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Editorial

Metaheuristic Algorithms in Optimal Design of Engineering Problems

by
Łukasz Knypiński
1,*,
Ramesh Devarapalli
2,* and
Marcin Kamiński
3
1
Faculty of Automatic Control, Robotic and Electrical Engineering, Poznan University of Technology, 60-965 Poznan, Poland
2
Department of Electrical/Electronics and Instrumentation Engineering, Institute of Chemical Technology, Indianoil Odisha Campus, Bhubaneswar 751013, India
3
Department of Electrical Machines, Drives and Measurements, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-372 Wroclaw, Poland
*
Authors to whom correspondence should be addressed.
Algorithms 2024, 17(11), 522; https://doi.org/10.3390/a17110522
Submission received: 4 November 2024 / Accepted: 11 November 2024 / Published: 14 November 2024
(This article belongs to the Special Issue Metaheuristic Algorithms in Optimal Design of Engineering Problems)
Versions Notes

1. Introduction

Metaheuristic optimization algorithms (MOAs) are widely used to optimize the design process of engineering problems [1,2]. MOAs are successfully applied to the optimal design of electromagnetic devices [3], power dispatch problems [4], the search for the tune parameters of controllers [5] for different control systems, and many other applications [6,7]. Among metaheuristic algorithms, the most commonly used [8] are the Particle Swarm Optimization (PSO) algorithm, developed in 1995 [9], and the grey wolf optimization (GWO) algorithm, developed in 2014 [10].
Metaheuristic optimization algorithms are still intensively developed in many research centers all over the world [11]. Nowadays, researchers working in the area of the optimal design of technical objects have a huge number of various optimization algorithms. The algorithms have different properties and are characterized by different convergence. The final solution depends on the random coefficient, and metaheuristic characteristic parameters vary for different optimization algorithms. Due to the above reasons, the optimization process should be repeated several times for different starting populations [12,13,14].
In order to improve efficiency, modifications of classical methods are often developed [15]. The aim of developing modifications is to reduce the number of objective function calls and to increase the reliability of the optimization algorithm [16]. An increasing number of manuscripts on heuristic optimization methods are concerned with hybrid optimization algorithms [17,18]. Hybrid algorithms can consist of two, three, or even more different algorithms. In the available global literature, there are various algorithm architectures: parallel [19], serial [20], or even mixed [21].
Very often, after the proper selection of an optimization algorithm, it is necessary to execute the adaptation of the algorithm to the optimized system or optimized technical object [22,23,24]. Proper adaptation of the algorithm by correctly selecting the characteristic parameters of the selected algorithm can also improve the efficiency of the algorithm.
In this Special Issue, entitled “Metaheuristic Algorithms in Optimal Design of Engineering Problems”, the Editorial Office received 15 submissions from researchers worldwide. After a rigorous peer-review process, nine manuscripts were accepted for publication.
The Special Issue is concerned with the application of metaheuristic algorithms to solving various optimization problems. The authors publishing their works presented interesting approaches for researchers working with optimization algorithms, especially a hybrid whale optimization algorithm featuring improved genetic characteristics and a multi-objective majority–minority cellular automata algorithm.

