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Chaotic opposition-based plant propagation algorithm for engineering problem
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AI
DOI：10.1007/s10489-025-06320-9.png)
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Abstract
En 中文
The Plant Propagation Algorithm (PPA), often exemplified by the Strawberry Algorithm, has demonstrated its effectiveness in solving lower-dimensional optimization problems as a neighborhood search algorithm. While multiple enhancements have been introduced to boost its performance, PPA remains a population-based metaheuristic algorithm. A key element of PPA involves balancing exploration and exploitation, akin to a strawberry plant seeking the best survival strategy. This paper delves into the integration of chaotic numbers and opposition theory in PPA, focusing on how these additions impact its efficiency. The primary research questions revolve around enhancing PPA's performance and reducing its search space to expedite the algorithm, ultimately leading to faster overall results. Experiments were carried out on three challenging engineering problems: the Pressure Vessel Optimization, the Spring Design Optimization, and the Welded Beam Problem, to fully assess the effectiveness of the improved PPA. The effectiveness of the original PPA, the Chaotic Opposition-Based PPA (COPPA), and several other metaheuristic algorithms were examined in each of these problems. In terms of efficiency and solution quality, the findings consistently demonstrate that COPPA performs better than the traditional PPA and other algorithms. The results indicate that using chaotic-based oppositional processes decreases the search space and enhances performance, resulting in faster and more resource-efficient optimization. The investigation reveals that incorporating chaotic-based oppositional PPA yields improved results while conserving resources and accelerating execution.
Keywords:
Plant propagation algorithm
Optimization
Chaos theory
Opposition theory
Search space reduction
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