{'fraction_non_alphanumeric': 0.025728987993138937, 'fraction_numerical': 0.0, 'mean_word_length': 5.488888888888889, '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}}
Using Bioinspired Meta-heuristics to Solve Reward-Based Energy-Aware Mandatory/Optional Real-Time Tasks Scheduling

In this paper we present meta-heuristics to solve the energy aware reward based scheduling of real-time tasks with mandatory and optional parts in homogeneous multi-core processors. The problem is NP-Hard. The meta-heuristics are the bioinspired methods Particle Swarm Optimization and Genetic Algorithm. Results are compared using synthetic systems of tasks, generated following the guidelines proposed in previous papers with an Integer Lineal Programming solution.