2. Special Issue Contribution

Contribution 1: An extension of the Majority–minority Cellular Automata Algorithm, referred to as the Multi-objective Majority–minority Cellular Automata Algorithm, or MOMmCAA, to solve multi-objective optimization problems. By including the repository management and controlling the density of the multi-objective search space, MOMmCAA can optimize multiple objectives without a processing time. The performance of MOMmCAA was compared to established multi-objective algorithms on benchmark test sets and real-world engineering problems; in the results, MOMmCAA was comparable, giving promise for real applications in complex optimization problems.
Contribution 2 develops the work performed in robotics, focusing on humanoid robots that sense and act on a specific task in any industry. The paper discusses a hybrid H∞/sliding mode controller optimized using Particle Swarm Optimization to control a triple inverted pendulum, which has been considered a benchmark for driving joints, stability, and balance. The optimized controller could demonstrate robust performance across different types of perturbations with an average error of 0.053 degrees and a steady torque range of 0.13–0.621 N·m.
The third contribution contrasted heuristic versus metaheuristic algorithms (in particular, Steiglitz–McBride, Jaya, the genetic algorithm, and the Grey Wolf Optimizer) to maximize the fit of the optimal parameters found by a dynamic model to the current and angular velocity responses of DC motors. The study is presented with the most accurate parametric estimation average mean squared error of 0.43% but more computation expense; to the contrary, the Stieglitz–McBride algorithm, with an average MSE of 3.32%, is more computationally efficient. Overall, it appears that if precision is the priority, then GWO is likely a better option. For balance between performance and efficiency, however, the Stieglitz–McBride heuristic may provide an alternative with a more efficient solution, knowing that these algorithms’ performance will also depend on the particular error functions.
Contribution 4 proposes a new PSO algorithm that exploits the Sobol and Halton random number samplings with the performance comparison of traditional Monte Carlo-based PSO. For nine benchmark problems plus TSP, in all cases, the variant PSO improves over PSO, especially with better iteration efficiency via Sobol-based PSO and decreased computational times. These results indicate that using Sobol and Halton methods for generating random numbers makes optimization algorithms more efficient.
Contribution 5: Hybrid Improved Whale Optimization Algorithm with Enhanced Genetic Properties for Optimal Application Mapping on a 2D Network on Chip (NoC) Platform. This paper introduces a hybrid of an advanced whale optimization algorithm and an improved genetic algorithm that can perform better in power reduction, energy consumption, and latency than all other state-of-the-art algorithms for various benchmarks and real-time applications. Conclusions derived from the obtained results indicate the efficiency of the proposed hybrid approach. It shows superiority in converging better on both synthetic and real-world task graphs.
The cost-reducing benefit paid by the ridesharing concept encourages more riders to ride in this means of transportation. Contribution 6 is a discount-guaranteed ridesharing concept that assures that the kept discount should always be a minimum to help drivers and riders increase the latter’s acceptability. The researchers developed a new metaheuristic algorithm based on a differential evolution incorporating a self-adaptation scheme. They applied this to the DGRP with optimal performance and the fastest convergence compared with existing algorithms.
Contribution 7 recommends a new modified fractional order proportional integral derivative (FOPID) controller aimed at managing frequency stability issues due to the integration of intermittent wind turbines in power systems. A cascaded FOPD presents the proposed method–FOPID controller for coordinated LFC and SMES, optimized with the DOSA for its parameters. The comparative scenario of four scenarios showed how the proposed control technique would outsmart many of the state-of-the-art methods while providing satisfactory responses to such load changes, disturbances posed by wind turbines, or uncertainty due to parameters from the system.
Contribution 8 relates to this issue that partial shading conditions on the photovoltaic panel cause the presence of more than one peak power point at the P-V curve, degrading the efficiency of even the most superior MPPT algorithm adopted for the optimum extraction of power from this array. This study discusses the performance optimization through a single-objective nonlinear optimization problem by using some of the latest metaheuristic algorithms like Cat Swarm Optimization (CSO), grey wolf optimization (GWO), and a newly proposed Chimp Optimization algorithm (ChOA). The MATLAB/SIMULINK results indicate that all metaheuristic methods converge towards the global Maximum Power Point (MPP), and the ChOA shows better performance than the existing algorithms.
Contribution 9 addresses the Transportation Problem (TP) as a unique linear programming problem to minimize the costs of distribution between multiple sources and different destinations through two new adaptations of the Particle Swarm Optimization (PSO) algorithm: Trigonometric Acceleration Coefficients PSO (TrigAc-PSO) and Four Sectors Varying Acceleration Coefficients PSO (FSVAC-PSO). Thirty-two problems of varied sizes have been tested on extended experimental tests that ascertain new PSO variations that outperform classical exact techniques, such as Vogel’s Approximation Method and Total Differences Method, as well as already developed PSO variants: Decreasing Weight PSO. The solutions are two efficient and practical approaches to the Transportation Problem: TrigAc-PSO and FSVAC-PSO.

