A Novel Variant of Moth Flame Optimizer for Higher Dimensional Optimization Problems

Sahoo, Saroj Kumar; Sharma, Sushmita; Saha, Apu Kumar

doi:10.1007/s42235-023-00357-7

Cited by 11 publications

(2 citation statements)

References 87 publications

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“…The swarm-based MAs have been getting more attention in the last years due to the availability of inspiration sources and their efficacy in resolving various optimization problems. New papers propose or review this category of optimization algorithms, such as Mountain Gazelle Optimizer (MGO) 17 , Advances in Spotted Hyena Optimizer 40 , Advances in Tree Seed Algorithm 41 , Advances in Sparrow Search Algorithm 42 , Butterfly Optimization Algorithm (BOA) 43 , Advanced Butterfly Optimization Algorithm (ABOA) 44 , Modified Butterfly Optimization Algorithm with Lagrange Interpolation (MBOALI) 45 , African Vultures Optimization Algorithm (AVOA) 16 , Whale optimization algorithm (WOA) 46 , Artificial Gorilla Troops Optimizer (GTO) 18 , COOT algorithm 19 , Weibull Flight based Moth Flame Optimization (WF-MFO) 47 , Hunger Games Search (HGS) 20 , Aquila Optimizer (AO) 21 and so on. On the other hand, Quantum-inspired metaheuristic algorithms developed by combining Quantum Computing (QC) principles into metaheuristic algorithms are gaining more interest 48 .…”

Section: Related Workmentioning

confidence: 99%

Red-tailed hawk algorithm for numerical optimization and real-world problems

Ferahtia,

Houari,

Rezk

et al. 2023

Sci Rep

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This study suggests a new nature-inspired metaheuristic optimization algorithm called the red-tailed hawk algorithm (RTH). As a predator, the red-tailed hawk has a hunting strategy from detecting the prey until the swoop stage. There are three stages during the hunting process. In the high soaring stage, the red-tailed hawk explores the search space and determines the area with the prey location. In the low soaring stage, the red-tailed moves inside the selected area around the prey to choose the best position for the hunt. Then, the red-tailed swings and hits its target in the stooping and swooping stages. The proposed algorithm mimics the prey-hunting method of the red-tailed hawk for solving real-world optimization problems. The performance of the proposed RTH algorithm has been evaluated on three classes of problems. The first class includes three specific kinds of optimization problems: 22 standard benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions, IEEE Congress on Evolutionary Computation 2020 (CEC2020), and IEEE CEC2022. The proposed algorithm is compared with eight recent algorithms to confirm its contribution to solving these problems. The considered algorithms are Farmland Fertility Optimizer (FO), African Vultures Optimization Algorithm (AVOA), Mountain Gazelle Optimizer (MGO), Gorilla Troops Optimizer (GTO), COOT algorithm, Hunger Games Search (HGS), Aquila Optimizer (AO), and Harris Hawks optimization (HHO). The results are compared regarding the accuracy, robustness, and convergence speed. The second class includes seven real-world engineering problems that will be considered to investigate the RTH performance compared to other published results profoundly. Finally, the proton exchange membrane fuel cell (PEMFC) extraction parameters will be performed to evaluate the algorithm with a complex problem. The proposed algorithm will be compared with several published papers to approve its performance. The ultimate results for each class confirm the ability of the proposed RTH algorithm to provide higher performance for most cases. For the first class, the RTH mostly got the optimal solutions for most functions with faster convergence speed. The RTH provided better performance for the second and third classes when resolving the real word engineering problems or extracting the PEMFC parameters.

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Section: Related Workmentioning

confidence: 99%

Red-tailed hawk algorithm for numerical optimization and real-world problems

Ferahtia,

Houari,

Rezk

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In recent years, meta-heuristic algorithms are applied for solving complex problems in different applications such as optimization of weight and cost of cantilever retaining wall 70 , multi-response machining processes 71 , symbiosis organisms search for global optimization and image segmentation 72 , human social learning intelligence 73 , nanotubular halloysites in weathered pegmatites 74 , numerical optimization and real-world applications 75 , convergence analysis 76 , higher Dimensional Optimization Problems 77 , non-dominated sorting advanced 78 , Lagrange Interpolation 79 . LCA is quite different from the existing meta-heuristic algorithms although it belongs to the category of human-based meta-heuristics.…”

Section: Introductionmentioning

confidence: 99%

Learning cooking algorithm for solving global optimization problems

Gopi,

Mohapatra

2024

Sci Rep

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In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of humans in order to solve challenging problems. The LCA strategy is primarily motivated by observing how mothers and children prepare food. The fundamental idea of the LCA strategy is mathematically designed in two phases: (i) children learn from their mothers and (ii) children and mothers learn from a chef. The performance of the proposed LCA algorithm is evaluated on 51 different benchmark functions (which includes the first 23 functions of the CEC 2005 benchmark functions) and the CEC 2019 benchmark functions compared with state-of-the-art meta-heuristic algorithms. The simulation results and statistical analysis such as the t-test, Wilcoxon rank-sum test, and Friedman test reveal that LCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the LCA algorithm has been employed to solve seven real-world engineering problems, such as the tension/compression spring design, pressure vessel design problem, welded beam design problem, speed reducer design problem, gear train design problem, three-bar truss design, and cantilever beam problem. The results demonstrate the LCA’s superiority and capability over other algorithms in solving complex optimization problems.

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An Amended Moth Flame Optimization Algorithm Based on Fibonacci Search Approach for Solving Engineering Design Problems

Sahoo

Saha

2023

Mathematics and Computer Science Volume 2

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A Novel Variant of Moth Flame Optimizer for Higher Dimensional Optimization Problems

Cited by 11 publications

References 87 publications

Red-tailed hawk algorithm for numerical optimization and real-world problems

Red-tailed hawk algorithm for numerical optimization and real-world problems

Learning cooking algorithm for solving global optimization problems

An Amended Moth Flame Optimization Algorithm Based on Fibonacci Search Approach for Solving Engineering Design Problems

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