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Evolutionary, Neural, or LLM-Driven Heuristic Generation? A Unified Ant Colony Optimization Benchmark for Nature-Inspired Routing Heuristics on the TSP and CVRP.

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10 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 10 matches · 3 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § 3. Materials and Methods › 3.3. Compared Methods and Parameter Settings ↔ baselines/human_designed.py, lines 20–29 · score 0.70 · nearest neighbor weighting, inverse distance, Human designed, heuristics
  2. [2] § 2. Related Work and SOTA Positioning › 2.3. Evolutionary Hyper-Heuristics and LLM-Driven Automated Algorithm Design ↔ baselines/ghpp.py, lines 1–16 · score 0.57 · hyper heuristics, genetic programming, evolve, GHPP
  3. [3] § 2. Related Work and SOTA Positioning › 2.3. Evolutionary Hyper-Heuristics and LLM-Driven Automated Algorithm Design ↔ evolved_heuristics/tsp_evolved.py, lines 1–9 · score 0.57 · LLM driven reflective, reflective evolution, ReEvo, evolve, heuristics
  4. [4] § 3. Materials and Methods › 3.3. Compared Methods and Parameter Settings ↔ evolved_heuristics/tsp_evolved.py, lines 23–39 · score 0.55 · nearest neighbor, inverse distance, heuristics
  5. [5] § 3. Materials and Methods › 3.5. Computing Environment, Reproducibility, and Timing Protocol ↔ aco_solver/cvrp_aco.py, the whole file · a weak match · score 0.55 · PyTorch, stack, device, tensors, CPU, pheromone
  6. [6] § 3. Materials and Methods › 3.5. Computing Environment, Reproducibility, and Timing Protocol ↔ aco_solver/tsp_aco.py, the whole file · a weak match · score 0.54 · PyTorch, stack, device, tensors, CPU, pheromone
  7. [7] § 3. Materials and Methods › 3.2. Unified ACO Evaluation Framework ↔ aco_solver/cvrp_aco.py, the whole file · a weak match · score 0.53 · vector, masks, visited, customers, depot, Pheromone
  8. [8] § 2. Related Work and SOTA Positioning › 2.1. Traditional Heuristics and Exact Solving Methods ↔ aco_solver/tsp_aco.py, lines 1–6 · score 0.51 · ant colony, ACO solver, optimization, TSP
  9. [9] § 2. Related Work and SOTA Positioning › 2.2. Neural Combinatorial Optimization Methods ↔ visualization/report_builder.py, lines 199–262 · score 0.50 · neural networks, learn heuristic, GPU, DeepACO, modeling, NCO
  10. [10] § 2. Related Work and SOTA Positioning › 2.1. Traditional Heuristics and Exact Solving Methods ↔ aco_solver/cvrp_aco.py, lines 1–6 · score 0.50 · ant colony, ACO solver, optimization

