Metaheuristic Algorithms in Engineering Optimization 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 (20 October 2020) | Viewed by 7488

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Electronic Engineering Department, University of Seville, 41004 Sevilla, Spain
Interests: multi-hop networks; sensor networks; VANETs; FANETs; evolutionary computation; machine learning; deep learning
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Special Issue Information

Dear Colleagues,

At present, many engineering optimization problems cannot be solved by traditional methods based on gradient. Several reasons make traditional methods unsuitable for complex engineering problems, such as usage of derivatives that are not available in simulation-based systems, where a mathematical formulation is difficult, and also poor performance in nonconvex landscapes, where local minimum/maximum can stop the optimization algorithm. In the last decade, new meta-heuristic algorithms have arisen, such as Firefly Algorithm, Harmonic Search, and Bat Optimization, among other approaches, that present significant performances in many engineering areas, such as telecommunications, robotics, mechanical design, and power systems, among others. Furthermore, multiobjective approaches like the ones based on Pareto dominance are appropriate for engineering applications, where normally, efficiency and cost performance metrics are counterbalanced.

This Special Issue pursues both novel metaheuristic algorithms and the application of existing metaheuristic approaches in engineering problems. Since abundant liteturate can be found in some engineering areas, both surveys and literature reviews are welcome.

The possible topics of interest include but are not limited to the following areas:

  • Genetic Algorithm (GA) for engineering optimization problems;
  • Swarm Optimization Algorithms (PSO, Firefly, Ant Colony, etc.) for engineering optimization problems;
  • Bio-inspired optimization algorithms for engineering optimization problems;
  • Genetic programing for engineering optimization problems;
  • Evolutionary strategies for engineering optimization problems;
  • Multiobjective optimization for engineering optimization problems;
  • Evolutionary algorithms based on subrogate models for engineering optimization problems;
  • Hybrid metaheuristic algorithms for engineering optimization problems;
  • Parallel metaheuristic algorithms for engineering optimization problems;
  • Combination of machine learning approaches and metaheuristic algorithms for engineering optimization problems;
  • Application of metaheuristic algorithms for adjusting the hyperparameter of Deep Learning models applied to engineering problems.

Dr. Daniel Gutiérrez Reina
Dr. Kathiravan Srinivasan
Dr. vishal sharma
Guest Editor

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Keywords

  • Evolutionary computation
  • Bio-inspired optimization
  • Swarm optimization
  • Machine learning
  • Genetic programming

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Published Papers (2 papers)

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19 pages, 1631 KiB  
Article
Sine Cosine Algorithm Assisted FOPID Controller Design for Interval Systems Using Reduced-Order Modeling Ensuring Stability
by Jagadish Kumar Bokam, Naresh Patnana, Tarun Varshney and Vinay Pratap Singh
Algorithms 2020, 13(12), 317; https://doi.org/10.3390/a13120317 - 1 Dec 2020
Cited by 9 | Viewed by 2828
Abstract
The focus of present research endeavor was to design a robust fractional-order proportional-integral-derivative (FOPID) controller with specified phase margin (PM) and gain cross over frequency (ωgc) through the reduced-order model for continuous interval systems. Currently, this investigation is two-fold: [...] Read more.
The focus of present research endeavor was to design a robust fractional-order proportional-integral-derivative (FOPID) controller with specified phase margin (PM) and gain cross over frequency (ωgc) through the reduced-order model for continuous interval systems. Currently, this investigation is two-fold: In the first part, a modified Routh approximation technique along with the matching Markov parameters (MPs) and time moments (TMs) are utilized to derive a stable reduced-order continuous interval plant (ROCIP) for a stable high-order continuous interval plant (HOCIP). Whereas in the second part, the FOPID controller is designed for ROCIP by considering PM and ωgc as the performance criteria. The FOPID controller parameters are tuned based on the frequency domain specifications using an advanced sine-cosine algorithm (SCA). SCA algorithm is used due to being simple in implementation and effective in performance. The proposed SCA-based FOPID controller is found to be robust and efficient. Thus, the designed FOPID controller is applied to HOCIP. The proposed controller design technique is elaborated by considering a single-input-single-output (SISO) test case. Validity and efficacy of the proposed technique is established based on the simulation results obtained. In addition, the designed FOPID controller retains the desired PM and ωgc when implemented on HOCIP. Further, the results proved the eminence of the proposed technique by showing that the designed controller is working effectively for ROCIP and HOCIP. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Engineering Optimization Problems)
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26 pages, 7092 KiB  
Article
Study on Multi-Objective Optimization-Based Climate Responsive Design of Residential Building
by Zhixing Li, Paolo Vincenzo Genovese and Yafei Zhao
Algorithms 2020, 13(9), 238; https://doi.org/10.3390/a13090238 - 21 Sep 2020
Cited by 9 | Viewed by 3657
Abstract
This paper proposes an optimization process based on a parametric platform for building climate responsive design. Taking residential buildings in six typical American cities as examples, it proposes thermal environment comfort (Discomfort Hour, DH), building energy demand (BED) and building global cost (GC) [...] Read more.
This paper proposes an optimization process based on a parametric platform for building climate responsive design. Taking residential buildings in six typical American cities as examples, it proposes thermal environment comfort (Discomfort Hour, DH), building energy demand (BED) and building global cost (GC) as the objective functions for optimization. The design variables concern building orientation, envelope components, and window types, etc. The optimal solution is provided from two different perspectives of the public sector (energy saving optimal) and private households (cost-optimal) respectively. By comparing the optimization results with the performance indicators of the reference buildings in various cities, the outcome can give the precious indications to rebuild the U.S. residential buildings with a view to energy-efficiency and cost optimality depending on the location. Full article
(This article belongs to the Special Issue Metaheuristic Algorithms in Engineering Optimization Problems)
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