Self-configuring Intelligent Water Drops Algorithm for Software Project Scheduling Problem

Crawford, Broderick; Soto, Ricardo; Astorga, Gino; Lemus, José Antonio Cruz; Salas-Fernández, Agustín

doi:10.1007/978-3-030-11890-7_27

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…Table 6 shows that the GWO is better than MMAS-HC, ACO, and IWD. To assess the GWO algorithm quality, we compared its results with the results obtained in [13], the GWO algorithm exceeded the results of IWDSTD and IWDAS as shown in Table 7. At last, we compared our results with the results of firefly algorithm in [16], Table 8 shows that GWO exceeded FA for all instances except one.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…When the datasets have a few tasks the WOA gave good results but it failed to find feasible solutions when increasing task's number. Crawford et al [13] presented the self-configuring of the velocity parameter of IWD metaheuristic that effects the algorithm behaviour by having a direct relationship between removed soil and velocity. The proposed methodology was used in solving the SPSP and the outcomes of standard version (IWDSTD) and configuring version (IWDAS) were very similar.…”

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confidence: 99%

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Solving software project scheduling problem using grey wolf optimization

2021

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In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project's duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

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confidence: 99%

Solving software project scheduling problem using grey wolf optimization

2021

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“…Optimization problems can be classified depending on the domain of the decision variables in discrete and continuous problems. In recent years, discrete optimization problems have become more and more frequent in the industry with problems as Set Covering Problem (SCP) [11,12], Knapsack Problem [13], Software Project Scheduling Problem [14,15] and Feature Selection [16]. The No-Free-Lunch Theorem (NFLT) [17] tells us that there is no universal optimization algorithm for all existing optimization problems.…”

Section: Introductionmentioning

confidence: 99%

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

et al. 2021

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One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.

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“…Алгоритм інтелектуальних крапель води. Одним із нещодавно запропонованих алгоритмів у сфері ройового інтелекту є алгоритм інтелектуальних крапель води [43,44]. Алгоритм базується на динаміці річкових систем, діях та реакціях, які відбуваються серед крапель води в річках.…”

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Metaheuristic Algorithms. Metaphors-Strategies (Review Article)

Bondarenko¹,

Ustynenko²,

Serykov³

2023

МіСАПР

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The relevance of modern metaheuristic algorithms, clarification of a number of terms and relationships between them, the need for classification, as well as metaphors used to describe algorithms are described. This makes it possible to understand the need to cover the specified topic and conduct research on literary sources related to the issue. A number of terms and categories and their interrelationships were considered, which made it possible to propose a classification of metaheuristic algorithms. An approach to the classification of metaheuristic algorithms based on the terms and division of categories taken from the natural sciences is proposed. According to the names of the classes, their filling takes place. This makes it possible to combine a certain segment of knowledge into a cluster with a single terminology. Considering the category of "metaphor" and its functions in the formation of metaheuristic algorithms, it made it possible to gain a deeper understanding of the possibilities of using metaphors in scientific activity and to form a list of requirements for them when describing algorithms. An overview of interesting, original and diverse metaheuristic algorithms was conducted, which made it possible to understand modern trends in this issue, to determine the advantages and disadvantages of algorithms, as well as to understand and shape the role of metaphor in their formation or description. Also, the review makes it possible to distinguish two intellectual directions of using metaphors: helping to increase understanding and intensifying the delivery of the idea to the target audience of an already developed algorithm or strategy and the development of new algorithms or strategies for finding optimal parameters. The system of formation and design of new knowledge and the role of metaphor in it are considered. The system triangle "idea-algorithm-metaphor" has been formed, as well as possible ways of development in this system, which allows the developer to define or choose a certain path and its subsequent stages. Keywords: metaheuristic algorithm, metaphor, optimization.

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Self-configuring Intelligent Water Drops Algorithm for Software Project Scheduling Problem

Cited by 4 publications

References 15 publications

Solving software project scheduling problem using grey wolf optimization

Solving software project scheduling problem using grey wolf optimization

Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics

Metaheuristic Algorithms. Metaphors-Strategies (Review Article)

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