An algorithm for numerical nonlinear optimization: Fertile Field Algorithm (FFA)

Mohammadi, Mohsen; Khodaygan, S.

doi:10.1007/s12652-019-01598-3

Cited by 13 publications

(10 citation statements)

References 30 publications

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“…Plant-Based Algorithms has been categorized as the fifth class of population-based metaheuristic optimization algorithm that mimics the intelligent behavior exhibited by plants. Some of the renowned plantbased algorithms are: Plant Growth Optimization [119], Root Growth Algorithm [120], Invasive Weed Optimization [121], Fertile Field Algorithm [122], Flower Pollination Algorithm [123], Paddy Field Algorithm [124], Root Mass Optimization Algorithm [125], Artificial Plant Optimization Algorithm [126], Sapling Growing up Algorithm [127], Photosynthetic Algorithm [42], Plant Propagation Algorithm [128], Rooted Tree Optimization [129], Path Planning inspired by Plant Growth [130] and Artificial Root Foraging Algorithm [131]. The last category that falls under the population-based meta-heuristic optimization algorithm is the Maths-Based Algorithms that basically tend to imitate the procedure of numerical techniques, mathematical programming and its orientation to resolve numerous constraints and optimization issues of the real environment.…”

Section: Introductionmentioning

confidence: 99%

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Rai

Dhal

Das

et al. 2023

Arch Computat Methods Eng

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Marine Predators Algorithm (MPA) is the existing population-based meta-heuristic algorithms that falls under the category of Nature-Inspired Optimization Algorithm (NIOA) enthused by the foraging actions of the marine predators that principally pursues Levy or Brownian approach as its foraging strategy. Furthermore, it employs the optimal encounter rate stratagem involving both the predator as well as prey. Since its introduction by Faramarzi in the year 2020, MPA has gained enormous popularity and has been employed in numerous application areas ranging from Mathematical and Engineering Optimization problems to Fog Computing to Image Processing to Photovoltaic System to Wind-Solar Generation System for resolving continuous optimization problems. Such huge interest from the research fraternity or the massive recognition of MPA is due to several factors such as its simplicity, ease of application, realistic execution time, superior convergence acceleration rate, soaring effectiveness, its ability to unravel continuous, multi-objective and binary problems when compared with other renowned optimization algorithms existing in the literature. This paper offers a detailed summary of the Marine Predators Algorithm (MPA) and its variants. Furthermore, the applications of MPA in a number of spheres such as Image processing, classification, electrical power system, Photovoltaic models, structural damage detection, distribution networks, engineering applications, Task Scheduling, optimization problems etc., are illustrated. To conclude, the paper highlights and thereby advocates few of the potential future research directions for MPA.

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Section: Introductionmentioning

confidence: 99%

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Rai

Dhal

Das

et al. 2023

Arch Computat Methods Eng

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show abstract

“…Invasive weed optimization algorithm is a random search algorithm that simulates the process of weed colonization in na- 2006; Giri et al, 2010). Compared with particle swarm algorithm, weed algorithm only needs smaller storage space when memorizing the trajectory of particles (Nguyen et al, 2020;Zhao et al, 2020;Mohammadi and Khodaygan, 2020), compared with genetic algorithm and other evolutionary algorithms, the algorithm programming is easy to implement, and it is easy to realize without genetic algorithm. In the case of operating operators, it can easily and effectively converge to the optimal solution of the problem.…”

Section: Invasive Weed Algorithmmentioning

confidence: 99%

Research on Optimization Operation Technology of Qt Oil Pipeline Based on the Heuristic Algorithm

et al. 2022

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“…GBDT is the machine learning technique, it produce the relationship between the input and output target value. 69,70 If new data is added to the output, which is calculated depending on this relationship.…”

Section: Proposed Ffa-gbdt Approach For Energy Managementmentioning

confidence: 99%