A Hybrid of ﻿Fireworks and Harmony Search Algorithm for Multilevel Image Thresholding

Shivali,; Maurya, Lalit; Sharma, Ekta; Mahapatra, Prasant Kumar; Doegar, Amit

doi:10.1007/978-981-10-4603-2_2

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…In the case of D = 10, the DMDS-HS algorithm is significantly superior to HS, IHS, GHS, SGHS, NGHS, IGHS, LHS, IMGHS, and IDHS-LDD algorithms in 29, 24, 28, 28, 29, 29, 27, 30, and 13 of the 30 functions, respectively. Similarly, when D = 30, the DMDS-HS algorithm outperforms the HS, IHS, GHS, SGHS, NGHS, IGHS, LHS, IMGHS, and ID-HS-LDD algorithms in 28,27,29,30,29,30,23,26, and 20 of the 30 functions, respectively. And in the highdimensional case (D = 50), the DMDS-HS algorithm also performs well.…”

Section: Comparison Of Dmds-hs Under the Same Evaluation Budgetmentioning

confidence: 92%

“…And in the highdimensional case (D = 50), the DMDS-HS algorithm also performs well. It outperforms the HS, IHS, GHS, SGHS, NGHS, IGHS, LHS, IMGHS, and ID-HS-LDD algorithms in 26,26,28,30,28,30,25,26, and 24 of the 30 functions, respectively. As can be seen from the comparative notation in Tables 3−9, although the DMDS-HS algorithm does not give satisfactory results on functions F10, F12, and F18, it achieves the best results on the vast majority of the other functions.…”

Section: Comparison Of Dmds-hs Under the Same Evaluation Budgetmentioning

confidence: 94%

“…Wang et al [36] transformed optimization variables into matrix form, utilizing a Conductor State Memory (CSM) to record time sequence constraints for scheduling problems. Li et al [26] proposed an innovative harmony generation strategy that relies on explicit learning experiences, improving search efficiency and applying it to image segmentation with constraints on the search space. However, some enhancement strategies require significant computational resources and time, limiting their application to large-scale problems.…”

Section: Introductionmentioning

confidence: 99%

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Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Wang,

Ouyang,

Zhou

et al. 2023

Complex Syst. Model. Simul.

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Harmony Search (HS) algorithm is highly effective in solving a wide range of real-world engineering optimization problems. However, it still has the problems such as being prone to local optima, low optimization accuracy, and low search efficiency. To address the limitations of the HS algorithm, a novel approach called the Dual-Memory Dynamic Search Harmony Search (DMDS-HS) algorithm is introduced. The main innovations of this algorithm are as follows: Firstly, a dual-memory structure is introduced to rank and hierarchically organize the harmonies in the harmony memory, creating an effective and selectable trust region to reduce approach blind searching. Furthermore, the trust region is dynamically adjusted to improve the convergence of the algorithm while maintaining its global search capability. Secondly, to boost the algorithm's convergence speed, a phased dynamic convergence domain concept is introduced to strategically devise a global random search strategy. Lastly, the algorithm constructs an adaptive parameter adjustment strategy to adjust the usage probability of the algorithm's search strategies, which aim to rationalize the abilities of exploration and exploitation of the algorithm. The results tested on the Computational Experiment Competition on 2017 (CEC2017) test function set show that DMDS-HS outperforms the other nine HS algorithms and the other four state-of-the-art algorithms in terms of diversity, freedom from local optima, and solution accuracy. In addition, applying DMDS-HS to data clustering problems, the results show that it exhibits clustering performance that exceeds the other seven classical clustering algorithms, which verifies the effectiveness and reliability of DMDS-HS in solving complex data clustering problems.

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Section: Comparison Of Dmds-hs Under the Same Evaluation Budgetmentioning

confidence: 92%

Section: Comparison Of Dmds-hs Under the Same Evaluation Budgetmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Wang,

Ouyang,

Zhou

et al. 2023

Complex Syst. Model. Simul.

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Equilibrium optimizer-based harmony search algorithm with nonlinear dynamic domains and its application to real-world optimization problems

Wang,

Ouyang,

et al. 2024

Artif Intell Rev

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Harmony Search (HS) algorithm is a swarm intelligence algorithm inspired by musical improvisation. Although HS has been applied to various engineering problems, it faces challenges such as getting trapped in local optima, slow convergence speed, and low optimization accuracy when applied to complex problems. To address these issues, this paper proposes an improved version of HS called Equilibrium Optimization-based Harmony Search Algorithm with Nonlinear Dynamic Domains (EO-HS-NDD). EO-HS-NDD integrates multiple leadership-guided strategies from the Equilibrium Optimizer (EO) algorithm, using harmony memory considering disharmony and historical harmony memory, while leveraging the hidden guidance direction information from the Equilibrium Optimizer. Additionally, the algorithm designs a nonlinear dynamic convergence domain to adaptively adjust the search space size and accelerate convergence speed. Furthermore, to balance exploration and exploitation capabilities, appropriate adaptive adjustments are made to Harmony Memory Considering Rate (HMCR) and Pitch Adjustment Rate (PAR). Experimental validation on the CEC2017 test function set demonstrates that EO-HS-NDD outperforms HS and nine other HS variants in terms of robustness, convergence speed, and optimization accuracy. Comparisons with advanced versions of the Differential Evolution (DE) algorithm also indicate that EO-HS-NDD exhibits superior solving capabilities. Moreover, EO-HS-NDD is applied to solve 15 real-world optimization problems from CEC2020 and compared with advanced algorithms from the CEC2020 competition. The experimental results show that EO-HS-NDD performs well in solving real-world optimization problems.

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Monarch butterfly optimization algorithm for computed tomography image segmentation

Dorgham

Alweshah

Ryalat

et al. 2021

Multimed Tools Appl

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A Hybrid of Fireworks and Harmony Search Algorithm for Multilevel Image Thresholding

Cited by 3 publications

References 12 publications

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Equilibrium optimizer-based harmony search algorithm with nonlinear dynamic domains and its application to real-world optimization problems

Monarch butterfly optimization algorithm for computed tomography image segmentation

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A Hybrid of ﻿Fireworks and Harmony Search Algorithm for Multilevel Image Thresholding

Cited by 3 publications

References 12 publications

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Harmony Search Algorithm Based on Dual-Memory Dynamic Search and Its Application on Data Clustering

Equilibrium optimizer-based harmony search algorithm with nonlinear dynamic domains and its application to real-world optimization problems

Monarch butterfly optimization algorithm for computed tomography image segmentation

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Product

Resources

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A Hybrid of Fireworks and Harmony Search Algorithm for Multilevel Image Thresholding