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CODEIVYA: An Enhanced Ivy Algorithm for Engineering Optimization
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Abstract
En 中文
The Ivy Algorithm is a novel metaheuristic devised by researchers based on the growth characteristics of ivy plants. It has become popular for its simple structure and strong global search ability, yet it also suffers from shortcomings such as insufficient population diversity and weak local search capability. To overcome the above shortcomings, this paper proposes an enhanced Ivy Algorithm (CODEIVYA). First, opposition-based learning is introduced to supplement diversity at a low cost. It periodically opens new regions without disturbing superior individuals, thereby preventing population degradation. Second, because the original algorithm relies excessively on the local neighborhood to determine the search direction, a differential evolution strategy is incorporated. Third, an adaptive cauchy local search is employed to address the difficulty of tuning step sizes in the middle and later stages of the original algorithm. Finally, CODEIVYA is compared with five other competing algorithms on the CEC2022 test functions and three actual engineering applications. The test results and experimental analysis show that CODEIVYA outperforms the competing algorithms and has stronger engineering application capabilities.
Keywords:
Ivy Algorithm
opposition-based learning
differential evolution
adaptive cauchy local search
Journal
E
IF:
0.6
Papers:
149
Citations:
0