A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem
作者： Zi-bin Jiang , Qiong Yang
DOI: 10.1371/JOURNAL.PONE.0165804
关键词: Benchmark (computing) 、 Computer science 、 Travelling salesman problem 、 Evolutionary algorithm 、 Mathematical optimization 、 Domain (software engineering) 、 Swarm behaviour 、 Crossover 、 Genetic algorithm 、 Intersection (set theory) 、 Operator (computer programming)
摘要: The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. continuous variant version of FOA has been proven to be powerful evolutionary approach determining the optima numerical function on definition domain. In this study, discrete (DFOA) and applied traveling salesman problem (TSP), common combinatorial problem. DFOA, TSP tour represented by an ordering city indices, meta-heuristic search processes are executed with two elaborately designed main procedures: smelling tasting processes. process, effective crossover operator used group for neighbors best-known swarm location. During edge intersection elimination (EXE) improve non-optimum food location in order enhance exploration performance DFOA. addition, benchmark instances from TSPLIB classified test searching ability proposed Furthermore, effectiveness DFOA compared that other algorithms. results indicate can effectively solve TSPs, especially large-scale problems.
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