Social Rumor Propagation Optimization (SRPO): A Novel Metaheuristic Approach Inspired by Human Behaviour in Social Networks

This paper proposes a novel metaheuristic algorithm named Social Rumor Propagation Optimization (SRPO), inspired by the natural process of rumor spreading in social networks.SRPO models the human decisionmaking mechanisms involved in rumor disseminationnamely attractiveness of information, trust in the source, and individual influenceto effectively navigate complex and high-dimensional search spaces.These behavioral elements are mathematically formalized into the algorithm's propagation, evaluation, and update mechanisms, ensuring a dynamic balance between exploration and exploitation.The SRPO algorithm does not require parameter tuning, making it highly adaptable and user-friendly.Its exploration-exploitation strategy is governed by adaptive coefficients that shift the algorithm's focus from global search in early iterations to local intensification near optimal solutions in later stages.The algorithm's effectiveness is validated using the CEC 2017 benchmark suite, which includes 29 challenging functions from unimodal, multimodal, hybrid, and composite categories.SRPO is benchmarked against nine recent metaheuristic algorithms and ranks first in 28 of 29 test functions.This demonstrates its superior convergence behavior, robustness, and scalability.The proposed SRPO algorithm offers a promising and versatile optimization framework with broad potential for real-world engineering applications.Future work will focus on constrained and multi-objective extensions, theoretical convergence analysis, and deployment in practical domains such as energy systems, logistics, and biomedical optimization.