# Multi Swarm Optimization Based Clustering with Tabu Search in Wireless Sensor Network
## 4 Results and Discussion
The improvement of MSO-Tabu over Tabu is 18%, 13%, 4%, 11%, 14%, and 8% whereas over MSO based clustering the advantage is 8%, 6%, 4%, 3%, 3%, and 0% when compared for 200, 400, 600, 800, 1000, and 1200 number of nodes, respectively. From Figure 2, it can be observed that the MSO-Tabu has a lower average end to end delay by 24%, 22%, 13%, 11%, and 7% for 200, 400, 600, 1000, and 1200 number of nodes, but higher delay by 9% for 800 number of nodes when compared with GA. It is observed that MSO-Tabu has the average end-to-end delay advantage of 0%, 4%, 7%, 4%, 0%, and 0% compared to DE. At the same time, MSO-Tabu, when compared with Tabu, has a lower average end to end delay by 0%, 11%, and 9% for 200, 400, and 600 nodes but a higher average end to end delay of 17%, 12%, and 13% for 800, 1000, and 1200 number of nodes  
Figure 4: Lifetime Computation for MSO-Tabu. Figure 5: Average energy dissipated in Joules for MSO-Tabu. respectively. With respect to MSO based clustering, the proposed MSO-Tabu has 8%, 6%, 7%, 9%, 8%, and 6% improvement for 200, 400, 600, 800, 1000, and 1200 number of nodes respectively. It is also observed that the delay performance characteristics of MSO-Tabu are similar to DE. From Figure 3, it can be observed that the MSO-Tabu has a lower average packet loss rate by 21%, 23%, 25%, 20%, 9%, and 22% as compared to GA and by 12%, 14%, 20%, 12%, 7%, and 19% as compared to Tabu for 200, 400, 600, 800, 1000, and 1200 number of nodes respectively. It is also observed that MSO-Tabu perform better than MSO based clustering by 7%, 6%, 6%, 8%, 6%, and 5% for 200, 400, 600, 800, 1000, and 1200 nodes respectively. However, in the case of DE for 600 nodes, the performance of MSO-Tabu is lower than DE by 1%. Nevertheless, MSO-Tabu's performance is better than DE by 12%, 7%, 9%, 6%, and 3% for 200, 400, 800, 1000, and 1200 number of nodes. From Figure 4, it can be observed that the MSO-Tabu has higher lifetime computation by 0%, 4%, 2%, 8%, 10%, 24%, 41%, and 1000% for GA and by 0%, 0%, 1%, 3%, 8%, 7%, 94%, and 175% for DE when compared for 0, 100, 200, 300, 400, 500, 600, and 700 number of rounds respectively. For the same numbers of round, MSO-Tabu also reports better lifetime characteristics by 0%, 3%, 2%, 7%, 10%, 22%, 41%, and 1000% for Tabu and 0%, 0%, 1%, 2%, 8%, 9%, 107%, and 175% for MSO based clustering algorithm respectively. From Figure 5, it can be observed that the MSO-Tabu has higher average energy dissipation by 0%, 2%, 9%, 3%, 3%, 12%, 5%, and 86% as compared to DE, by 0%, 0%, 6%, 9%, 7%, 27%, 22%, and 333% as compared to Tabu and 0%, 2%, 12%, 3%, 3%, 12%, 10%, and 44% as compared to MSO based clustering when compared for 0, 100, 200, 300, 400, 500, 600, and 700 number of rounds respectively. However, when compared with GA, the MSO-Tabu has lower average energy dissipation by 11% and 4% for 0 and 100 number of rounds, but higher average energy dissipation by 0%, 6%, 0%, 12%, 16%, and 160% for 200, 300, 400, 500, 600, and 700 number of rounds respectively. Thus, MSO-Tabu is also able to secure better energy dissipation characteristics than all the metaheuristics.