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Algorithms
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Metaheuristic Algorithms in Optimal Design of Engineering Problems
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Metaheuristic Algorithms in Optimal Design of Engineering Problems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 July 2024) | Viewed by 22833

Special Issue Editors

Dr. Łukasz Knypiński

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Guest Editor
Faculty of Control, Robotics and Electrical Engineering, Poznan University of Technology, 60-965 Poznań, Poland
Interests: heuristic optimization algorithms; constrained optimization; permanent magnet machines; hybrid optimization algorithms
Dr. Ramesh Devarapalli

E-Mail Website
Guest Editor
Institute of Chemical Technology, IndianOil Odisha Campus, Bhubaneswar, India
Interests: renewable energy sources; artificial intelligence and optimization algorithms; hydrogen energy-fuel cells
Dr. Marcin Kaminski

E-Mail Website
Guest Editor
Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: neural networks; adaptive control; fuzzy logic; optimization of control structures using nature-inspired techniques; hardware implementations (FPGA, DSP, microcontrollers) of algorithms based on artificial intelligence; electrical drives; machine learning; digital image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Metaheuristic algorithms are a class of optimization algorithms that can solve complex engineering design problems by finding near-optimal solutions efficiently. These algorithms are based on iterative searches of the permissible space, using various heuristics and strategies to explore the design space and refine solutions over time. Metaheuristics are used extensively in many engineering disciplines, including mechanical, civil, electrical, and aerospace engineering, to optimize the performance of systems, components, and processes.

Some of the most popular metaheuristic algorithms used in engineering design include genetic algorithms, particle swarm optimization, simulated annealing, and ant colony optimization. These algorithms can efficiently solve complex optimization problems with many design variables and constraints, allowing engineers to quickly and accurately identify optimal solutions. 

The use of metaheuristic algorithms in engineering design has several advantages, including the ability to handle nonlinear and non-convex optimization problems, the ability to find near-optimal solutions in a reasonable amount of time, and the ability to handle large-scale optimization problems. However, the effectiveness of these algorithms depends on several factors, such as the quality of the initial design, the choice of optimization algorithm, and the selection of appropriate optimization parameters. 

Overall, metaheuristic algorithms are an important tool for engineers to optimize the design of complex engineering systems and processes. By combining advanced algorithms with domain-specific knowledge and expertise, engineers can design systems that meet performance, cost, and other constraints while achieving optimal outcomes.

Dr. Łukasz Knypiński
Dr. Ramesh Devarapalli
Dr. Marcin Kaminski
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • metaheuristic optimization algorithms
  • genetic algorithms
  • particle swarm optimization
  • simulated annealing
  • engineering design

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Published Papers (10 papers)

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Editorial

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5 pages, 188 KiB  
Editorial
Metaheuristic Algorithms in Optimal Design of Engineering Problems
by Łukasz Knypiński, Ramesh Devarapalli and Marcin Kamiński
Algorithms 2024, 17(11), 522; https://doi.org/10.3390/a17110522 - 14 Nov 2024
Cited by 1 | Viewed by 1110
Abstract
Metaheuristic optimization algorithms (MOAs) are widely used to optimize the design process of engineering problems [...] Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)

