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VOL. 13, ISSUE 3 (2026)
Stress-adaptive mutation operator for enhanced multimodal optimization in Phasmatodea population evolution algorithm
Authors
Lim Eng Aik, Tan Wee Choon
Abstract
This paper proposed a stress-adaptive mutation
operator to enhance the multimodal optimization capability of the Phasmatodea
Population Evolution Algorithm, addressing the challenge of balancing
exploration and exploitation in dynamic environments. The proposed method
dynamically adjusts the mutation rate based on a stress factor, which
integrates fitness diversity and iteration-dependent decay, thereby enabling
adaptive control over the algorithm’s search behavior. Early in the
optimization process, when population diversity is high, the mutation rate
increases to promote exploration of the solution space; conversely, as diversity
diminishes or iterations progress, the rate decreases to prioritize local
refinement. This mechanism ensures robust performance across multimodal
landscapes, such as those encountered in stock price prediction, where
identifying multiple optima is critical. The novelty lies in the integration of
a stress factor that systematically modulates mutation intensity without
relying on static parameters, offering a more responsive and scalable approach
compared to conventional fixed-rate strategies. Experimental validation
demonstrates that the adaptive operator significantly improves convergence
accuracy and solution diversity, particularly in complex optimization
scenarios. Furthermore, the method maintains computational efficiency, making
it suitable for real-world applications where both precision and adaptability
are essential. The results highlight the operator’s potential to advance
population-based optimization techniques, providing a flexible framework for
solving multimodal problems across various domains.
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Pages:182-188
How to cite this article:
Lim Eng Aik, Tan Wee Choon "Stress-adaptive mutation operator for enhanced multimodal optimization in Phasmatodea population evolution algorithm". International Journal of Multidisciplinary Research and Development, Vol 13, Issue 3, 2026, Pages 182-188
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