters. Another significant problem is solving the nonlinear equation system. This study aims to investigate the effect of laser-textured surfaces on the tribological behavior of titanium Ti6Al4V. Optimization of laser texture process parameters using metaheuristic algorithms is a hot topic. The first contribution of this study is the modeling of the contact angle, kurtosis and skewness quality parameters of Ti6Al4V titanium alloy based on the polynomial curve fitting method as a function of laser power, laser speed and overlap rate. Another significant contribution is to optimize the proposed nonlinear regression models using metaheuristic approaches such as genetic algorithm, particle swarm optimization, whale optimization algorithm, gray wolf optimization, equilibrium optimizer, teaching-learning-based optimization, student psychology-based optimization (SPBO), slime mould algorithm and social network search. The reason for using many metaheuristics is to test the effectiveness of different metaheuristic methods in solving this problem. Analysis of variance was performed to evaluate experimental data and compare the success of optimization methods. The results revealed that the proposed metaheuristic methods can be applied to the related problem. It has been observed that the methods give identical results. Among these methods, the SPBO method provided the best performance in the optimization of all laser output parameters.

关键词： Ti6Al4V alloy texture CO2 laser optimization metaheuristics algorithms

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Optimizing the Scheduling of Teaching Activities in a Faculty

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9554页

作者： Diallo, Francis Patrick Tudose, Catalin Natl Univ Sci & Technol POLITEHN Bucharest Fac Automat Control & Comp Bucharest 060042 Romania Luxoft Romania Bucharest 020335 Romania

To maximize resource usage, minimize disputes, and improve academic experience, professors must schedule teaching activities efficiently. This study provides an optimized automated schedule creation technique. The system generates schedules that aim to be conflict-free and efficient, utilizing evolutionary algorithms along with multi-objective optimization. Resource usage, scheduling problems, and faculty/student satisfaction are the goals of the research. The system optimizes scheduling based on room limitations, instructor availability, and student preferences. The project uses system design, model creation, algorithm implementation, and performance analysis to solve the difficult timetable-generating problem. This research should save administrators time, improve academic operations, and improve staff and student academic experiences. Scalability and flexibility allow the system to be used in multiple faculties and incorporate new limits and requirements. This paper presents a complete approach to faculty scheduling, including insights and recommendations for future study and application in educational institutions.

关键词： automatic timetable generation faculty scheduling metaheuristics algorithms resource utilization scheduling conflicts scalability Timefold

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A modified firefly algorithm for the inverse kinematics solutions of robotic manipulators

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INTEGRATED COMPUTER-AIDED ENGINEERING 2021年第3期28卷 257-275页

作者： Hernandez-Barragan, Jesus Lopez-Franco, Carlos Arana-Daniel, Nancy Alanis, Alma Y. Lopez-Franco, Adriana Univ Guadalajara Comp Sci Dept CUCEI Marcelino Garcia Barragan 1421 Guadalajara 44430 Jalisco Mexico

The inverse kinematics of robotic manipulators consists of finding a joint configuration to reach a desired end-effector pose. Since inverse kinematics is a complex non-linear problem with redundant solutions, sophisticated optimization techniques are often required to solve this problem;a possible solution can be found in metaheuristic algorithms. In this work, a modified version of the firefly algorithm for multimodal optimization is proposed to solve the inverse kinematics. This modified version can provide multiple joint configurations leading to the same end-effector pose, improving the classic firefly algorithm performance. Moreover, the proposed approach avoids singularities because it does not require any Jacobian matrix inversion, which is the main problem of conventional approaches. The proposed approach can be implemented in robotic manipulators composed of revolute or prismatic joints of n degrees of freedom considering joint limits constrains. Simulations with different robotic manipulators show the accuracy and robustness of the proposed approach. Additionally, non-parametric statistical tests are included to show that the proposed method has a statistically significant improvement over other multimodal optimization algorithms. Finally, real-time experiments on five degrees of freedom robotic manipulator illustrate the applicability of this approach.

