A method for routing optimization of very large scale integrated circuit physical design using a constraint multi-objective evolutionary method based on multi-stage search

By employing a multi-stage search strategy, the problem of search stagnation in routing optimization in VLSI physical design is solved, enabling rapid approach to the Pareto front, preservation of high-quality solutions, and guarantee of feasibility, thereby improving the efficiency and quality of routing optimization.

CN115859825BActive Publication Date: 2026-02-06FUZHOU UNIV
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Patent Information

Application Number
CN202211609973.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-12-14
Publication Date
2026-02-06
Estimated Expiration
2042-12-14

AI Technical Summary

Technical Problem

Existing multi-objective evolutionary methods struggle to effectively traverse large infeasible regions when dealing with routing optimization in VLSI physical design, leading to search stagnation and difficulty in finding high-quality solution sets within feasible regions.

Method used

A constrained multi-objective evolutionary approach based on multi-stage search is adopted, which includes transforming the problem into a single-objective optimization without considering constraints, using weight vectors to guide the population search, and adopting an improved epsilon constraint handling strategy and constraint priority principle to adjust the search strategy at different stages, so as to quickly approach the Pareto front, retain high-quality solutions, and ensure feasibility.

Benefits of technology

This approach enables the population to quickly traverse infeasible regions, uniformly search the target space, retain high-quality solutions, ensure the feasibility and diversity of the final solution set, and improve the efficiency and quality of wiring optimization.

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Abstract

The present application relates to a constrained multi-objective evolutionary method based on multi-stage search. Different search strategies are adopted in three stages of the method. In order to make the population quickly cross the large infeasible region and approach the Pareto front, the method does not consider the constraint condition in the first stage, and a convergence index is used to guide the population search; a set of uniformly distributed weight vectors is used to maintain the diversity of the population in the second stage, and an improved epsilon constraint processing strategy is proposed to retain high-quality solutions in the infeasible region; and the constraint priority principle is adopted in the third stage to concentrate the search preference in the feasible region to ensure the feasibility of the final solution set.
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