6. Conclusion

Figure 9. Minimum 'interconnection cost' Steiner Tree obtained for PSO-C in SET 1.

76 Particle Swarm Optimization with Applications

Figure 10. Minimum 'interconnection cost' Steiner Tree obtained for PSO-MU in SET 1.

This chapter intends variants developed on Particle Swarm Optimization algorithm to resolve the global routing problem in VLSI domain. Simultaneously the controlling of acceleration constant in PSO has been verified for the VLSI routing problem. Lastly, a proportional analysis is done amongst the pre mentioned algorithms beside three variants of PSO, which have been recognized as decent routing algorithms in VLSI design. Researches are piloted to inspect the optimization property, rate of convergence, computational time and robustness of the algorithms including the ways by which algorithms work proficiently in problem space with dissimilar distributive topologies of VLSI layout.

The outcomes demonstrates that from the standpoint of topologically dissimilar problem spaces of VLSI domain, the general performance of PSO-ST [20] is very agreeable, however PSO-SAAC executes finest in an approximately uniform distributed problem space. It has also been observed that the performance of PSO-C and PSO-MU are unhampered of the diverse distribution of VLSI global routing problem space. The performance of the algorithm PSO-MU preserves a balance between the optimization and convergence rate. Although PSO-MU is realized to be steady in random problem space [22], PSO-C is appeared to be the best algorithm in the perspective of robustness.

Therefore the chapter indicated the exclusive merits and demerits of the PSO algorithm and its variants, well-matched for solving the wire-length minimization problem of global routing in VLSI physical design. It is projected that in the situation of VLSI global routing optimization, the paradigm of hybridization with essence of genetics can contest with the functioning of PSO conventional ones and can exhibit enhanced performance. Hence the global routing problem in VLSI can be competently managed by contemporary PSO meta-heuristics and by hybridization of distinct swarm intelligence.

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