The Slime Mould Algorithm (SMA) uses two random search agents from the whole population to decide the future displacement and direction from the best search agents, which limits its exploitation and exploration. To solve this problem, an adaptive approach is investigated to decide whether opposition-based learning (OBL) will be used or not. Sometimes, the OBL is used to further increase the exploration. In addition, it maximizes the exploitation by replacing one random search agent with the best one in the position updating. The suggested technique is called an adaptive opposition slime mould algorithm (AOSMA). The proposed AOSMA algorithm would be useful for function optimization to solve real-world engineering problems.
M. K. Naik, R. Panda, and A. Abraham, “Adaptive opposition slime mould algorithm,” Soft Comput., Aug. 2021, DOI: 10.1007/s00500-021-06140-2
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