Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Optimal Foraging00:48

Optimal Foraging

14.2K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
14.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Multiomics integrative analysis identifies <i>APOE</i> allele-specific blood biomarkers associated to Alzheimer's disease etiopathogenesis.

Aging·2021
Same author

Transfer mechanism and bioaccumulation risk of potentially toxic elements in soil-rice systems comparing different soil parent materials.

Ecotoxicology and environmental safety·2021
Same author

Plug-in tubes allow tunable oil removal, droplet packing, and reaction incubation for time-controlled droplet-based assays.

Biomicrofluidics·2021
Same author

White Matter Alterations of the Goal-Directed System in Patients With Obsessive-Compulsive Disorder and Their Unaffected First-Degree Relatives.

Biological psychiatry. Cognitive neuroscience and neuroimaging·2021
Same author

Mutation analysis of the GLA gene in Chinese patients with intracerebral hemorrhage.

Neurobiology of aging·2021
Same author

Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk.

JCI insight·2021

Related Experiment Video

Updated: Mar 12, 2026

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field
04:21

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Published on: July 28, 2023

2.6K

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem.

Zi-Bin Jiang1, Qiong Yang1

  • 1College of Business Administration, Hunan University, Changsha, Hunan, China.

Plos One
|November 5, 2016
PubMed
Summary
This summary is machine-generated.

A new discrete fruit fly optimization algorithm (DFOA) effectively solves the traveling salesman problem (TSP). This bio-inspired approach enhances search performance for complex combinatorial optimization tasks.

More Related Videos

An Optimized Protocol for Rearing Fopius arisanus, a Parasitoid of Tephritid Fruit Flies
12:00

An Optimized Protocol for Rearing Fopius arisanus, a Parasitoid of Tephritid Fruit Flies

Published on: July 2, 2011

15.5K
FLEX: Flight Exercise Training Protocol for the Fruit Fly Drosophila
03:47

FLEX: Flight Exercise Training Protocol for the Fruit Fly Drosophila

Published on: October 14, 2025

592

Related Experiment Videos

Last Updated: Mar 12, 2026

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field
04:21

Author Spotlight: Sieving Fruit Pulp to Detect Immature Tephritid Fruit Flies in the Field

Published on: July 28, 2023

2.6K
An Optimized Protocol for Rearing Fopius arisanus, a Parasitoid of Tephritid Fruit Flies
12:00

An Optimized Protocol for Rearing Fopius arisanus, a Parasitoid of Tephritid Fruit Flies

Published on: July 2, 2011

15.5K
FLEX: Flight Exercise Training Protocol for the Fruit Fly Drosophila
03:47

FLEX: Flight Exercise Training Protocol for the Fruit Fly Drosophila

Published on: October 14, 2025

592

Area of Science:

  • Computational Intelligence
  • Operations Research
  • Algorithm Design

Background:

  • The continuous fruit fly optimization algorithm (FOA) is a powerful bio-inspired method for numerical optimization.
  • The traveling salesman problem (TSP) is a significant combinatorial optimization challenge with broad applications.
  • Existing meta-heuristic algorithms face challenges in solving large-scale TSP instances efficiently.

Purpose of the Study:

  • To develop a discrete variant of the fruit fly optimization algorithm (DFOA) tailored for combinatorial problems.
  • To apply the DFOA to solve the traveling salesman problem (TSP).
  • To evaluate the performance and effectiveness of the DFOA against other meta-heuristic algorithms on benchmark TSP instances.

Main Methods:

  • The discrete fruit fly optimization algorithm (DFOA) represents TSP tours as ordered city indices.
  • The DFOA employs 'smelling' and 'tasting' processes, incorporating a crossover operator for neighbor search and an edge intersection elimination (EXE) operator for exploration enhancement.
  • Benchmark instances from TSPLIB were used to rigorously test the algorithm's searching capabilities.

Main Results:

  • The DFOA demonstrated strong performance in solving TSP instances, particularly for large-scale problems.
  • Comparative analysis showed the DFOA to be effective when contrasted with other meta-heuristic algorithms.
  • The designed smelling and tasting processes, along with the EXE operator, contributed to improved exploration and exploitation.

Conclusions:

  • The proposed discrete fruit fly optimization algorithm (DFOA) is a viable and effective meta-heuristic for solving the traveling salesman problem.
  • DFOA shows particular promise for tackling large-scale and complex TSP instances.
  • The study validates the potential of bio-inspired algorithms, like DFOA, in addressing challenging combinatorial optimization problems.