关键词： Denavit-Hartenberg Inverse kinematics metaheuristics algorithms Multimodal optimization

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A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems

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APPLIED MATHEMATICAL MODELLING 2021年 95卷 339-360页

作者： Jui, Julakha Jahan Ahmad, Mohd Ashraf Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia

This paper presents a new hybrid identification algorithm called the Average Multi-Verse Optimizer and Sine Cosine Algorithm for identifying the continuous-time Hammerstein system. In this paper, two modifications were employed on the conventional Multi-Verse Optimizer. Our first modification was an average design parameter updating mechanism to solve the local optima issue. The second modification was the hybridization of Multi-Verse Optimizer with Sine Cosine Algorithm that will balance the exploration and exploitation processes and thus improve the poor searching capability. The proposed hybrid method was used for identifying the parameters of linear and nonlinear subsystems in the Hammerstein model using the given input and output data. A continuous-time linear subsystem was considered in this study, while there were a few methods that utilize such models. Furthermore, various nonlinear subsystems such as the quadratic and hyperbolic functions had been used in those experiments. The efficiency of the novel technique is illustrated using a numerical example and two real-world applications, which are a twin rotor system and a flexible manipulator system. The numerical and experimental results analysis were observed with respect to the convergence curve of the fitness function, the parameter deviation index, time-domain and frequency-domain responses of the identified model, and the Wilcoxon's rank test. The results showed that the proposed method was efficient in identifying both the Hammerstein model subsystems in terms of the quadratic output estimation error and parameter deviation index. The proposed hybrid method also achieved better performance in modeling of the twin-rotor system as well as the flexible manipulator system and provided better solutions compared to other optimization methods including Particle Swarm Optimizer, Grey Wolf Optimizer, Multi-Verse Optimizer and Sine Cosine Algorithm. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Hammerstein system identification metaheuristics algorithms Multi-verse optimizer Sine cosine algorithm Twin rotor system Flexible manipulator system

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Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm

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RESULTS IN ENGINEERING 2024年 24卷

作者： Islam, Muhammad Shafiqul Ahmad, Mohd Ashraf Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Al Sultan Abdullah Ctr Adv Ind Technol Pekan 26600 Pahang Malaysia

This research introduces the improved Archimedes optimization algorithm (IAOA) for data-driven modeling of continuous-time Hammerstein models with missing data. It addresses the limitations of the original Archimedes optimization algorithm (AOA) through two key modifications: rebalancing the exploration and exploitation phases and mitigating local optima trapping issues. The primary focus is on developing a novel data-driven approach for modeling continuous-time Hammerstein models, particularly in the presence of missing output data. Four levels of missing measurement data (5 %, 15 %, 35 %, and 50 %) were considered, with data points randomly replaced with zeros. Models were tested with both complete and missing output data to evaluate the robustness of the IAOA-based method. The proposed based method identified linear and nonlinear subsystem variables in a continuous-time Hammerstein model leveraging input and output data, validated through two practical experiments: a Twin Rotor System and an Electromechanical Positioning System. The performance was assessed by examining various factors, including the convergence curve of the fitness function and its statistical analysis, responses in the frequency and time domains, Wilcoxon's rank-sum test, and computational time. Across all experiments, the IAOA-based method demonstrated superior performance compared to AOA and other methods, including a hybrid approach combining the average multi-verse optimizer and sine cosine algorithm, particle swarm optimizer, the sine cosine algorithm, multi-verse optimizer and grey wolf optimizer. The findings showed that the proposed IAOA-based method delivered highly accurate and consistent solutions, proving it to be the most effective and reliable method compared to the others assessed.

关键词： Data-driven Hammerstein modeling metaheuristics algorithms Data-driven control with missing data Archimedes optimization algorithm Twin rotor system Electrical-mechanical positioning system

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