MOSSA: An Efficient Swarm Intelligent Algorithm to Solve Global Optimization and Carbon Fiber Drawing Process Problems

• Published in: IEEE internet of things journal Vol. 12; no. 9; pp. 11940 - 11953
• Main Authors: Xue, Jiankai, Zhang, Chenglong, Wang, Muming, Dong, Xuezhe
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; Carbon; Carbon fiber; Carbon fibers; Convergence; Genetic algorithms; Global optimization; Internet of Things; multiobjective optimization; Multiple objective analysis; Optimization; Pareto optimality; Pareto optimization; Particle swarm optimization; Search algorithms; Search methods; Search problems; Sorting; sparrow search algorithm (SSA); swarm intelligence; Vectors
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3518581

In this article, the sparrow search algorithm (SSA) is extended to the multiobjective SSA (MOSSA) with the purpose of efficiently solving the multiobjective optimization problems (MOPs). First, the MOSSA adaptively evaluates nondominated sparrow individuals stored in the external archive (EA) by using an adaptive mesh approach, which is utilized to obtain the best producer. Second, the scrounger sparrows adjust their trajectories according to the location of the best producer, called the scrounger follow strategy, which can improve the quality of the solutions when solving MOPs. Then, the proposed scouter search strategy is capable of maintaining population diversity and accelerate convergence. Moreover, the EA is pruned with the aim of avoiding the waste of computing resources. Extensive experiments with 22 benchmark examples validate the effectiveness of our approach against six state-of-the-art optimization approaches. Finally, the MOSSA is applied in the carbon fiber drawing process problems and the stretching parameters obtained by the MOSSA is reasonable.