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This paper proposes fusion of synchronous germ computing (SGC) with twin swarm intelligence (TSI) technique named as SGCTSI to enhance quality of global solutions with faster convergence of multimodal functions. In this paper, initially the authors tried to increase the speed of bacteria by updating bacteria positions synchronously, which is treated as SGC. In SGC, all the bacteria update their positions to attain global best position after completion of each generation, by adopting the feature of communication with each other. After each generation, current positions of bacteria are updated by co-operation of ePSO (particle swarm optimization with extrapolation technique) and GLBestPSO (global and local best PSO) called as mutation operator. The mutation operator brings about diversity in the population to avoid premature convergence or being trapped in some local optima. The SGCTSI has more global search ability at the beginning and improves the quality of solutions at the end of each run. The proposed technique is tested with eight standard benchmark functions and results are compared with ePSO GLBestPSO and canonical PSO (cPSO). The experimental results on bencmark functions validate that, the proposed trifusion approach produces good quality solution with faster convergence compared to other techniques. The performance of the SGCTSI has been tested through various statistical parameters and analysis of variance (ANOVA) test.