Paper

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The authors' code

Python · 118 lines · 4.8 KB · no license · 3 matches

  1. """
  2. CVRP Ant Colony Optimization solver.
  3. Adapted from problems/cvrp_aco/aco.py for standalone use in Experiment.
  4. """
  5. import torch
  6. from torch.distributions import Categorical
  9. class ACO:
  10. def __init__(self, distances, demand, heuristic, capacity,
  11. n_ants=30, decay=0.9, alpha=1, beta=1, device='cpu'):
  12. """
  13. Args:
  14. distances: (n, n) distance matrix (includes depot at index 0)
  15. demand: (n,) demand vector (depot demand = 0)
  16. heuristic: (n, n) heuristic matrix
  17. capacity: vehicle capacity
  18. """
  19. self.problem_size = len(distances)
  20. self.distances = torch.tensor(distances, dtype=torch.float32, device=device) \
  21. if not isinstance(distances, torch.Tensor) else distances.to(device)
  22. self.demand = torch.tensor(demand, dtype=torch.float32, device=device) \
  23. if not isinstance(demand, torch.Tensor) else demand.to(device)
  24. self.capacity = capacity
  26. self.n_ants = n_ants
  27. self.decay = decay
  28. self.alpha = alpha
  29. self.beta = beta
  31. self.pheromone = torch.ones_like(self.distances)
  32. self.heuristic = torch.tensor(heuristic, dtype=torch.float32, device=device) \
  33. if not isinstance(heuristic, torch.Tensor) else heuristic.to(device)
  35. self.shortest_path = None
  36. self.lowest_cost = float('inf')
  37. self.device = device
  39. @torch.no_grad()
  40. def run(self, n_iterations):
  41. for _ in range(n_iterations):
  42. paths = self.gen_path()
  43. costs = self.gen_path_costs(paths)
  45. best_cost, best_idx = costs.min(dim=0)
  46. if best_cost < self.lowest_cost:
  47. self.shortest_path = paths[:, best_idx]
  48. self.lowest_cost = best_cost
  50. self.update_pheromone(paths, costs)
  52. return self.lowest_cost
  54. @torch.no_grad()
  55. def update_pheromone(self, paths, costs):
  56. self.pheromone = self.pheromone * self.decay
  57. for i in range(self.n_ants):
  58. path = paths[:, i]
  59. cost = costs[i]
  60. self.pheromone[path[:-1], torch.roll(path, shifts=-1)[:-1]] += 1.0 / cost
  61. self.pheromone[self.pheromone < 1e-10] = 1e-10
  63. @torch.no_grad()
  64. def gen_path_costs(self, paths):
  65. u = paths.permute(1, 0) # (n_ants, seq_len)
  66. v = torch.roll(u, shifts=-1, dims=1)
  67. return torch.sum(self.distances[u[:, :-1], v[:, :-1]], dim=1)
  69. def gen_path(self):
  70. actions = torch.zeros((self.n_ants,), dtype=torch.long, device=self.device)
  71. visit_mask = torch.ones(size=(self.n_ants, self.problem_size), device=self.device)
  72. visit_mask = self.update_visit_mask(visit_mask, actions)
  73. used_capacity = torch.zeros(size=(self.n_ants,), device=self.device)
  74. used_capacity, capacity_mask = self.update_capacity_mask(actions, used_capacity)
  76. paths_list = [actions]
  77. done = self.check_done(visit_mask, actions)
  79. while not done:
  80. actions = self.pick_move(actions, visit_mask, capacity_mask)
  81. paths_list.append(actions)
  82. visit_mask = self.update_visit_mask(visit_mask, actions)
  83. used_capacity, capacity_mask = self.update_capacity_mask(actions, used_capacity)
  84. done = self.check_done(visit_mask, actions)
  86. return torch.stack(paths_list)
  88. def pick_move(self, prev, visit_mask, capacity_mask):
  89. pheromone = self.pheromone[prev]
  90. heuristic = self.heuristic[prev]
  91. dist = ((pheromone ** self.alpha) * (heuristic ** self.beta)
  92. * visit_mask * capacity_mask)
  93. dist = dist.clamp(min=1e-30)
  94. cat = Categorical(dist)
  95. actions = cat.sample()
  96. return actions
  98. def update_visit_mask(self, visit_mask, actions):
  99. visit_mask = visit_mask.clone()
  100. visit_mask[torch.arange(self.n_ants, device=self.device), actions] = 0
  101. visit_mask[:, 0] = 1 # depot can always be revisited
  102. # Exception: cannot revisit depot if there are still unvisited customers
  103. visit_mask[(actions == 0) * (visit_mask[:, 1:] != 0).any(dim=1), 0] = 0
  104. return visit_mask
  106. def update_capacity_mask(self, cur_nodes, used_capacity):
  107. capacity_mask = torch.ones(size=(self.n_ants, self.problem_size), device=self.device)
  108. used_capacity = used_capacity.clone()
  109. used_capacity[cur_nodes == 0] = 0
  110. used_capacity = used_capacity + self.demand[cur_nodes]
  111. remaining = self.capacity - used_capacity
  112. remaining_repeat = remaining.unsqueeze(-1).repeat(1, self.problem_size)
  113. demand_repeat = self.demand.unsqueeze(0).repeat(self.n_ants, 1)
  114. capacity_mask[demand_repeat > remaining_repeat] = 0
  115. return used_capacity, capacity_mask
  117. def check_done(self, visit_mask, actions):
  118. return (visit_mask[:, 1:] == 0).all() and (actions == 0).all()

cvrp_aco.py at commit cc02b2d, no license · at the source

Overview

Authors: Haoyuan Wu1, You Wu2
ORCID iDs: Haoyuan Wu
  1. School of Finance, Jiangxi University of Finance and Economics, Nanchang 330013, China
  2. Software College, Northeastern University, Shenyang 110169, China
Institutions: Jiangxi University of Finance and Economics (China); Northeastern University (China)
Journal: Biomimetics (Basel, Switzerland), volume 11, issue 7, article 516
Dates: received 22 June 2026; accepted 19 July 2026; published online 22 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/biomimetics11070516 · PMID 42505549 · PMCID PMC13406755 · OpenAlex W7170096698
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning
Keywords: biomimetics, stigmergy, evolutionary computation, genetic programming hyper-heuristics, ant colony optimization, swarm intelligence, large language models
Topic: Slime Mold and Myxomycetes Research (Biomedical Engineering, Engineering), according to OpenAlex
Funding: National Natural Science Foundation of China (52130403)
Citations: not cited yet (Europe PMC); 34 references in the paper

