Fertilization optimization algorithm on CEC2015 and large scale problems

Ghafil, Hazim Nasir; Alsamia, Shaymaa; Jármai, Kâroly

doi:10.1556/606.2021.00343

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…( 2008 ) 193 Fertilization Optimization Algorithm (FOA) Ghafil et al. ( 2022 ) 194 Fibonacci Indicator Algorithm (FIA) Etminaniesfahani et al. ( 2018 ) 195 FIFA Word Cup Competitions (FIFA) Razmjooy et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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“…The advantages and disadvantages of employing an optimization algorithm for a control system are competitive depending on the nature of the algorithm itself [25,26]. However, the population size used for the statistical results in Table 1 is 50, with one independent run with three variable size of integer values.…”

Section: Tuning Weighting Matrixmentioning

confidence: 99%

Evolutionary control system of asymmetric quadcopter

Ghafil

Jármai

2023

IRASE

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Drones, specifically quadcopters, have increased in importance during the last years due to their wide range of applications, from civil applications to military employment. One of the most important issues in quadcopters is the efficient control system. While many researchers have dealt with building control systems for symmetric quadcopters, this work presents an efficient control system for asymmetric quadcopters using evolutionary computations. The problem is well-defined throughout the paper, and the methodology is explained in detail in the respective sections. A genetic algorithm is used to tune the weighting matrix of the control system after formulating the control system as an optimization problem. The genetic algorithm was fast and active to increase the performance of the proposed system.

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“…The various soft computing techniques, which represent a set of computational techniques designed to find solutions and deal with incomplete, uncertain, or imprecise data for which it is difficult to find solutions using traditional methods, are vastly used in various engineering fields [17][18][19]. Fuzzy logic [20], particle swarm optimization [21], neural networks [22], metaheuristic techniques [23] and genetic algorithms [24] are some soft computing techniques commonly used in engineering fields. In geotechnical engineering applications, it is widely used, like designing stabilized earth walls [25], assessing landslide and slope stability [26], predicting soil compression coefficient [27], modeling bearing capacity [28] and among others.…”

Section: Introductionmentioning

confidence: 99%

Random forest regression on pullout resistance of a pile

Alsamia,

Koch

2024

Pollack

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This research aims to study the pullout resistance of a helical pile using three methods of machine learning techniques, which are: random forest regression, support vector regression, and adaptive neuro-fuzzy inference system, based on experimental results of a helical pile. The performance of these three techniques has been d compared and the results show that random forest algorithm has best performance than neuro-fuzzy inference system and support vector technique. The results show that machine learning considered a good tool in terms of estimating the pullout resistance of helical piles in the soil.

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Fertilization optimization algorithm on CEC2015 and large scale problems

Cited by 7 publications

References 15 publications

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Evolutionary control system of asymmetric quadcopter

Random forest regression on pullout resistance of a pile

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