DNA Sequence Optimization Design of Arithmetic Optimization Algorithm Based on Billiard Hitting Strategy

Xie, Linpeng; Wang, Siwei; Zhu, Donglin; Hu, Gangqiang; Zhou, Changjun

doi:10.1007/s12539-023-00559-x

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…The optimization standard deviation is the lowest in F(1), F(5), and F (7), indicating strong search stability. Additionally, RPSO outperforms the APGSK-IMODE algorithm on the F(1), F(5), and F (11) functions; the EA4eig algorithm on the F(11) function; and the IMODE algorithm on the F(3), F(4), F(5), F (7), F(8), F (10), and F (11) functions in terms of the optimal value. RPSO also outperforms the AGSK algorithm on the F(4), F(5), and F(11) functions.…”

Section: Comparison With the Top Algorithmmentioning

confidence: 98%

“…RPSO also outperforms the AGSK algorithm on the F(4), F(5), and F(11) functions. In terms of the worst value, RPSO outperforms the APGSK-IMODE algorithm on the F(1) and F(4) functions; the EA4eig algorithm on the F (10) and F (11) functions; and the IMODE algorithm on the F(4), F(5), F (7), F (10), and F (11) functions. RPSO also outperforms the PVADE algorithm on the F(9) and F (10) functions; and the AGSK algorithm on the F(1), F(4), F(5), and F(11) functions.…”

Section: Comparison With the Top Algorithmmentioning

confidence: 98%

“…PSO exhibits good learning ability through the interaction of individual information during the evolutionary process. Consequently, it effectively addresses numerous real-world issues, including energy systems [4], path planning [5], image processing [6], DNA computing [7], and engineering optimization [8]. Consequently, there is significant research interest in combining PSO with machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

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A Random Particle Swarm Optimization Based on Cosine Similarity for Global Optimization and Classification Problems

Liu,

Zeng,

et al. 2024

Biomimetics

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In today’s fast-paced and ever-changing environment, the need for algorithms with enhanced global optimization capability has become increasingly crucial due to the emergence of a wide range of optimization problems. To tackle this issue, we present a new algorithm called Random Particle Swarm Optimization (RPSO) based on cosine similarity. RPSO is evaluated using both the IEEE Congress on Evolutionary Computation (CEC) 2022 test dataset and Convolutional Neural Network (CNN) classification experiments. The RPSO algorithm builds upon the traditional PSO algorithm by incorporating several key enhancements. Firstly, the parameter selection is adapted and a mechanism called Random Contrastive Interaction (RCI) is introduced. This mechanism fosters information exchange among particles, thereby improving the ability of the algorithm to explore the search space more effectively. Secondly, quadratic interpolation (QI) is incorporated to boost the local search efficiency of the algorithm. RPSO utilizes cosine similarity for the selection of both QI and RCI, dynamically updating population information to steer the algorithm towards optimal solutions. In the evaluation using the CEC 2022 test dataset, RPSO is compared with recent variations of Particle Swarm Optimization (PSO) and top algorithms in the CEC community. The results highlight the strong competitiveness and advantages of RPSO, validating its effectiveness in tackling global optimization tasks. Additionally, in the classification experiments with optimizing CNNs for medical images, RPSO demonstrated stability and accuracy comparable to other algorithms and variants. This further confirms the value and utility of RPSO in improving the performance of CNN classification tasks.

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Section: Comparison With the Top Algorithmmentioning

confidence: 98%

Section: Comparison With the Top Algorithmmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Random Particle Swarm Optimization Based on Cosine Similarity for Global Optimization and Classification Problems

Liu,

Zeng,

et al. 2024

Biomimetics

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“…As one of the novel algorithms, AOA was initially applied to numerical optimization problems and engineering design problems. Due to its uncomplicated structure and excellent performance, AOA has covered many areas such as support vector regression (SVR) parameter optimization [ 2 ], tuning PID controllers [ 3 , 4 ], fuel cell parameter extraction [ 5 ], DNA sequence optimization design [ 6 ], clustering optimization [ 7 , 8 ], power system stabilizer design [ 9 ], feature selection [ 10 ], photovoltaic parameter optimization [ 11 , 12 , 13 ], robot path planning [ 14 ], wireless sensor network location and deployment [ 15 ], IoT workflow scheduling [ 16 ], image segmentation [ 17 ], etc.…”

Section: Introductionmentioning

confidence: 99%

A Multiple Mechanism Enhanced Arithmetic Optimization Algorithm for Numerical Problems

et al. 2023

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The Arithmetic Optimization Algorithm (AOA) is a meta-heuristic algorithm inspired by mathematical operators, which may stagnate in the face of complex optimization issues. Therefore, the convergence and accuracy are reduced. In this paper, an AOA variant called ASFAOA is proposed by integrating a double-opposite learning mechanism, an adaptive spiral search strategy, an offset distribution estimation strategy, and a modified cosine acceleration function formula into the original AOA, aiming to improve the local exploitation and global exploration capability of the original AOA. In the proposed ASFAOA, a dual-opposite learning strategy is utilized to enhance population diversity by searching the problem space a lot better. The spiral search strategy of the tuna swarm optimization is introduced into the addition and subtraction strategy of AOA to enhance the AOA’s ability to jump out of the local optimum. An offset distribution estimation strategy is employed to effectively utilize the dominant population information for guiding the correct individual evolution. In addition, an adaptive cosine acceleration function is proposed to perform a better balance between the exploitation and exploration capabilities of the AOA. To demonstrate the superiority of the proposed ASFAOA, two experiments are conducted using existing state-of-the-art algorithms. First, The CEC 2017 benchmark function was applied with the aim of evaluating the performance of ASFAOA on the test function through mean analysis, convergence analysis, stability analysis, Wilcoxon signed rank test, and Friedman’s test. The proposed ASFAOA is then utilized to solve the wireless sensor coverage problem and its performance is illustrated by two sets of coverage problems with different dimensions. The results and discussion show that ASFAOA outperforms the original AOA and other comparison algorithms. Therefore, ASFAOA is considered as a useful technique for practical optimization problems.

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The Construction of DNA Coding Sets by an Intelligent Optimization Algorithm: TMOL-TSO

Yan,

Wang,

et al. 2023

Communications in Computer and Information Science

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DNA Sequence Optimization Design of Arithmetic Optimization Algorithm Based on Billiard Hitting Strategy

Cited by 6 publications

References 29 publications

A Random Particle Swarm Optimization Based on Cosine Similarity for Global Optimization and Classification Problems

A Random Particle Swarm Optimization Based on Cosine Similarity for Global Optimization and Classification Problems

A Multiple Mechanism Enhanced Arithmetic Optimization Algorithm for Numerical Problems

The Construction of DNA Coding Sets by an Intelligent Optimization Algorithm: TMOL-TSO

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