Maximum Lyapunov Exponent-based Multiple Chaotic Slime Mold Algorithm For Real-world Optimization

Meta-heuristic strategies are becoming increasingly popular for solving complex mathematical optimization problems. Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. To improve the performance of SMA, we propose an improved algorithm, namely MCSMA, by investigating how to improve the probabilistic selection of chaotic operators based on the maximum Lyapunov exponent (MLE). The effectiveness of MCSMA is demonstrated by comparing it with other state-of-the-art methods on IEEE Congress on Evolution Computation (CEC) benchmark test suits and CEC2011 practical problems.