Multiobjective VLSI Cell Placement Using Distributed Simulated Evolution Algorithm

Evaluating parallel simulated evolution strategies for VLSI cell placement

“…To observe if a different row allocation pattern than the one mentioned earlier [5] can lead to a different behavior, we also experimented with random row allocation [7]. Two parallel multiobjective algorithms, a wirelength-power only and the other including delay optimization as well, were implemented using two types of row allocation patterns for each.…”

Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem

“…Thirdly, both SimE and SA have been applied to solve various multi-objective optimization problems. Some examples for SA are [25][26][27][28], and for SimE are [29][30][31]. Thus, the overall aim of this paper is to compare and study the performance of fuzzy SA and fuzzy SimE algorithms (with three optimization objectives) with respect to the existing SA and SimE approaches (with two objectives).…”

A Swarm Random Walk Based Method for the Standard Cell Placement Problem

“…There remains significant room for improvement in existing placement algorithms, suggesting that new-more scalable and stable-hybrid techniques may be needed for future generations. There are other methods that have been used to solve the SCP problem, using genetic algorithms (parallel genetic algorithm (PGA) [19] and genetic algorithm for placement (GAP) [20]), simulated evolution (force-directed simulated evolution [21] and distributed parallelized SE algorithm (SimE) [22]) in addition to some hybridized methods (parallel simulated annealing/genetic algorithm (PSAGA) [23] and SimE-GA [24]); however, these methods are not as competitive, being tested on older benchmarks. Although simulated evolution was used to solve instances of the PEKO suite in [25], it is not included in the experimental results as it was uncompetitive, with quality ratios ranging from 6.33 to 8.52.…”