A Novel Self-Adaptive Harmony Search Algorithm

Harmony search algorithm is the recently developed metaheuristic in the last decade. It mimics the behavior of a musician producing a perfect harmony. It has been used to solve the wide variety of real-life optimization problems due to its easy implementation over other metaheuristics. It has an ability to provide the balance between exploration and exploitation during search. In this paper, a systematic review on harmony search algorithm (HSA) is presented. The natural inspiration and conceptual framework of HSA are discussed. The control parameters of HSA are described with their mathematical foundation. The improvement and hybridization in HSA with other metaheuristics are discussed in detail. The applicability of HSA in different problem domains is studied. The future research directions of HSA are also investigated.

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“…Luo [13] developed a novel self-adaptive mechanism for HSA. PAR was not considered during the improvisation process.…”

Section: Control Parameters Of Hsamentioning

confidence: 99%

“…where n represents the dimension of problem. BW was dynamic updated for each variable (say, z i ) and defined as [13] BW i (gn) �…”

Section: Control Parameters Of Hsamentioning

confidence: 99%

A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications

Dubey

Kumar

Kaur

et al. 2021

Mathematical Problems in Engineering

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“…While still requiring to fine-tune the HMCR parameter, the parameters PAR and BW are made dynamic in this variant with the introduction of the PARmax (BWmax) and PARmin (BWmin) iterative parameters. Although IHS has shown better performance than SHS, this variant increased the burdensome process of setting suitable values for four parameters instead of just two in SHS [51]. Nevertheless, IHS is considered to be a breakthrough that paved the way for the development of various HS variants to date.…”

Section: Improved Harmony Search (Ihs)mentioning

confidence: 99%

Parameter Estimation of Compartmental Epidemiological Model Using Harmony Search Algorithm and Its Variants

2021

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Epidemiological models play a vital role in understanding the spread and severity of a pandemic of infectious disease, such as the COVID-19 global pandemic. The mathematical modeling of infectious diseases in the form of compartmental models are often employed in studying the probable outbreak growth. Such models heavily rely on a good estimation of the epidemiological parameters for simulating the outbreak trajectory. In this paper, the parameter estimation is formulated as an optimization problem and a metaheuristic algorithm is applied, namely Harmony Search (HS), in order to obtain the optimized epidemiological parameters. The application of HS in epidemiological modeling is demonstrated by implementing ten variants of HS algorithm on five COVID-19 data sets that were calibrated with the prototypical Susceptible-Infectious-Removed (SIR) compartmental model. Computational experiments indicated the ability of HS to be successfully applied to epidemiological modeling and as an efficacious estimator for the model parameters. In essence, HS is proposed as a potential alternative estimation tool for parameters of interest in compartmental epidemiological models.

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“…Novel self-adaptive HS (NSHS) was developed by Luo [28], and it is a modified process for determining HMCR, PAR, and Bw from constant values. HMCR is set according to the dimension of the problem, and these values are analogous, e.g., a complex problem has a large HMCR, as shown by Equation (11).…”

Section: Novel Self-adaptive Harmony Searchmentioning

confidence: 99%

“…The method, called the self-adaptive HS (SAHS), generated the new solution according to the minimum and maximum values in the harmony memory (HM). Luo [28] developed the novel self-adaptive HS (NSHS) that considers the HMCR, PAR, and Bw for suitable parameter settings. HMCR is set to a constant value according to the number of decision variables.…”

mentioning

confidence: 99%

Self-Adaptive Models for Water Distribution System Design Using Single-/Multi-Objective Optimization Approaches

Choi

Kim

2019

Water

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This study compares the performance of self-adaptive optimization approaches in efficient water distribution systems (WDS) design and presents a guide for the selection of the appropriate method employing optimization utilizing the characteristic of each technique formulation. To this end, this study performs three types of analyses. First, the sensitivity analysis of each self-adaptive approach is conducted on single/multi-objective mathematical benchmark problems with various problem types (e.g., using solution shape or many local optimal solutions). Second, based on the applications and results of the mathematical problem, the performance of the algorithm is verified in the WDS design problem considering the minimum cost and the maximum system resilience under the single/multi-objective optimization framework. Third, the characteristics of search operators in the self-adaptive approach are compared according to the presence or absence of additional parameters and operators. Moreover, various performance indices are employed to compare the quantitative evaluation of each algorithm. Each algorithm is found to exhibit different characteristics depending on the problem scale and solution type. These results are expected to benefit future research in the formulation of new approaches and developments. Hence, this study provides rigorous testing of the performance of newly proposed algorithms in a highly simplified manner.

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A Novel Self-Adaptive Harmony Search Algorithm

Cited by 11 publications

References 21 publications

A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications

A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications

Parameter Estimation of Compartmental Epidemiological Model Using Harmony Search Algorithm and Its Variants

Self-Adaptive Models for Water Distribution System Design Using Single-/Multi-Objective Optimization Approaches

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