Abstract: Group search optimizer(GSO)is a new swarm intelligence algorithms based on the producer-scrounger model.GSO has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence and get stuck in local minima. This paper proposed an enhanced GSO algorithm called GOGSO, which employs generalized opposition-based learning to transform the current population into a new opposition population and uses an elite selection mechanism on the two populations. xperiments were conducted on a comprehensive set of benchmark functions. The results show that OGSO obtains promising performance.

Key words: Group search optimizer, Opposition-based learning, Numerical optimization