Research

Jump to: Editorial

25 pages, 7993 KiB  
Article
Multi-Objective Majority–Minority Cellular Automata Algorithm for Global and Engineering Design Optimization
by Juan Carlos Seck-Tuoh-Mora, Ulises Hernandez-Hurtado, Joselito Medina-Marín, Norberto Hernández-Romero and Liliana Lizárraga-Mendiola
Algorithms 2024, 17(10), 433; https://doi.org/10.3390/a17100433 - 27 Sep 2024
Viewed by 1245
Abstract
When dealing with complex models in real situations, many optimization problems require the use of more than one objective function to adequately represent the relevant characteristics of the system under consideration. Multi-objective optimization algorithms that can deal with several objective functions are necessary [...] Read more.
When dealing with complex models in real situations, many optimization problems require the use of more than one objective function to adequately represent the relevant characteristics of the system under consideration. Multi-objective optimization algorithms that can deal with several objective functions are necessary in order to obtain reasonable results within an adequate processing time. This paper presents the multi-objective version of a recent metaheuristic algorithm that optimizes a single objective function, known as the Majority–minority Cellular Automata Algorithm (MmCAA), inspired by cellular automata operations. The algorithm presented here is known as the Multi-objective Majority–minority Cellular Automata Algorithm (MOMmCAA). The MOMmCAA adds repository management and multi-objective search space density control to complement the performance of the MmCAA and make it capable of optimizing multi-objective problems. To evaluate the performance of the MOMmCAA, results on benchmark test sets (DTLZ, quadratic, and CEC-2020) and real-world engineering design problems were compared against other multi-objective algorithms recognized for their performance (MOLAPO, GS, MOPSO, NSGA-II, and MNMA). The results obtained in this work show that the MOMmCA achieves comparable performance with the other metaheuristic methods, demonstrating its competitiveness for use in multi-objective problems. The MOMmCAA was implemented in MATLAB and its source code can be consulted in GitHub. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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32 pages, 8006 KiB  
Article
Application of Particle Swarm Optimization to a Hybrid H∞/Sliding Mode Controller Design for the Triple Inverted Pendulum System
by Yamama A. Shafeek and Hazem I. Ali
Algorithms 2024, 17(10), 427; https://doi.org/10.3390/a17100427 - 24 Sep 2024
Cited by 1 | Viewed by 1345
Abstract
The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro–mechanical devices that [...] Read more.
The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro–mechanical devices that are constructed in the semblance of humans and have the ability to sense their environment and take actions accordingly. The control of humanoids is broken down to the following: sensing and perception, path planning, decision making, joint driving, stability and balance. In order to establish and develop control strategies for joint driving, stability and balance, the triple inverted pendulum is used as a benchmark. As the presence of uncertainty is inevitable in this system, the need to develop a robust controller arises. The robustness is often achieved at the expense of performance. Hence, the controller design has to be optimized based on the resultant control system’s performance and the required torque. Particle Swarm Optimization (PSO) is an excellent algorithm in finding global optima, and it can be of great help in automatic tuning of the controller design. This paper presents a hybrid H∞/sliding mode controller optimized by the PSO algorithm to control the triple inverted pendulum system. The developed control system is tested by applying it to the nominal, perturbed by parameter variation, perturbed by external disturbance, and perturbed by measurement noise system. The average error in all cases is 0.053 deg and the steady controller effort range is from 0.13 to 0.621 N.m with respect to amplitude. The system’s robustness is provided by the hybrid H∞/sliding mode controller and the system’s performance and efficiency enhancement are provided by optimization. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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22 pages, 1864 KiB  
Article
Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor
by David M. Munciño, Emily A. Damian-Ramírez, Mayra Cruz-Fernández, Luis A. Montoya-Santiyanes and Juvenal Rodríguez-Reséndiz
Algorithms 2024, 17(5), 209; https://doi.org/10.3390/a17050209 - 11 May 2024
Cited by 5 | Viewed by 2053
Abstract
Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and [...] Read more.
Direct current motors are widely used in industry applications, and it has become necessary to carry out studies and experiments for their optimization. In this manuscript, a comparison between heuristic and metaheuristic algorithms is presented, specifically, the Steiglitz–McBride, Jaya, Genetic Algorithm (GA), and Grey Wolf Optimizer (GWO) algorithms. They were used to estimate the parameters of a dynamic model that approximates the actual responses of current and angular velocity of a DC motor. The inverse of the Euclidean distance between the current and velocity errors was defined as the fitness function for the metaheuristic algorithms. For a more comprehensive comparison between algorithms, other indicators such as mean squared error (MSE), standard deviation, computation time, and key points of the current and velocity responses were used. Simulations were performed with MATLAB/Simulink 2010 using the estimated parameters and compared to the experiments. The results showed that Steiglitz–McBride and GWO are better parametric estimators, performing better than Jaya and GA in real signals and nominal parameters. Indicators say that GWO is more accurate for parametric estimation, with an average MSE of 0.43%, but it requires a high computational cost. On the contrary, Steiglitz–McBride performed with an average MSE of 3.32% but required a much lower computational cost. The GWO presented an error of 1% in the dynamic response using the corresponding indicators. If a more accurate parametric estimation is required, it is recommended to use GWO; however, the heuristic algorithm performed better overall. The performance of the algorithms presented in this paper may change if different error functions are used. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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19 pages, 2588 KiB  
