Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Sahoo, Saroj Kumar; Houssein, Essam H.; Premkumar, M.; Saha, Apu Kumar; Emam, Marwa M.

doi:10.1016/j.eswa.2023.120367

Cited by 14 publications

(2 citation statements)

References 103 publications

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“…Similar scenarios were considered for the comparison, with each algorithm operating under the conditions of 1000 iterations and 30 dimensions. Specifically, the Fibonacci search-based Moth Flame Optimizer [42] and the Bacterial Foraging Optimization with Chaotic Chemotaxis Step Length, Gaussian Mutation, and Chaotic Local Search [43] were selected as the comparison algorithms. Remarkably, our proposed method exhibits a greater number of performance improvements in the presented results.…”

Section: First Study Casementioning

confidence: 99%

Enhancing Dynamic Parameter Adaptation in the Bird Swarm Algorithm Using General Type-2 Fuzzy Analysis and Mathematical Functions

Miramontes,

Melin

2023

Axioms

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The pursuit of continuous improvement across diverse processes presents a pressing challenge. Precision in manufacturing, efficient delivery route planning, and accurate diagnostics are imperative, prompting the exploration of innovative solutions. Nature-inspired algorithms offer a pathway for enhancing these processes. In this study, we address this challenge by dynamically adapting parameters in the Bird Swarm Algorithm using General Type-2 Fuzzy Systems, encompassing a range of rules and membership functions. Two complex case studies validate the effectiveness of our approach. The first evaluates Congress of Evolutionary Competition 2017 functions, while the second tackles the intricacies of Congress of Evolutionary Competition 2019 functions. Our methodology achieves an 97% improvement for Congress of Evolutionary Competition 2017 functions and a significant 70% enhancement for Congress of Evolutionary Competition 2019 functions. Notably, our results are benchmarked against the original method. Crucially, rigorous statistical analysis underscores the significant advancements facilitated by our proposed method. The comparison demonstrates clear and statistically significant improvements over the original approach. This study proves the marked impact of integrating General Type-2 Fuzzy Systems into the Bird Swarm Algorithm, presenting a promising avenue for addressing intricate optimization challenges in diverse domains.

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Section: First Study Casementioning

confidence: 99%

Enhancing Dynamic Parameter Adaptation in the Bird Swarm Algorithm Using General Type-2 Fuzzy Analysis and Mathematical Functions

Miramontes,

Melin

2023

Axioms

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“…Another important technique in this group is the Ant Colony Optimization (ACO) algorithm, which is motivated by the direction of the food source of ants seeking behaviour from the colony 22 . Krill Herd (KH) 23 , Cuckoo Search Algorithm (CSA) 24 , Ant Lion Optimizer (ALO) 25 , Artificial Bee Colony (ABC) 26 , 27 , Bat Algorithm (BA) 28 , Firefly Algorithm (FA) 29 , 30 , Grey-Wolf Optimizer (GWO) 31 , 32 , Manta-Ray Foraging Optimization (MRFO) 33 , Salp Swarm Optimization (SSA) 34 , artificial rabbit optimizer 35 , Whale Optimization Algorithm (WOA) 36 , moth flame optimization 37 , and Marine-Predator Algorithm (MPA) 38 are few examples of swarm-based algorithms that are recently reported.…”

Section: Introductionmentioning

confidence: 99%

Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

Ravichandran,

Manoharan,

Jangir

et al. 2023

Sci Rep

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The primary objective of this study is to delve into the application and validation of the Resistance Capacitance Optimization Algorithm (RCOA)—a new, physics-inspired metaheuristic optimization algorithm. The RCOA, intriguingly inspired by the time response of a resistance–capacitance circuit to a sudden voltage fluctuation, has been earmarked for solving complex numerical and engineering design optimization problems. Uniquely, the RCOA operates without any control/tunable parameters. In the first phase of this study, we evaluated the RCOA's credibility and functionality by deploying it on a set of 23 benchmark test functions. This was followed by thoroughly examining its application in eight distinct constrained engineering design optimization scenarios. This methodical approach was undertaken to dissect and understand the algorithm's exploration and exploitation phases, leveraging standard benchmark functions as the yardstick. The principal findings underline the significant effectiveness of the RCOA, especially when contrasted against various state-of-the-art algorithms in the field. Beyond its apparent superiority, the RCOA was put through rigorous statistical non-parametric testing, further endorsing its reliability as an innovative tool for handling complex engineering design problems. The conclusion of this research underscores the RCOA's strong performance in terms of reliability and precision, particularly in tackling constrained engineering design optimization challenges. This statement, derived from the systematic study, strengthens RCOA's position as a potentially transformative tool in the mathematical optimization landscape. It also paves the way for further exploration and adaptation of physics-inspired algorithms in the broader realm of optimization problems.

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Multi-objective quasi-reflection learning and weight strategy-based moth flame optimization algorithm

Sahoo,

Premkumar,

Saha

et al. 2023

Neural Comput & Applic

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Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Cited by 14 publications

References 103 publications

Enhancing Dynamic Parameter Adaptation in the Bird Swarm Algorithm Using General Type-2 Fuzzy Analysis and Mathematical Functions

Enhancing Dynamic Parameter Adaptation in the Bird Swarm Algorithm Using General Type-2 Fuzzy Analysis and Mathematical Functions

Resistance–capacitance optimizer: a physics-inspired population-based algorithm for numerical and industrial engineering computation problems

Multi-objective quasi-reflection learning and weight strategy-based moth flame optimization algorithm

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