Optimization of Jacobi Moments Parameters using Artificial Bee Colony Algorithm for 3D Image Analysis

Bencherqui, Ahmed; Karmouni, Hicham; Daoui, Achraf; Alfidi, Mohammed; Qjidaa, Hassan; Sayyouri, Mhamed

doi:10.1109/icds50568.2020.9268736

Cited by 8 publications

(1 citation statement)

References 17 publications

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“…The ongoing evolution of the field of global optimization has seen the emergence of several new metaheuristic algorithms, each bringing its own innovations for improving computational efficiency, solving large-scale problems, and implementing robust optimization codes. Among these newcomers, ABC (Artificial Bee Colony) has adopted an approach that simulates the behavior of bees in their search for nectar [6]. Cuckoo search (CS) was inspired by the brooding behavior of certain birds to explore the search space [7].…”

Section: Introductionmentioning

confidence: 99%

Chaos-Enhanced Archimede Algorithm for Global Optimization of Real-World Engineering Problems and Signal Feature Extraction

Bencherqui,

Tahiri,

Karmouni

et al. 2024

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Optimization algorithms play a crucial role in a wide range of fields, from designing complex systems to solving mathematical and engineering problems. However, these algorithms frequently face major challenges, such as convergence to local optima, which limits their ability to find global, optimal solutions. To overcome these challenges, it has become imperative to explore more efficient approaches by incorporating chaotic maps within these original algorithms. Incorporating chaotic variables into the search process offers notable advantages, including the ability to avoid local minima, diversify the search, and accelerate convergence toward optimal solutions. In this study, we propose an improved Archimedean optimization algorithm called Chaotic_AO (CAO), based on the use of ten distinct chaotic maps to replace pseudorandom sequences in the three essential components of the classical Archimedean optimization algorithm: initialization, density and volume update, and position update. This improvement aims to achieve a more appropriate balance between the exploitation and exploration phases, offering a greater likelihood of discovering global solutions. CAO performance was extensively validated through the exploration of three distinct groups of problems. The first group, made up of twenty-three benchmark functions, served as an initial reference. Group 2 comprises three crucial engineering problems: the design of a welded beam, the modeling of a spring subjected to tension/compression stresses, and the planning of pressurized tanks. Finally, the third group of problems is dedicated to evaluating the efficiency of the CAO algorithm in the field of signal reconstruction, as well as 2D and 3D medical images. The results obtained from these in-depth tests revealed the efficiency and reliability of the CAO algorithm in terms of convergence speeds, and outstanding solution quality in most of the cases studied.

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Section: Introductionmentioning

confidence: 99%