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modified Archimedes optimization algorithm for global optimization problems: a comparative study

NEURAL COMPUTING & APPLICATIONS 2024年第14期36卷 8007-8038页

作者： Nurmuhammed, Mustafa Akdag, Ozan Karadag, Teoman Inonu Univ Malatya Malatya OIZ Vocat Sch Dept Elect & Energy Malatya Turkiye Malatya Turgut Ozal Univ Dept Comp Engn Malatya Turkiye Inonu Univ Dept Elect Elect Engn Malatya Turkiye

Archimedes optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes' Principle. In this study, a modified Archimedes optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly optimization Algorithm, Particle Swarm optimization Butterfly optimization Algorithm, Golden Jackal optimization, Whale optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.

关键词： Benchmark functions modified optimization algorithms optimization algorithms Swarm intelligence

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Modification of codebook search in adaptive multi-rate wideband speech codecs using intelligent optimization algorithms

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NEURAL COMPUTING & APPLICATIONS 2014年第3-4期24卷 911-926页

作者： Sheikhan, Mansour Islamic Azad Univ Fac Engn Dept Elect Engn South Tehran Branch Tehran Iran

In recognition of high-quality wideband speech codecs, several standardization activities have been conducted, resulting in the selection of a wideband speech codec called adaptive multi-rate wideband (AMR-WB). The algebraic code-excited linear prediction (ACELP) technique is recommended in AMR-WB, and it is noted that most of the complexity in the ACELP structure comes from the codebook search. In this paper, a new method is proposed for codebook search based on the behavior of backward filtered target signal, d(n), introduced in ITU-T G.722.2 recommendation. To optimize the proposed scheme, five optimization algorithms (i.e., modified genetic algorithm, particle swarm optimization with dynamic inertia weight, bee colony optimization, modified differential evolution, and imperialist competition algorithm) are investigated. Experimental results show that the reduction in codebook search operations of the proposed method is able to reach up to 59 percent as compared with ITU-T G.722.2 recommendation. Meanwhile, BCO-based codebook search scheme has better convergence speed without significant degradation in quality metrics, such as segmental signal-to-noise ratio, mean opinion score, and perceptual evaluation of speech quality, when used in an AMR-WB speech codec.

关键词： ACELP codebook search AMR-WB modified optimization algorithms MGA MDE ICA

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