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CMSRAS: A Novel Chaotic Multi-Specular Reflection Optimization Algorithm Considering Shared Nodes
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DOI：10.1109/ACCESS.2021.3066180.png)
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Abstract
En 中文
Specular reflection algorithm (SRA) was a single population meta-heuristic algorithm inspired by the physical function of mirror. However, similar to most of meta-heuristic algorithms, it had the disadvantages of weak population diversity, stagnation in local optimal and low convergence rate. In order to overcome these shortcomings, a chaotic multi-specular reflection optimization algorithm considering shared nodes (CMSRAS) was proposed by the combination of population strategy with shared node, improved Tent chaos strategy and Gaussian mutation strategy. Initially, a single population SRA was extended to the multi-population with shared node and the population was initialized by improved Tent chaos sequence to improve the quality of the initial solution and the population diversity, and to enhance the global search ability. Meanwhile, to strengthen the local search ability and the convergence accuracy, the Gaussian mutation and improved Tent chaotic disturbance strategy were introduced into SRA. And then the influence law and sensitivity analysis of the CMSRAS algorithm between the initial setting parameters were obtained based on the response surface method and the Sobol's method. Finally, compared with both 12 state-of-the-art algorithms and 8 well-known advanced algorithms, the performance of CMSRAS was evaluated on a comprehensive set of 32 benchmark problems. In addition, CMSRAS was applied to solve the complex optimization problems of engineering structure. The results demonstrated that proposed CMSRAS algorithm outperformed most competitive algorithms and efficiently solve the real-life global optimization problems.
Keywords:
Optimization
Convergence
Statistics
Sociology
Search problems
Reflection
Heuristic algorithms
Specular reflection algorithm
chaotic multi-specular reflection optimization algorithm
sharing nodes
sensitivity analysis
Tent chaos
Gaussian mutation
real-life global optimization problems
Journal
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3.6
Papers:
9.8W
Citations:
29.4W