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April 30, 2026International Journal of Engineering & Technology0 citationsOpen Access

Particle Swarm Optimization for Vlsi Floor planning with Clustering Control

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VMV. MariselvamSS.Rajanandhini

Key Points

  • The aim is to address the NP-hard problem of VLSI floor planning using clustering constraints and optimization techniques.
  • Development of a Particle Swarm Optimization (PSO) algorithm with clustering control.
  • Utilization of pheromone paths for communication between particles.
  • Application of B tree representation for encoding relational connections in floor planning.
  • The proposed PSO-based method improved the quality of VLSI floor plans compared to traditional approaches.
  • Analyses using MCNC benchmarks showed the technique effectively finds better configurations.
  • Faster convergence occurred, resulting in optimal arrangements in fewer iterations.

Abstract

Floorplanning is a critical issue in simple VLSI design. It is a NP-hard combinatorial optimization problem. Now this investigation the VLSI floor planning issue through grouping limitations then the design region as per minimization paradigm remains measured. A procedure, which depends on essential standards of Particle Swarm Optimization (PSO), to take care of this issue is exhibited. This PSO-based calculation utilizes two distinct sorts of pheromone paths by way of the correspondence media between fake particles towards adequately manage them to agreeably develop a great floorplan. Based on the attributes of PSO, in addition, an encoding plan, which is alluded to as B tree representation, is planned on the way to speak to the ordered connections among route elements designed for a floorplan. Analyses utilizing MCNC benchmarks demonstrate that the execution of our technique intended for arrangement through the capacity of investigating better arrangements. The planned method displayed quickly merging then prompted extra ideal arrangements than other related approach.

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Cite This Study

Mariselvam et al. (2026) studied this question.

synapsesocial.com/papers/69f2f1471e5f7920c6386f3fhttps://doi.org/10.14419/ijet.v7i3.20.27352
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