Strengthening Teaching and Learning Based Multi-population Symbiotic Organism Search Algorithm

A Strengthening Teaching and Learning based Multi-population Symbiotic Organism Search algorithm (STLMSOS) is proposed to address the shortcomings of the symbiotic biological algorithm in solving complex high-dimensional problems, such as low accuracy, poor diversity and the tendency to fall into local optimality. The algorithm divides the ecosystem into three populations based on the evolutionary characteristics of organisms, and draws on the powerful search capability of the “teaching” phase and the excellent search capability of the “learning” phase of the teaching optimization algorithm, so that the mutualism population and commensalism population can evolve in stages, enhancing the solution accuracy and diversity of the algorithm. At the same time, by taking into account the complex connection between biological migration and species evolution, the information interaction between multiple populations is improved so that stagnant populations can interact in time to jump out of the local optimum. The proposed STLMSOS was fully evaluated on the CEC2017 problem set, and the simulation results show that STMSOS outperforms other state-of-the-art algorithms and is an effective algorithm for solving complex high-dimensional problems.