{'fraction_non_alphanumeric': 0.01696969696969697, 'fraction_numerical': 0.0, 'mean_word_length': 5.453125, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Hybrid swarm intelligence for VLSI floorplan

Floorplanning and Placement Optimization problem is important for the VLSI (Very Large Scale Integration) physical design process. Obtained result from this step is the necessary for the other consecutive processes of the chip designing. Whereas VLSI Floorplanning is always considered as NP-hard problem. A hybrid particle swarm and ant colony optimization (PSO/ACO) algorithm is used in this paper with non slicing structure representation. This hybrid PSO/ACO is effective swarm intelligence search method for optimized solution of various modules by exploring the search space. Effective performance of this algorithm is tested on the various MCNC benchmarks circuits. This hybrid algorithm achieves best optimized solutions for area and wirelength with optimal running time.