An Area-Optimized Chip of Ant Colony Algorithm Design in Hardware Platform Using the Address-Based Method

Fard, Elnaz Shafigh; Monfaredi, Khalil; Nadimi, Mohammad H.

doi:10.11591/ijece.v4i6.6923

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…Nature-inspired algorithms have become popular choices to solve a wide variety of optimization issues in diverse areas such as engineering [28][29][30][31][32][33][34], image processing and segmentation [35][36][37], global optimization [38][39][40][41][42][43][44][45], software fault prediction [46], scheduling [47][48][49][50], photovoltaic modeling [51][52][53][54], structural design problems [55][56][57][58][59], power and energy management [60][61][62], planning and routing problems [63-65], power take off and placements of wave energy converters [66,67], power consumption [68,69], and wind speed prediction [70,71].…”

Section: Related Workmentioning

confidence: 99%

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

et al. 2023

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Monkey king evolution (MKE) is a population-based differential evolutionary algorithm in which the single evolution strategy and the control parameter affect the convergence and the balance between exploration and exploitation. Since evolution strategies have a considerable impact on the performance of algorithms, collaborating multiple strategies can significantly enhance the abilities of algorithms. This is our motivation to propose a multi-trial vector-based monkey king evolution algorithm named MMKE. It introduces novel best-history trial vector producer (BTVP) and random trial vector producer (RTVP) that can effectively collaborate with canonical MKE (MKE-TVP) using a multi-trial vector approach to tackle various real-world optimization problems with diverse challenges. It is expected that the proposed MMKE can improve the global search capability, strike a balance between exploration and exploitation, and prevent the original MKE algorithm from converging prematurely during the optimization process. The performance of the MMKE was assessed using CEC 2018 test functions, and the results were compared with eight metaheuristic algorithms. As a result of the experiments, it is demonstrated that the MMKE algorithm is capable of producing competitive and superior results in terms of accuracy and convergence rate in comparison to comparative algorithms. Additionally, the Friedman test was used to examine the gained experimental results statistically, proving that MMKE is significantly superior to comparative algorithms. Furthermore, four real-world engineering design problems and the optimal power flow (OPF) problem for the IEEE 30-bus system are optimized to demonstrate MMKE’s real applicability. The results showed that MMKE can effectively handle the difficulties associated with engineering problems and is able to solve single and multi-objective OPF problems with better solutions than comparative algorithms.

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Section: Related Workmentioning

confidence: 99%

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

et al. 2023

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“…To solve feature selection problems, wrapper-based methods widely apply discrete metaheuristic optimization algorithms as search strategies to find effective feature subsets [47,[54][55][56][57]. Since the majority of metaheuristic optimization algorithms such as DA [58], SSA [59], HGSO [60], FFA [61], MTDE [62], QANA [63], and AO [21] have been proposed to solve continuous problems such as engineering [64][65][66][67][68], cloud computing [69], and rail-car fleet sizing [70], they should be converted into binary algorithms for using in wrapper-based methods and solving discrete problems. The continuous algorithm can be converted to a binary form in a variety of ways [71].…”

Section: Related Workmentioning

confidence: 99%

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

et al. 2021

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Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.

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“…Due to the strong robustness, on the basis of the basic ant colony algorithm model modification can be applied to other problems. Therefore, ant colony algorithm was developed to solve complex optimization problems in many areas provides a powerful tool, has the very broad development prospects [4][5][6].…”

Section: Ant Colony Algorithmmentioning

confidence: 99%

Improved Ant Colony Algorithm of Image Retrieval Methods

Hua-lin¹

2016

IJSIP

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Ant colony algorithm since it has a better convergence and parallelism, widely used in the data retrieval, however, is not high, due to the characteristics of retrieval object use seriously affect the accuracy of retrieval, and according to this problem, this paper proposed an improved ant colony algorithm, this algorithm will retrieve objects comprehensive characteristics into the ant colony algorithm, and solve the convergence speed and computational complexity of the algorithm, obtained good results in image retrieval.

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An Area-Optimized Chip of Ant Colony Algorithm Design in Hardware Platform Using the Address-Based Method

Cited by 8 publications

References 10 publications

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

MMKE: Multi-trial vector-based monkey king evolution algorithm and its applications for engineering optimization problems

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Improved Ant Colony Algorithm of Image Retrieval Methods

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