A new specular reflection optimization algorithm

Qi, Qisong; Xu, Gening; Fan, Xiaohui; Jun, Wang

doi:10.1177/1687814015610475

Cited by 12 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Introduction to M-SRA M-SRA is a new type of hybrid intelligent optimization algorithm, which is evolved from the Specular Reflection Algorithm. 41,42 Early versions of various swarm intelligence algorithms usually occur premature, and the best results can be achieved by making certain corrections or combining with other algorithms. Like the Specular Reflection Algorithm (SRA), the principle of M-SRA is to simulate the reflection phenomenon of light from the mirror surface, and the observer finds the target object through this natural phenomenon.…”

Section: Multi-specular Reflection Algorithm (M-sra)mentioning

confidence: 99%

Lightweight and green design of general bridge crane structure based on multi- specular reflection algorithm

Dong

et al. 2021

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

At present, there are some problems that need to be solved in crane steel structure design, such as heavy self-weight of finished structure, high production energy consumption, and huge manufacturing costs. In view of this, a lightweight design method that meets the green product design principle is proposed for crane structure. In this method, an improved new swarm intelligence optimization algorithm named the multi-specular reflection algorithm (M-SRA) is proposed. This algorithm combines the specular reflection algorithm (SRA) with the traditional swarm intelligence optimization algorithm, which has higher computational efficiency. After that, M-SRA is applied to crane steel structure optimization. The results show that the improved algorithm has higher global searching ability and computational efficiency than traditional population intelligence optimization algorithms. The research in this paper provides a reliable design method for the green lightweight design of crane steel structure and a reference for the green design of mechanical products.

show abstract

Section: Multi-specular Reflection Algorithm (M-sra)mentioning

confidence: 99%

Lightweight and green design of general bridge crane structure based on multi- specular reflection algorithm

Dong

et al. 2021

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…The SRA 21,22 is an evolutionary optimization method proposed by the author representing a recent research achievement in the field of intelligent optimization. This method uses a non-population search strategy that is different from the general swarm-type intelligence optimization algorithms, such as the GA, [23][24][25][26] particle swarm optimization algorithm, [27][28][29] and simulated annealing algorithm.…”

Section: Sramentioning

confidence: 99%

Robust optimization design of structures based on the specular reflection algorithm

Dong

Xin

2019

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

The nominal values of structural design parameters are usually calculated using a traditional deterministic optimization design method. However, owing to the failure of this type of method to consider potential variations in design parameters, the theoretical design results can be far from reality. To address this problem, the specular reflection algorithm, a recent advancement in intelligence optimization, is used in conjunction with a robust design method based on sensitivity. This method not only is able to fully consider the influence of parameter uncertainty on the design results but also has strong applicability. The effectiveness of the proposed method is verified by numerical examples, and the results show that the robust design method can significantly improve the reliability of the structure.

show abstract

“…The PBAs usually simulate the physical laws underlying a wide range of natural phenomena, including electromagnetic force, inertial force, light diffraction, reflection, and so on. The few of the famed PBAs are gravitational search algorithm (GSA) [33], specular reflection optimization algorithm (SRA) [34], chaotic multi-specular reflection optimization algorithm considering shared nodes (CMSRAS) [35], equilibrium optimizer (EO) [36], Young's double-slit experiment optimizer (YSDE) [37], Kepler optimization algorithm (KOA) [38], light spectrum optimizer (LSO) [39], Fick's Law Algorithm (FLA) [40], and multi-verse optimizer (MVO) [41], and Turbulent Flow of Water-based Optimization (TFWO) [42].…”

Section: Introductionmentioning

confidence: 99%

IMSCSO: An Intensified Sand Cat Swarm Optimization With Multi-Strategy for Solving Global and Engineering Optimization Problems

Li,

Qi,

Xing

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Optimization challenges are becoming more complex as the world advances. Since deterministic and heuristic approaches are no longer sufficient to deal with such complex problems, metaheuristics have recently emerged as a viable option to address optimization difficulties. Since Sand Cat Swarm Optimization (SCSO) is a famous meta-heuristic algorithm, SCSO has a weak ability to balance search between exploration and exploitation and slow convergence, so it may not be effective in finding the global optima, particularly for complex problems. Hence, this paper proposes an intensified SCSO with multiple strategies (IMSCSO). The performance of the IMSCSO algorithm was evaluated on 23 standard test functions and test suites of CEC 2017, CEC 2019, and CEC 2020. Experimental results show that the IMSCSO algorithm performs significantly better than or is on par with other state-of-the-art optimizers. The statistical results obtained from the Wilcoxon signed-rank test and the Friedman test also indicate that the IMSCSO algorithm has a high ability to significantly outperform and rank first among all methods. Moreover, seven typical engineering issues were employed to estimate the efficacy of IMSCSO in optimizing constrained problems. The experimental findings show that the suggested IMSCSO method can efficiently handle real-world application issues.INDEX TERMS Sand cat swarm optimization, hybrid opposition-based learning, joint opposite selection, benchmark functions.

show abstract

A new specular reflection optimization algorithm

Cited by 12 publications

References 20 publications

Lightweight and green design of general bridge crane structure based on multi- specular reflection algorithm

Lightweight and green design of general bridge crane structure based on multi- specular reflection algorithm

Robust optimization design of structures based on the specular reflection algorithm

IMSCSO: An Intensified Sand Cat Swarm Optimization With Multi-Strategy for Solving Global and Engineering Optimization Problems

Contact Info

Product

Resources

About