Metaheuristic Algorithms: A Comprehensive Review

Abdel‐Basset, Mohamed; Abdel-Fatah, Laila; Sangaiah, Arun Kumar

doi:10.1016/b978-0-12-813314-9.00010-4

Cited by 292 publications

(104 citation statements)

References 86 publications

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“…Based on their nature, we can categorize the algorithms in several ways. These algorithms may be classified as single solution based or population based [37], metaphor based or non-metaphor based [38], nature inspired or non-nature inspired [39]. Based on the inspiration used to implement the algorithms, we may categorize these as evolutionary, swarm based and physics based [40].…”

Section: Literature Surveymentioning

confidence: 99%

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

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Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many metaheuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics-Harmony Search (HS) algorithm and Artificial Electric Field Algorithm (AEFA), which we have named as Electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-Algorithms/FS_AEFAhHS.

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Section: Literature Surveymentioning

confidence: 99%

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

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“…Meta-heuristic algorithms are categorized differently in the literature: single solution based and population based [18], nature inspired and non-nature inspired [19], metaphor based and non-metaphor based [20]. These algorithms can also be divided into four different categories from 'inspiration' point of view [21]: Evolutionary, Swarm inspired, Physics based and Human behavior related.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid of Harmony Search Algorithm and Ring Theory-Based Evolutionary Algorithm for Feature Selection

et al. 2020

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Feature Selection (FS) is an important pre-processing step in the fields of machine learning and data mining, which has a major impact on the performance of the corresponding learning models. The main goal of FS is to remove the irrelevant and redundant features, resulting in optimized time and space requirements along with enhanced performance of the learning model under consideration. Many metaheuristic optimization techniques have been applied to solve FS problems because of its superiority over the traditional optimization approaches. Here, we have introduced a new hybrid meta-heuristic FS model based on a well-known meta-heuristic Harmony Search (HS) algorithm and a recently proposed Ring Theory based Evolutionary Algorithm (RTEA), which we have named as Ring Theory based Harmony Search (RTHS). Effectiveness of RTHS has been evaluated by applying it on 18 standard UCI datasets and comparing it with 10 state-of-the-art meta-heuristic FS methods. Obtained results prove the superiority of RTHS over the state-of-the-art methods considered here for comparison.

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“…As a modern trend in optimization, researchers across the globe are proposing various meta-heuristic algorithms to tackle different avenues of optimization problems. Meta-heuristic algorithms can be divided into different categories based on varied criteria: single solution based and population based [15], nature inspired and non-nature inspired [16], metaphor based and non-metaphor based [17]. From the 'inspiration' point of view, these algorithms can roughly be divided into four categories [18]: Evolutionary, Swarm inspired, Physics based, and Human related.…”

Section: Related Workmentioning

confidence: 99%

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

Guha¹,

Ghosh²,

Ghosh³

et al. 2020

Preprint

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A novel meta-heuristic nature-inspired optimization algorithm known as Groundwater Flow Algorithm (GWFA) is proposed in this paper. GWFA is inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy's law which provides a mathematical framework of groundwater flow. The proposed optimization algorithm has been evaluated on 23 benchmark functions. The significance of the results is statistically validated using Wilcoxon rank-sum, Friedman and Kruskal Walis tests. To prove the robustness of the algorithm, it has been further applied on several standard engineering problems. From these exhaustive experiments, it has been observed that the proposed GWFA can outperform many state-of-the-art optimization algorithms.

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Metaheuristic Algorithms: A Comprehensive Review

Cited by 292 publications

References 86 publications

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

Hybrid of Harmony Search Algorithm and Ring Theory-Based Evolutionary Algorithm for Feature Selection

Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm

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