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NOA-RAC: An Enhanced Nutcracker Optimization Algorithm for Optimization Tasks
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DOI：10.1002/cpe.70717.png)
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Abstract
En 中文
Most real-world problems are constrained continuous variable problems and discrete variable problems. In order to develop an algorithm that can solve these two types of problems in a balanced way, this paper proposes NOA-RAC, an enhanced variant of the Nutcracker Optimization Algorithm (NOA). To address the limitations of NOA, including the exploration-exploitation imbalance and tendency to fall into local optima in certain cases, three enhancement strategies were implemented. First, the implementation of a random subgroup strategy to better balance exploration-exploitation trade-offs. Second, the development of an adaptive fitness update mechanism that enhances population diversity. Finally, incorporation of a retractable transformable cruise strategy improves the algorithm's ability to jump out of local optima. A comprehensive experimental analysis, including effectiveness analysis of improvement strategies, qualitative analysis, non-parametric statistical test, and so forth, was conducted to validate the results of the algorithmic improvements from multiple perspectives. NOA-RAC was quantitatively compared with well-known algorithms of various types proposed in recent years in three tests (CEC2017 benchmark suite, 30 engineering problems, and 12 feature selection problems). Experimental results demonstrate that NOA-RAC exhibits strong competitiveness in solving both discrete-variable and constrained continuous-variable optimization problems, it serves as an effective tool for addressing real-world optimization problems.
Keywords:
engineering design problems
enhanced nutcracker optimizer
feature selection problems
numerical optimization
strategy improvement
Journal
C
IF:
1.5
Papers:
439
Citations:
0