Improved Slime Mould Algorithm Hybridizing Chaotic Maps and Differential Evolution Strategy for Global Optimization

Slime Mould Algorithm (SMA) is a new meta-heuristics algorithm that inspired by the behaviors of slime mould from nature. Due to its effective performance, SMA has shown competitive performance among other algorithms and been used in many mathematical optimization real-world problems. However, tends sink into local optimality lacks diversity population. Therefore, cope with drawbacks classical SMA, this paper proposes an improved named CHDESMA. First all, chaotic maps methods have characteristics ergodicity randomness, we replace original random initialization improve algorithm, which more suitable for exploring potential areas early stage. Then, based on superior searching ability differential evolution (DE), crossover selection operations DE are applied CHDESMA, position updated combination operator mutation strategy DE, effectively avoids falling optimum. CHDESMA was evaluated using CEC2014 CEC2017 test suits four engineering compared advanced variants. The experimental results statistical analysis indicate state-of-the-art algorithms.