Symbiotic organisms search (SOS) algorithm is a nature-inspired meta-heuristic algorithm, which has been successfully applied to solve a wide range of benchmarks and real-world optimization problems. In this paper, an extended version of SOS, namely symbiotic organisms search with perturbed global crossover operator (PGCSOS), is introduced to enhance the performance of the basic SOS. In parasitism phase, an organism can benefit from other organisms that are better than it, and a perturbed crossover scheme is incorporated into the parasitism phase, which aims at maintaining the trade-off between exploration and exploitation effectively, and preventing the current best solution from getting trapped into local optima. The performance of PGCSOS is assessed by solving global optimization functions with different characteristics and real-world problems. Compared to the SOS, modified SOS and other promising heuristic methods, numerical results reveal that the PGCSOS has better optimization performance.
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