3. Final Remarks

These papers highlight the meaningful progress in the development of metaheuristic optimization algorithms for the optimal design of engineering problems. This Special Issue offers manuscripts presenting interesting algorithms for researchers, students, and practitioners. The Special Issue Editors thank all authors, reviewers, and the editorial team for making this Special Issue possible.

List of Contributions

  • Seck-Tuoh-Mora, J. C.; Hernandez-Hurtado, U.; Medina-Marín, J.; Hernández-Romero, N.; Lizárraga-Mendiola, L. Multi-Objective Majority–Minority Cellular Automata Algorithm for Global and Engineering Design Optimization. Algorithms 2024, 17, 433. https://doi.org/10.3390/a17100433.
  • Shafeek, Y.A.; Ali, H.I. Application of Particle Swarm Optimization to a Hybrid H∞/Sliding Mode Controller Design for the Triple Inverted Pendulum System. Algorithms 2024, 17, 427. https://doi.org/10.3390/a17100427.
  • Munciño, D.M.; Damian-Ramírez, E.A.; Cruz-Fernández, M.; Montoya-Santiyanes, L.A.; Rodríguez-Reséndiz, J. Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor. Algorithms 2024, 17, 209. https://doi.org/10.3390/a17050209.
  • Kannan, S.K.; Diwekar, U. An Enhanced Particle Swarm Optimization (PSO) Algorithm Employing Quasi-Random Numbers. Algorithms 2024, 17, 195. https://doi.org/10.3390/a17050195.
  • Saleem, S.; Hussain, F.; Baloch, N.K. IWO-IGA—A Hybrid Whale Optimization Algorithm Featuring Improved Genetic Characteristics for Mapping Real-Time Applications onto 2D Network on Chip. Algorithms 2024, 17, 115. https://doi.org/10.3390/a17030115.
  • Hsieh, F.-S. A Self-Adaptive Meta-Heuristic Algorithm Based on Success Rate and Differential Evolution for Improving the Performance of Ridesharing Systems with a Discount Guarantee. Algorithms 2024, 17, 9. https://doi.org/10.3390/a17010009.
  • Amiri, F.; Eskandari, M.; Moradi, M.H. Improved Load Frequency Control in Power Systems Hosting Wind Turbines by an Augmented Fractional Order PID Controller Optimized by the Powerful Owl Search Algorithm. Algorithms 2023, 16, 539. https://doi.org/10.3390/a16120539.
  • Nagadurga, T.; Devarapalli, R.; Knypiński, Ł. Comparison of Meta-Heuristic Optimization Algorithms for Global Maximum Power Point Tracking of Partially Shaded Solar Photovoltaic Systems. Algorithms 2023, 16, 376. https://doi.org/10.3390/a16080376.
  • Aroniadi, C.; Beligiannis, G.N. Applying Particle Swarm Optimization Variations to Solve the Transportation Problem Effectively. Algorithms 2023, 16, 372. https://doi.org/10.3390/a16080372.

Author Contributions

Special Issue Editorial by Ł.K., R.D. and M.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

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MDPI and ACS Style

Knypiński, Ł.; Devarapalli, R.; Kamiński, M. Metaheuristic Algorithms in Optimal Design of Engineering Problems. Algorithms 2024, 17, 522. https://doi.org/10.3390/a17110522

AMA Style

Knypiński Ł, Devarapalli R, Kamiński M. Metaheuristic Algorithms in Optimal Design of Engineering Problems. Algorithms. 2024; 17(11):522. https://doi.org/10.3390/a17110522

Chicago/Turabian Style

Knypiński, Łukasz, Ramesh Devarapalli, and Marcin Kamiński. 2024. "Metaheuristic Algorithms in Optimal Design of Engineering Problems" Algorithms 17, no. 11: 522. https://doi.org/10.3390/a17110522

APA Style

Knypiński, Ł., Devarapalli, R., & Kamiński, M. (2024). Metaheuristic Algorithms in Optimal Design of Engineering Problems. Algorithms, 17(11), 522. https://doi.org/10.3390/a17110522

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