A Novel Discrete Fruit Fly Optimization Algorithm for Intelligent Parallel Test sheets Generation

Wang, Fengrui; Wang, Wenhong; Dong, Jing; Feng, Tianmin

doi:10.1051/matecconf/20152201040

Cited by 3 publications

(1 citation statement)

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“…Although the original FOA has primarily been applied to problems on a continuous definition domain, it can also be successfully applied to problems with continuous variables. However, the FOA must be modified in order to effectively manage the discrete variables associated with combinatorial optimization issues, such as the food source representations and effective generation mechanisms of candidate solutions near swarm locations in the TSP, intelligent parallel test sheet generation [31], and flow shop scheduling problems with intermingling equivalent sublots [32], optimizing a location allocation-inventory problem in a two-echelon supply chain network [33], and the homogeneous fuzzy series-parallel redundancy allocation problem [34]. As stated previously, the TSP is an NP-hard combinatorial optimization issue involving a large search area that cannot be easily solved with traditional algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem

Zi-bin

Yang

2016

PLoS ONE

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The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems.

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