2022
DOI: 10.1109/access.2022.3223388
|View full text |Cite
|
Sign up to set email alerts
|

Giant Trevally Optimizer (GTO): A Novel Metaheuristic Algorithm for Global Optimization and Challenging Engineering Problems

Abstract: Metaheuristic algorithms are becoming powerful methods for solving continuous global optimization and engineering problems due to their flexible implementation on the given problem. Most of these algorithms draw their inspiration from the collective intelligence and hunting behavior of animals in nature. This paper proposes a novel metaheuristic algorithm called the Giant Trevally Optimizer (GTO). In nature, giant trevally feeds on many animals, including fish, cephalopods, and seabirds (sooty terns). In this … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
9
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
7
1

Relationship

0
8

Authors

Journals

citations
Cited by 70 publications
(31 citation statements)
references
References 64 publications
0
9
0
Order By: Relevance
“…Moth-Flame Optimization (MFO) [54], Whale Optimization Algorithm (WOA) [82], [83], Multi-Verse Optimizer (MVO) [34],Sine Cosine Algorithm (SCA) [78], Equilibrium Optimizer (EO) [83], Henry Gas Solubility Optimization (HGSO) [76], Sea-Horse optimizer (SHO) [77], Artiőcial hummingbird algorithm [84], Reptile Search Algorithm (RSA) [55], and Dragonŕy algorithm [85] Dandelion Optimizer (DO) [86], Geometric Mean Optimizer (GMO) [87], Grey Wolf Optimizer (GWO) [88], Ant Lion Optimizer (ALO) [45], Arithmetic Optimization Algorithm (AOA) [89], White Shark Optimizer (WSO) [90], Golden Jackal Optimization (GJO) [91], Red-Tailed Hawk Algorithm (RTH) [92], Mountain Gazelle Optimizer (MGO) [93], Flow Direction Algorithm (FDA) [94], Giant Trevally Optimizer (GTO) [95], Grasshopper Optimization Algorithm (GOA) [96].…”
Section: Evaluation Of Various Optimization Algorithmsmentioning
confidence: 99%
“…Moth-Flame Optimization (MFO) [54], Whale Optimization Algorithm (WOA) [82], [83], Multi-Verse Optimizer (MVO) [34],Sine Cosine Algorithm (SCA) [78], Equilibrium Optimizer (EO) [83], Henry Gas Solubility Optimization (HGSO) [76], Sea-Horse optimizer (SHO) [77], Artiőcial hummingbird algorithm [84], Reptile Search Algorithm (RSA) [55], and Dragonŕy algorithm [85] Dandelion Optimizer (DO) [86], Geometric Mean Optimizer (GMO) [87], Grey Wolf Optimizer (GWO) [88], Ant Lion Optimizer (ALO) [45], Arithmetic Optimization Algorithm (AOA) [89], White Shark Optimizer (WSO) [90], Golden Jackal Optimization (GJO) [91], Red-Tailed Hawk Algorithm (RTH) [92], Mountain Gazelle Optimizer (MGO) [93], Flow Direction Algorithm (FDA) [94], Giant Trevally Optimizer (GTO) [95], Grasshopper Optimization Algorithm (GOA) [96].…”
Section: Evaluation Of Various Optimization Algorithmsmentioning
confidence: 99%
“…Finally, some existing algorithms were selected for comparison with IDPGA. These algorithms performed well in solving all or some of these six problems, including SC (society and civilization) [71], PSO-DE (a novel hybrid algorithm) [72], DEDS (differential evolution with dynamic stochastic selection for constrained optimization) [73], HEAA (constrained optimization based on hybrid evolutionary algorithm and adaptive constrainthandling technique) [74], CS (cuckoo search algorithm) [16], AHA (artificial hummingbird algorithm) [75], GA2 (using co-evolution to adapt the penalty factors of a fitness function incorporated in a genetic algorithm) [76], GA3 (a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm) [77], CA (a cultural algorithm that uses domain knowledge) [78], CPSO (a co-evolutionary particle swarm optimization approach) [79], ABC (artificial bee colony) [80], GA5 (a new approach to handle constraints using evolutionary algorithms in which the new technique treats constraints as objectives and uses a multiobjective optimization approach to solve the re-stated singleobjective optimization problem) [81], GeneAS I (genetic adaptive search I) [82], GeneAS II (genetic adaptive search II) [76], DMO (dwarf mongoose optimization algorithm) [83], AOA (the arithmetic optimization algorithm) [84], SSA (squirrel search algorithm) [85], SCA (sine cosine algorithm) [86], GWO (grey wolf optimizer) [87], CPSOGSA (hybrid constriction coefficient based PSO with gravitational search algorithm (GSA)) [83], Hsu and Liu's algorithm [88], Rao's algorithm [89], GSA-GA (a new hybrid GSA-GA algorithm) [90], AO (aquila optimizer) [12], GTO (giant trevally optimizer) [13] and TLCO (termite life cycle optimizer) [14]. The best simulation results of IDPGA and other results reported in the current literature are listed in Table 9.…”
Section: Comparison With Other Algorithmsmentioning
confidence: 99%
“…In recent years, metaheuristic algorithms have developed rapidly, including genetic algorithms [1], simulated annealing algorithms [2], particle swarm algorithms [3], neural networks [4], ant colony algorithms [5], artificial fish-swarm algorithms [6], firefly algorithms [7], cuckoo search [8], tree growth algorithms [9], interactive fuzzy algorithms [10], chaos game algorithms [11], aquila optimizer [12], giant trevally optimizer [13] and termite life cycle optimizer [14]. Metaheuristic algorithms do not need information about firstorder Jacobian matrix or second-order Hessian matrix and break through the traditional mathematical programming algorithms based on gradients.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…In light of this, this study offers a recently developed giant trevally optimizer (GTO) [30] for tackling the OACB issue while taking into account realistic load models, which are not specifically addressed in the literature. The goal of the target function is to reduce the running costs, active power loss, voltage profile, and VSI of CBs.…”
Section: Introductionmentioning
confidence: 99%