Exploratory differential ant lion-based optimization

Abstract In this work, an improved alternative method of the ant lion optimizer (ALO), integrating opposition-based training with two practical operators on the basis of differential evolution, named MALO, is proposed to cope with the implied weaknesses of classical ALO. Firstly, opposition-based practice is adopted into the ALO to prevent it from the searching deflation and obtain a faster convergence rate. Besides, two more operators, mutation and crossover strategies are implemented to further improve the local searching efficiency of the agents. Additionally, to verify the effectiveness of the enhanced process, comparison with existing optimizers was conducted for different benchmark functions with different qualities likewise unimodal, multimodal, and fixed-dimensional multimodaltasks were also carried out. Moreover, the extensibility test is, undertaken to assess the dimensional influence on problem consistency and optimization quality. Furthermore, the enhanced method is exploited to crack three practical, well-known constrained optimization problems, including spring plan, the concern of the welded beam case and the subject of a pressure vessel. The findings show that the introduced strategies will significantly enhance ALO's capability in optimizing different tasks. Promisingly, the proposed approach can be viewed as an efficient and effective strategy for more optimization scenarios.