Harmony Search Algorithm - Theoretical Background and Practical Applications: Volume 2

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 4525

Special Issue Editor


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Guest Editor
Department of Smart City & Energy, Gachon University, Seongnam 13120, Republic of Korea
Interests: energy; environment; hydrosystems; renewable energy technologies; optimization; mathematical programming; algorithms; artificial neural networks; Harmony Search; music
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Special Issue Information

Dear Colleagues,

Harmony Search is a jazz-inspired phenomenon-mimicking algorithm that has been applied to various optimization problems, including engineering design, computer science, and energy system management, as well as music composition, fine art appreciation, sudoku puzzle solving, and astronomical observation. More information on Harmony Search can be found at: http://www.harmonysearch.info.

This Special Issue will review recent developments in both the theory and applications of this algorithm. We welcome original research articles, as well as review articles and short communications.

Potential topics include, but are not limited to:

  • Harmony Search;
  • Theoretical background;
  • Enhancement of the stochastic derivative;
  • Performance of searching behavior;
  • Hybridization with other algorithms;
  • Hybridization with machine learning models;
  • Hybridization with quantum computing;
  • Applications in engineering and science;
  • Applications in smart cities (smart grid, smart mobility, metaverse, etc.);
  • Sustainability.

Prof. Dr. Zong Woo Geem
Guest Editor

Manuscript Submission Information

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Keywords

  • Harmony Search
  • phenomenon-mimicking algorithm
  • human-experience-based stochastic derivative
  • global optimization
  • evolutionary algorithm
  • computational intelligence
  • quantum computing
  • engineering optimization
  • smart city
  • sustainability

Published Papers (3 papers)

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Research

28 pages, 8067 KiB  
Article
Behavioral Analysis of an Interval Type-2 Fuzzy Controller Designed with Harmony Search Enhanced with Shadowed Type-2 Fuzzy Parameter Adaptation
by Cinthia Peraza, Patricia Ochoa, Oscar Castillo and Patricia Melin
Appl. Sci. 2023, 13(13), 7964; https://doi.org/10.3390/app13137964 - 07 Jul 2023
Viewed by 969
Abstract
The challenges we face in today’s world are increasingly complex, and effectively managing uncertainty when modeling control problems can yield significant benefits. However, the complexity of these models often leads to higher computational costs. Therefore, the main contribution of this article is the [...] Read more.
The challenges we face in today’s world are increasingly complex, and effectively managing uncertainty when modeling control problems can yield significant benefits. However, the complexity of these models often leads to higher computational costs. Therefore, the main contribution of this article is the use of the theory of shadowed type-2 fuzzy sets to address these challenges and to control the search space exploration in the harmony search algorithm by employing two alpha planes, and with this, it was possible to reduce the computational cost and obtain effective results. Furthermore, the application of this approach aims to find optimal parameters for the membership functions of a type-2 fuzzy controller and analyze its behavior. By adopting the proposed methodology, it becomes possible to minimize computational costs while still achieving feasible solutions for interval type-2 control problems. A key aspect is that symmetry is considered in the design of the controller to also obtain good results. To validate the effectiveness of the approach, extensive simulations were conducted with varying levels of noise introduced to the type-2 controller. This comprehensive analysis allowed for a thorough examination of the results obtained. The findings of the simulations are presented, showcasing the advantages of the proposed methodology. By incorporating noise into the system, it was observed that the objective function, in this case, the root mean square error (RMSE), was reduced. Moreover, the signal obtained with the presence of noise demonstrated a superior performance compared to the noise-free reference. In conclusion, the proposed approach of utilizing shadowed type-2 fuzzy systems, combined with the harmony search algorithm, offers a promising solution for managing complex control problems. By carefully analyzing the behavior of the system through simulations, it is evident that the inclusion of noise helps improve the system’s performance. Full article
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21 pages, 1937 KiB  
Article
CDDO–HS: Child Drawing Development Optimization–Harmony Search Algorithm
by Azad A. Ameen, Tarik A. Rashid and Shavan Askar
Appl. Sci. 2023, 13(9), 5795; https://doi.org/10.3390/app13095795 - 08 May 2023
Cited by 3 | Viewed by 1366
Abstract
Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children’s learning behavior and cognitive development, with the golden ratio being employed to optimize the aesthetic value of their artwork. Unfortunately, CDDO suffers [...] Read more.
Child drawing development optimization (CDDO) is a recent example of a metaheuristic algorithm. The motive for inventing this method is children’s learning behavior and cognitive development, with the golden ratio being employed to optimize the aesthetic value of their artwork. Unfortunately, CDDO suffers from low performance in the exploration phase, and the local best solution stagnates. Harmony search (HS) is a highly competitive algorithm relative to other prevalent metaheuristic algorithms, as its exploration phase performance on unimodal benchmark functions is outstanding. Thus, to avoid these issues, we present CDDO–HS, a hybridization of both standards of CDDO and HS. The hybridized model proposed consists of two phases. Initially, the pattern size (PS) is relocated to the algorithm’s core and the initial pattern size is set to 80% of the total population size. Second, the standard harmony search (HS) is added to the pattern size (PS) for the exploration phase to enhance and update the solution after each iteration. Experiments are evaluated using two distinct standard benchmark functions, known as classical test functions, including 23 common functions and 10 CEC-C06 2019 functions. Additionally, the suggested CDDO–HS is compared to CDDO, the HS, and six others widely used algorithms. Using the Wilcoxon rank-sum test, the results indicate that CDDO–HS beats alternative algorithms. Full article
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19 pages, 2869 KiB  
Article
Machine Learning Models for Ecofriendly Optimum Design of Reinforced Concrete Columns
by Yaren Aydın, Gebrail Bekdaş, Sinan Melih Nigdeli, Ümit Isıkdağ, Sanghun Kim and Zong Woo Geem
Appl. Sci. 2023, 13(7), 4117; https://doi.org/10.3390/app13074117 - 23 Mar 2023
Cited by 4 | Viewed by 1678
Abstract
CO2 emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution [...] Read more.
CO2 emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO2 emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy. Full article
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