Multi-culture diversity based self adaptive particle swarm optimization for optimal floorplanning

摘要

Floorplanning is the central part of the physical design of “Very Large Scale Integrated (VLSI)” circuit design. It decides the core performance characteristics of the fabricated chip. It is possible to model the floorplanning issue as a multi-objective problem, where a number of objectives satisfied simultaneously. Based on the complexity point of view, it is an NP-hard problem, which means getting an optimal solution is a challenging task. In order to resolve this problem, in this paper, an adversity-based multi-cultureself-adaptive form of Particle Swarm Optimization (PSO) has proposed. Multi-objective optimization approach has applied to minimize the layout area required to place all modules and their total interconnection wire length. The proposed solution has multicultural integration, which provides the ability to explore the solution space faster and efficiently. Diversity-based self-adaptation of parameters provides the optimum balance among the exploration as well as exploitation. Finally, the experimental results showed the advantages in comparison to other variations of PSO in delivering the compact and robust performances.