{'fraction_non_alphanumeric': 0.01972062448644207, 'fraction_numerical': 0.0, 'mean_word_length': 6.207100591715976, '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}}
Sequence optimization for integrated radar and communication systems using meta-heuristic multiobjective methods

In real-world engineering problems, several conflicting objective functions have often to be optimized simultaneously. Typically, the objective functions of these problems are too complex to solve using derivative-based optimization methods. Integration of navigation and radar functionality with communication applications is such a problem. Designing sequences for these systems is a difficult task. This task is further complicated by the following factors: (i) conflicting requirements on autocorrelation and crosscorrelation characteristics; (ii) the associated cost functions might be irregular and may have several local minima. Traditional or gradient based optimization methods may face challenges or are unsuitable to solve such a complex problem. In this paper, we pose simultaneous optimization of autocorrelation and crosscorrelation characteristics of Oppermann sequences as a multiobjective problem. We compare the performance of prominent state-of-the-art multiobjective evolutionary meta-heuristic algorithms to design Oppermann sequences for integrated radar and communication systems.