Adaptive opposition slime mould algorithm

Recently, the slime mould algorithm (SMA) has become popular in function optimization, because it effectively uses exploration and exploitation to reach an optimal solution or near-optimal solution. However, SMA two random search agents from whole population decide future displacement direction best agents, which limits its exploration. To solve this problem, we investigate adaptive approach whether opposition-based learning (OBL) will be used not. Sometimes, OBL is further increase In addition, maximizes by replacing one agent with position updating. The suggested technique called opposition (AOSMA). qualitative quantitative analysis of AOSMA reported using 29 test functions that consisting 23 classical 6 recently composition IEEE CEC 2014 suite. results are compared state-of-the-art optimization methods. Results presented paper show AOSMA’s performance better than other algorithms. evaluated Wilcoxon’s rank-sum test. It also ranked Friedman’s mean rank proposed would useful for real-world engineering problems.