Opposition-Based Learning Slime Mold Algorithm of Dynamic Nonlinear Parameters
In response to the low convergence accuracy,propensity for local optima,and slow convergence speed of the slime mold algorithm,a dynamic nonlinear parameter opposition-based learning slime mold algorithm is proposed.By using a opposition-based learning strategy to enrich population diversity and ob-tain a better initial population,the algorithm's optimization performance and convergence speed are im-proved.A dynamic nonlinear decreasing strategy is introduced to dynamically adjust the slime mold search area,to coordinate global exploration and local development to enhance the algorithm's ability to avoid lo-cal optima and to improve convergence accuracy.Experimental comparisons between different algorithms are conducted by using several benchmark test functions.The results show that the improved algorithm has stronger optimization characteristics and faster convergence speed,with varying degrees of improvement in convergence accuracy.Finally,the reliability and effectiveness of the improved algorithm in practical ap-plication problems are validated through two engineering design problems.
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