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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: 31 July 2024 | Viewed by 6812

Special Issue Editors

Dr. Łukasz Knypiński

E-Mail Website
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

Published Papers (5 papers)

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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://0-doi-org.brum.beds.ac.uk/10.3390/a17030115 - 10 Mar 2024
Viewed by 765
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://0-doi-org.brum.beds.ac.uk/10.3390/a17010009 - 25 Dec 2023
Cited by 1 | Viewed by 1158
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://0-doi-org.brum.beds.ac.uk/10.3390/a16120539 - 25 Nov 2023
Viewed by 1645
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://0-doi-org.brum.beds.ac.uk/10.3390/a16080376 - 05 Aug 2023
Cited by 4 | Viewed by 1418
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://0-doi-org.brum.beds.ac.uk/10.3390/a16080372 - 03 Aug 2023
Viewed by 880
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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