{'fraction_non_alphanumeric': 0.019402985074626865, 'fraction_numerical': 0.0, 'mean_word_length': 5.605911330049261, '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}}
Evolutionary optimization technique for multi-state two-terminal reliability allocation in multi-objective problems

This article presents a newly developed evolutionary algorithm for solving multi-objective optimization models for the design of multi-state two-terminal networks. It is assumed that for each network component, a known set of functionally equivalent component types (with different performance specifications) can be used to provide redundancy. Furthermore, the reliability behavior of the network and its components can have a range of states varying from perfect functioning to complete failure; that is, a multi-state behavior. Thus, the new algorithm allows solving the multi-objective optimization case of the reliability allocation problem for general multi-state two-terminal networks. The optimization routine is based on three major steps that use an evolutionary optimization approach and Monte Carlo simulation to generate a Pareto optimal string of probabilistic solutions to these problems. Examples for different multi-state two-terminal networks are used throughout the article to illustrate the approach. The results obtained for test cases are compared with other proposed methods to show the accuracy of the algorithm in generating approximate Pareto optimal sets for problems with a large solution space.