Tree Physiology Optimization in Constrained Optimization Problem

Halim, Abdul; Ismail, Idris

doi:10.12928/telkomnika.v16i2.9021

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…( 2020 ) 491 Tree Growth Algorithm (TGA) Cheraghalipour et al. ( 2018 ) 492 Tree Physiology Optimization (TPO) Halim and Ismail ( 2018 ) 493 Tree Seed Algorithm (TSA) Kiran ( 2015 ) 494 Trees Social Relations Optimization Algorithm (TSR) Alimoradi et al. ( 2022 ) 495 Triple Distinct Search Dynamics (TDSD) Li 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

View full text Add to dashboard Cite

show abstract

“… Class Optimization algorithm Year Abbreviated form Reference Evolution based Artificial Infections Disease 2016 AIDO Huang (2016) Earthworm Optimization 2018 EOA (G. . Wang et al, 2018 ) Improved Genetic Immune 2017 IGIA Benbouzid-Si Tayeb et al (2017) Virulence Optimization 2016 VOA Jaderyan & Khotanlou (2016) Plants based Artificial Flora Optimization 2018 AFO Cheng et al (2018) Natural Forest Regeneration 2016 NFR Moez et al (2016) Root Tree Optimization 2016 RTOA Labbi et al (2016) Tree Growth 2018 TGA Cheraghalipour et al (2018) Tree Physiology Optimization 2018 TPO Halim & Ismail (2018) Social Human Behavior based Adolescent Identity Search 2020 AISA Bogar & Beyhan (2020) Cognitive Behavior Optimization 2016 COA (M. Li et al, 2016 ) Swarm intelligence based Andean Condor 2019 ACA Almonacid & Soto (2019) Bald Eagle Search 2020 BES Alsattar et al (2020) Bison Behavior 2019 BBA Kazikova et al (2019) Biology Migration ...…”

Section: Classification Of Bio-inspired Optimizationmentioning

confidence: 99%

Application of bio-inspired optimization algorithms in food processing

Sarkar

Salauddin

Mukherjee³

et al. 2022

Current Research in Food Science

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“…This section discusses how meta-heuristic algorithms [29], [67]- [70], [73]- [75], [82], [90], [95], [109], [119], [160], [163], [176], [179]- [188] are included in the fuzzy modelling process. The implementation of fuzzy meta-heuristic algorithms is depicted in Figure 6.…”

Section: Fuzzy Meta-heuristic Algorithmsmentioning

confidence: 99%

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

Aseri

Ismail

Fakharudin

et al. 2022

IJ-AI

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The world health organization (WHO) proclaimed the COVID-19, commonly known as the coronavirus disease 2019, was a pandemic in March 2020. When people are in close proximity to one another, the virus spreads mostly through the air. It causes some symptoms in the affected person. COVID-19 symptoms are quite variable, ranging from none to severe sickness. As a result, the fuzzy method is seen favourably as a tool for determining the severity of a person’s COVID-19 sickness. However, when applied to a large situation, manually generating a fuzzy parameter is challenging. This could be because of the identification of a large number of fuzzy parameters. A mechanism, such as an automatic procedure, is consequently required to identify the right fuzzy parameters. The metaheuristic algorithm is regarded as a viable strategy. Five meta-heuristic algorithms were analyzed and utilized in this article to classify the severity of COVID-19 sickness data. The performance of the five meta-heuristic algorithms was evaluated using the COVID-19 symptoms dataset. The COVID-19 symptom dataset was created in accordance with WHO and the Indian ministry of health and family welfare criteria. The findings provide the average classification accuracy for each approach.

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Tree Physiology Optimization in Constrained Optimization Problem

Abstract: Abstract

Cited by 5 publications

References 12 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

Application of bio-inspired optimization algorithms in food processing

Comparison of meta-heuristic algorithms for fuzzy modelling of COVID-19 illness’ severity classification

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