Enhanced social emotional optimisation algorithm with generalised opposition-based learning

摘要

Social emotional optimisation algorithm (SEOA) is a newly developed evolutionary algorithm, which exhibits excellent performance for various engineering problems in real-world applications. However, SEOA may easily trap into local optima when solving complex multimodal function optimisation problems. This paper proposes a novel social emotional optimisation algorithm, called GOSEOA, which performs the generalised opposition-based learning (GOBL) strategy with a certain probability during the evolution process. The proposed algorithm uses the generalised opposition-based learning strategy to transform the current population to a generalised opposition-based population. Accordingly, the current population and the generalised opposition-based population are simultaneously considered to increase the probability for finding the global optimum. Experiments conducted on a comprehensive set of benchmark functions indicate that GOSEOA can obtain promising performance on the majority of the test functions.