Article
An Enhanced Particle Swarm Optimization (PSO) Algorithm Employing Quasi-Random Numbers
by Shiva Kumar Kannan and Urmila Diwekar
Algorithms 2024, 17(5), 195; https://doi.org/10.3390/a17050195 - 6 May 2024
Cited by 5 | Viewed by 2821
Abstract
This paper introduces an innovative Particle Swarm Optimization (PSO) Algorithm incorporating Sobol and Halton random number samplings. It evaluates the enhanced PSO’s performance against conventional PSO employing Monte Carlo random number samplings. The comparison involves assessing the algorithms across nine benchmark problems and [...] Read more.
This paper introduces an innovative Particle Swarm Optimization (PSO) Algorithm incorporating Sobol and Halton random number samplings. It evaluates the enhanced PSO’s performance against conventional PSO employing Monte Carlo random number samplings. The comparison involves assessing the algorithms across nine benchmark problems and the renowned Travelling Salesman Problem (TSP). The results reveal consistent enhancements achieved by the enhanced PSO utilizing Sobol/Halton samplings across the benchmark problems. Particularly noteworthy are the Sobol-based PSO improvements in iterations and the computational times for the benchmark problems. These findings underscore the efficacy of employing Sobol and Halton random number generation methods to enhance algorithm efficiency. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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28 pages, 14896 KiB  
Article
IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip
by Sharoon Saleem, Fawad Hussain and Naveed Khan Baloch
Algorithms 2024, 17(3), 115; https://doi.org/10.3390/a17030115 - 10 Mar 2024
Cited by 2 | Viewed by 1796
Abstract
Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In [...] Read more.
Network on Chip (NoC) has emerged as a potential substitute for the communication model in modern computer systems with extensive integration. Among the numerous design challenges, application mapping on the NoC system poses one of the most complex and demanding optimization problems. In this research, we propose a hybrid improved whale optimization algorithm with enhanced genetic properties (IWOA-IGA) to optimally map real-time applications onto the 2D NoC Platform. The IWOA-IGA is a novel approach combining an improved whale optimization algorithm with the ability of a refined genetic algorithm to optimally map application tasks. A comprehensive comparison is performed between the proposed method and other state-of-the-art algorithms through rigorous analysis. The evaluation consists of real-time applications, benchmarks, and a collection of arbitrarily scaled and procedurally generated large-task graphs. The proposed IWOA-IGA indicates an average improvement in power reduction, improved energy consumption, and latency over state-of-the-art algorithms. Performance based on the Convergence Factor, which assesses the algorithm’s efficiency in achieving better convergence after running for a specific number of iterations over other efficiently developed techniques, is introduced in this research work. These results demonstrate the algorithm’s superior convergence performance when applied to real-world and synthetic task graphs. Our research findings spotlight the superior performance of hybrid improved whale optimization integrated with enhanced GA features, emphasizing its potential for application mapping in NoC-based systems. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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26 pages, 5413 KiB  
Article
A Self-Adaptive Meta-Heuristic Algorithm Based on Success Rate and Differential Evolution for Improving the Performance of Ridesharing Systems with a Discount Guarantee
by Fu-Shiung Hsieh
Algorithms 2024, 17(1), 9; https://doi.org/10.3390/a17010009 - 25 Dec 2023
Cited by 8 | Viewed by 2034
Abstract
One of the most significant financial benefits of a shared mobility mode such as ridesharing is cost savings. For this reason, a lot of studies focus on the maximization of cost savings in shared mobility systems. Cost savings provide an incentive for riders [...] Read more.
One of the most significant financial benefits of a shared mobility mode such as ridesharing is cost savings. For this reason, a lot of studies focus on the maximization of cost savings in shared mobility systems. Cost savings provide an incentive for riders to adopt ridesharing. However, if cost savings are not properly allocated to riders or the financial benefit of cost savings is not sufficient to attract riders to use a ridesharing mode, riders will not accept a ridesharing mode even if the overall cost savings is significant. In a recent study, the concept of discount-guaranteed ridesharing has been proposed to provide an incentive for riders to accept ridesharing services through ensuring a minimal discount for drivers and passengers. In this study, an algorithm is proposed to improve the performance of the discount-guaranteed ridesharing systems. Our approach combines a success rate-based self-adaptation scheme with an evolutionary computation approach. We propose a new self-adaptive metaheuristic algorithm based on success rate and differential evolution for the Discount-Guaranteed Ridesharing Problem (DGRP). We illustrate effectiveness of the proposed algorithm by comparing the results obtained using our proposed algorithm with other competitive algorithms developed for this problem. Preliminary results indicate that the proposed algorithm outperforms other competitive algorithms in terms of performance and convergence rate. The results of this study are consistent with the empirical experience that two people working together are more likely to come to a correct decision than they would if working alone. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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24 pages, 5529 KiB  
