Hybrid Ant Colony System and Genetic Algorithm Approach for Scheduling of Jobs in Computational Grid

Alobaedy, Mustafa Muwafak; Ku-Mahamud, Ku Ruhana

doi:10.19026/rjaset.11.2044

Cited by 2 publications

(14 citation statements)

References 68 publications

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“…Evolutionary Algorithms (EAs) are a significant subcategory of metaheuristic algorithms [19]. Comparable to natural processes like crossover, mutation, and selection found in live organics, EAs go through similar processes [20]. Several algorithms have been developed under the EA category, like genetic algorithms, genetic programming, evolutionary programming, and evolution strategies [20].…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

“…Comparable to natural processes like crossover, mutation, and selection found in live organics, EAs go through similar processes [20]. Several algorithms have been developed under the EA category, like genetic algorithms, genetic programming, evolutionary programming, and evolution strategies [20]. For example, the GMS problem has been solved using differential evolution and genetic algorithms in the literature [7,13,21,22].…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

“…John Holland developed Genetic Algorithm (GA) in 1975 [20]. A genetic algorithm is used as a population-based metaheuristic to imitate biological evolution and other natural selection processes to find appropriate answers [2,10].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…Genetic algorithms often converge prematurely to a solution [23]. There are three main operators to the GA: crossover, mutation, and selection [20,24].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…Another type of optimization technique, Differential Evolution (DE), was first introduced in 1995 by (Storn and Price) [5,7,20]. It is a distinct population-based algorithm that uses the operators' crossover, mutation, and selection to evolve a population into potential contenders for an optimal solution [20].…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

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Single Objective Optimization Methods in Electrical Power Systems: A Review

Al-kharaz¹,

A.Wahhab²,

Ibrahim³

et al. 2023

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Although the scheduling of maintenance tasks for generators is not a new issue, it has recently attracted new attention due to the significant rise in demand for expanding power system size in modern power systems. Generator Maintenance Scheduling (GMS) is a nonlinear optimization problem, highly dimensional and constrained, and determines when power-producing units must undertake well-planned preventative maintenance. The objective function includes binary variables to indicate whether a generator is undergoing maintenance at a given time and is subject to several restrictions described in this paper. However, the biggest concern of GMS is to produce a precise timetable for preventive maintenance of generating units with low cost and high reliability. Despite that, regrettably, a large volume of research works has accomplished solutions towards a model of GMS with the consideration of either maximizing system reliability or minimizing operation costs as an objective of their research work. This is called Single-Objective Problem (SOP), which involves one objective function that needs to be optimized. SOP is solved by Single-Objective Optimization Method (SOOM). The primary purpose of the research is to present a review of SOOM methods used in solving GMS problems.

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Section: Evolutionary Algorithmsmentioning

confidence: 99%

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Section: Genetic Algorithmsmentioning

confidence: 99%

“…Genetic algorithms often converge prematurely to a solution [23]. There are three main operators to the GA: crossover, mutation, and selection [20,24].…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Section: Differential Evolution Algorithmmentioning

confidence: 99%

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Single Objective Optimization Methods in Electrical Power Systems: A Review

Al-kharaz¹,

A.Wahhab²,

Ibrahim³

et al. 2023

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Hybrid Metaheuristic Algorithms for Feature Selection in Classification: A Systematic Literature Review

Othman,

Ku-Mahamud

2024

Preprint

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The effectiveness of a machine learning model is significantly impacted by feature selection. Feature selection is one of the most popular and highly effective techniques for eliminating irrelevant and redundant features to enhance the relevance of the collected data and improve the effectiveness of classification tasks. Feature selection is challenging because of the intricate relationship between features and large search space, which grows exponentially with the number of existing features in the original dataset. Metaheuristic algorithms are the most effective techniques for managing feature selection due to their robustness, intelligibility, and effectiveness in solving intricate optimization problems. Recent studies have focused on using hybrid metaheuristics as feature selection approaches. This systematic literature review explores recent studies from 2019 to 2023 that used hybrid metaheuristic algorithms for feature selection in classification. This paper aims to understand the existing hybrid algorithms, the goal of hybridization, the type of hybridization, and their application domains. Moreover, crucial parameters, fitness and transfer functions, initial population method, feature selection approach, classification algorithm, evaluation criteria, and statistical test are also investigated in this paper. A list of 30 relevant papers in line with the topic were extracted and examined to develop new insights in the domain of feature selection in classification. The focus is on a single fitness function (single objective). However, feature selection can be seen as a multi-objective problem, making hybridization in multi-objective feature selection problems a future research work for scholars.

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Hybrid Ant Colony System and Genetic Algorithm Approach for Scheduling of Jobs in Computational Grid

Cited by 2 publications

References 68 publications

Single Objective Optimization Methods in Electrical Power Systems: A Review

Single Objective Optimization Methods in Electrical Power Systems: A Review

Hybrid Metaheuristic Algorithms for Feature Selection in Classification: A Systematic Literature Review

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