LAUSR

Real life problems are used as benchmarks to evaluate the performance of existing, improved and modified evolutionary algorithms. In this paper, we propose a new hybrid method, namely SIWO, by embedding space transformation search (STS) into invasive weed optimization to solve complex fixed-point problems. Invasive weed optimization suffers from premature convergence when solving complex optimization problems. Using STS transforms the current search space into a new search space by simultaneously evaluating solutions in the current and transformed spaces. This increases the probability that a solution is closer to the global optimum. Therefore, we can avoid premature convergence and the convergence speed is also increased. To evaluate the performance of SIWO, four complex fixed-point problems are chosen from the literature. Our findings demonstrate that SIWO can solve complex fixed-point problems with great precision. Moreover, the numerical results demonstrate that SIWO is an effective and efficient algorithm compared with some state-of-the-art algorithms.