Article
Improved Load Frequency Control in Power Systems Hosting Wind Turbines by an Augmented Fractional Order PID Controller Optimized by the Powerful Owl Search Algorithm
by Farhad Amiri, Mohsen Eskandari and Mohammad Hassan Moradi
Algorithms 2023, 16(12), 539; https://doi.org/10.3390/a16120539 - 25 Nov 2023
Cited by 5 | Viewed by 2692
Abstract
The penetration of intermittent wind turbines in power systems imposes challenges to frequency stability. In this light, a new control method is presented in this paper by proposing a modified fractional order proportional integral derivative (FOPID) controller. This method focuses on the coordinated [...] Read more.
The penetration of intermittent wind turbines in power systems imposes challenges to frequency stability. In this light, a new control method is presented in this paper by proposing a modified fractional order proportional integral derivative (FOPID) controller. This method focuses on the coordinated control of the load-frequency control (LFC) and superconducting magnetic energy storage (SMES) using a cascaded FOPD–FOPID controller. To improve the performance of the FOPD–FOPID controller, the developed owl search algorithm (DOSA) is used to optimize its parameters. The proposed control method is compared with several other methods, including LFC and SMES based on the robust controller, LFC and SMES based on the Moth swarm algorithm (MSA)–PID controller, LFC based on the MSA–PID controller with SMES, and LFC based on the MSA–PID controller without SMES in four scenarios. The results demonstrate the superior performance of the proposed method compared to the other mentioned methods. The proposed method is robust against load disturbances, disturbances caused by wind turbines, and system parameter uncertainties. The method suggested is characterized by its resilience in addressing the challenges posed by load disturbances, disruptions arising from wind turbines, and uncertainties surrounding system parameters. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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18 pages, 5509 KiB  
Article
Comparison of Meta-Heuristic Optimization Algorithms for Global Maximum Power Point Tracking of Partially Shaded Solar Photovoltaic Systems
by Timmidi Nagadurga, Ramesh Devarapalli and Łukasz Knypiński
Algorithms 2023, 16(8), 376; https://doi.org/10.3390/a16080376 - 5 Aug 2023
Cited by 14 | Viewed by 3560
Abstract
Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, resulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to operate effectively. This research work mainly focuses on the exploration of [...] Read more.
Partial shading conditions lead to power mismatches among photovoltaic (PV) panels, resulting in the generation of multiple peak power points on the P-V curve. At this point, conventional MPPT algorithms fail to operate effectively. This research work mainly focuses on the exploration of performance optimization and harnessing more power during the partial shading environment of solar PV systems with a single-objective non-linear optimization problem subjected to different operations formulated and solved using recent metaheuristic algorithms such as Cat Swarm Optimization (CSO), Grey Wolf Optimization (GWO) and the proposed Chimp Optimization algorithm (ChOA). This research work is implemented on a test system with the help of MATLAB/SIMULINK, and the obtained results are discussed. From the overall results, the metaheuristic methods used by the trackers based on their analysis showed convergence towards the global Maximum Power Point (MPP). Additionally, the proposed ChOA technique shows improved performance over other existing algorithms. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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30 pages, 2668 KiB  
Article
Applying Particle Swarm Optimization Variations to Solve the Transportation Problem Effectively
by Chrysanthi Aroniadi and Grigorios N. Beligiannis
Algorithms 2023, 16(8), 372; https://doi.org/10.3390/a16080372 - 3 Aug 2023
Cited by 8 | Viewed by 1985
Abstract
The Transportation Problem (TP) is a special type of linear programming problem, where the objective is to minimize the cost of distributing a product from a number of sources to a number of destinations. Many methods for solving the TP have been studied [...] Read more.
The Transportation Problem (TP) is a special type of linear programming problem, where the objective is to minimize the cost of distributing a product from a number of sources to a number of destinations. Many methods for solving the TP have been studied over time. However, exact methods do not always succeed in finding the optimal solution or a solution that effectively approximates the optimal one. This paper introduces two new variations of the well-established Particle Swarm Optimization (PSO) algorithm named the Trigonometric Acceleration Coefficients-PSO (TrigAc-PSO) and the Four Sectors Varying Acceleration Coefficients PSO (FSVAC-PSO) and applies them to solve the TP. The performances of the proposed variations are examined and validated by carrying out extensive experimental tests. In order to demonstrate the efficiency of the proposed PSO variations, thirty two problems with different sizes have been solved to evaluate and demonstrate their performance. Moreover, the proposed PSO variations were compared with exact methods such as Vogel’s Approximation Method (VAM), the Total Differences Method 1 (TDM1), the Total Opportunity Cost Matrix-Minimal Total (TOCM-MT), the Juman and Hoque Method (JHM) and the Bilqis Chastine Erma method (BCE). Last but not least, the proposed variations were also compared with other PSO variations that are well known for their completeness and efficiency, such as Decreasing Weight Particle Swarm Optimization (DWPSO) and Time Varying Acceleration Coefficients (TVAC). Experimental results show that the proposed variations achieve very satisfactory results in terms of their efficiency and effectiveness compared to existing either exact or heuristic methods. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
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