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- 저자 : Hussien Abdelazim G (검색결과 3 건)
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An enhanced dynamic differential annealed algorithm for global optimization and feature selection
Hussien Abdelazim G,Kumar Sumit,Singh Simrandeep,Pan Jeng-Shyang,Hashim Fatma A 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.1
Dynamic differential annealed optimization (DDAO) is a recently developed physics-based metaheuristic technique that mimics the classical simulated annealing mechanism. However, DDAO has limited search abilities, especially when solving complicated and complex problems. A unique variation of DDAO, dubbed as mDDAO, is developed in this study, in which opposition-based learning technique and a novel updating equation are combined with DDAO. mDDAO is tested on 10 different functions from CEC2020 and compared with the original DDAO and nine other algorithms. The proposed mDDAO algorithm performance is evaluated using 10 numerical constrained functions from the recently released CEC 2020 benchmark suite, which includes a variety of dimensionally challenging optimisation tasks. Furthermore, to measure its viability, mDDAO is employed to solve feature selection problems using fourteen UCI datasets and a real-life Lymphoma diagnosis problem. Results prove that mDDAO has a superior performance and consistently outperforms counterparts across benchmarks, achieving fitness improvements ranging from 1% to 99.99%. In feature selection, mDDAO excels by reducing feature count by 23% to 79% compared to other methods, enhancing computational efficiency and maintaining classification accuracy. Moreover, in lymphoma diagnosis, mDDAO demonstrates up to 54% higher average fitness, 18% accuracy improvement, and 86% faster computation times.
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A multi-strategy enhanced African vultures optimization algorithm for global optimization problems
Zheng Rong,Hussien Abdelazim G,Qaddoura Raneem,Jia Heming,Abualigah Laith,Wang Shuang,Saber Abeer 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1
The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic inspired by the African vultures’ behaviors. Though the basic AVOA performs very well for most optimization problems, it still suffers from the shortcomings of slow convergence rate and local optimal stagnation when solving complex optimization tasks. Therefore, this study introduces a modified version named enhanced AVOA (EAVOA). The proposed EAVOA uses three different techniques namely representative vulture selection strategy, rotating flight strategy, and selecting accumulation mechanism, respectively, which are developed based on the basic AVOA. The representative vulture selection strategy strikes a good balance between global and local searches. The rotating flight strategy and selecting accumulation mechanism are utilized to improve the quality of the solution. The performance of EAVOA is validated on 23 classical benchmark functions with various types and dimensions and compared to those of nine other state-of-the-art methods according to numerical results and convergence curves. In addition, three real-world engineering design optimization problems are adopted to evaluate the practical applicability of EAVOA. Furthermore, EAVOA has been applied to classify multi-layer perception using XOR and cancer datasets. The experimental results clearly show that the EAVOA has superiority over other methods.
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Wang Shuang,Hussien Abdelazim G,Kumar Sumit,AlShourbaji Ibrahim,Hashim Fatma A 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6
This paper introduces an Improved Smell Agent Optimization Algorithm (mSAO), a new and enhanced metaheuristic designed to tackle complex engineering optimization issues by overcoming the shortcomings of the recently introduced Smell Agent Optimization Algorithm. The proposed mSAO incorporates the jellyfish swarm active–passive mechanism and novel random operator in the elementary SAO. The objective of modification is to improve the global convergence speed, exploration–exploitation behaviour, and performance of SAO, as well as provide a problem-free method of global optimization. For numerical validation, the mSAO is examined using 29 IEEE benchmarks with varying degrees of dimensionality, and the findings are contrasted with those of its basic version and numerous renowned recently developed metaheuristics. To measure the viability of the mSAO algorithm for real-world applications, the algorithm was employed to solve to resolve eight challenges drawn from real-world scenarios including cantilever beam design, multi-product batch plant, industrial refrigeration system, pressure vessel design, speed reducer design, tension/compression spring, and three-bar truss problem. The computational analysis demonstrates the robustness of mSAO relatively in finding optimal solutions for mechanical, civil, and industrial design problems. Experimental results show that the suggested modifications lead to an improvement in solution quality by 10–20% of basic SAO while solving constraint benchmarks and engineering problems. Additionally, it contributes to avoiding local optimal stuck, and premature convergence limitations of SAO and simultaneously.
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