{'fraction_non_alphanumeric': 0.016835016835016835, 'fraction_numerical': 0.0, 'mean_word_length': 5.706766917293233, '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}}
Brain Storm optimization Algorithm based on Prior Knowledge and Heuristic Crossover Operator for Traveling Salesman Problem

Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem. This paper proposes a Brain Storm optimization (BSO) algorithm based on prior knowledge and heuristic crossover operator (PKHDBSO) to solve TSP. The algorithm firstly uses the convex hull or greedy algorithm to initialize the population, which can improve the initial population quality. Secondly, a heuristic crossover operator is introduced in the process of generating new individuals to improve the search efficiency of the algorithm considering the characteristics of the TSP. The effectiveness and practicability of the algorithm are verified by a set of simulation example. The results demonstrate that the proposed algorithm have better performance in solving large-scale TSP.