Abstract

Biomimetic optimization transfers biological information-processing mechanisms into computational systems. Ant colony optimization (ACO) is a canonical example: artificial ants functionally abstract pheromone-mediated stigmergy, decentralized exploration, trail decay through algorithmic evaporation, and adaptive path reinforcement. Building on this functional biological analogue, we present a controlled cross-paradigm evaluation of routing-heuristic generation. A standardized interface embeds human-designed rules, the genetic programming hyper-heuristic GHPP, a resource-constrained DeepACO-MLP proxy, and an offline ReEvo-style proxy into the same ACO solver. The methods are evaluated on held-out TSP and CVRP instances in terms of solution quality, reported generation or training cost, interpretability, and cross-scale behavior under a matched distribution. GHPP yields the shortest routes at all tested scales; the ReEvo-offline proxy and strong human-designed rules generally form a second tier, whereas the resource-constrained neural proxy degrades markedly as problem size increases. These results do not establish an intrinsic ranking of full-capability paradigms. Instead, they show that method selection depends on the operating constraint and on evidence provenance: longer locally measured offline search favors GHPP, while auditable explicit rules characterize the human and ReEvo-offline proxies. By holding the ant-inspired execution mechanism fixed and varying the source of heuristic information, the benchmark clarifies how evolutionary, neural, and LLM-style design strategies interact with a common biomimetic substrate.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 10 matches between paragraphs and lines of code.

HaoyuanWu-A/NCO

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: cc02b2d11b4dc455145f8509f8f41d8b7b2ca71f, 24 June 2026
Languages: Python (26)
Size: 83 files, 26 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Holds: README, environment (pyproject.toml), documentation
Not found: license file, CITATION.cff, tests, continuous integration
Tools: NumPy (13 files), SciPy (5 files), PyTorch (4 files), Matplotlib (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
27 files
At the source: github.com/HaoyuanWu-A/NCO

The paper's code and data availability statement is in the Data section.

Tracing map

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 26 scripts, each with its path and the digest of its content;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

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Data

No dataset and no data link were found in the paper.

Data Availability Statement

Source code, configurations, generated datasets, archived summary results, and figure assets are available at https://github.com/HaoyuanWu-A/NCO (accessed on 24 June 2026, archived revision audited at commit cc02b2d11b4dc455145f8509f8f41d8b7b2ca71f).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 7 keywords, 1 funder, 18 references.

Cite

This paper

Wu, H., & Wu, Y. (2026). Evolutionary, Neural, or LLM-Driven Heuristic Generation? A Unified Ant Colony Optimization Benchmark for Nature-Inspired Routing Heuristics on the TSP and CVRP. Biomimetics (Basel, Switzerland), 11(7), 516. https://doi.org/10.3390/biomimetics11070516

BibTeX

@article{wu2026evolutionary,
author = {Wu, Haoyuan and Wu, You},
title = {{Evolutionary, Neural, or LLM-Driven Heuristic Generation? A Unified Ant Colony Optimization Benchmark for Nature-Inspired Routing Heuristics on the TSP and CVRP}},
journal = {Biomimetics (Basel, Switzerland)},
year = {2026},
month = jul,
volume = {11},
number = {7},
pages = {516},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2313-7673},
doi = {10.3390/biomimetics11070516},
url = {https://doi.org/10.3390/biomimetics11070516},
pmid = {42505549},
pmcid = {PMC13406755}
}

RIS

TY - JOUR
AU - Wu, Haoyuan
AU - Wu, You
TI - Evolutionary, Neural, or LLM-Driven Heuristic Generation? A Unified Ant Colony Optimization Benchmark for Nature-Inspired Routing Heuristics on the TSP and CVRP
T2 - Biomimetics (Basel, Switzerland)
J2 - Biomimetics (Basel)
PY - 2026
DA - 2026/07/22
VL - 11
IS - 7
SP - 516
SN - 2313-7673
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/biomimetics11070516
UR - https://doi.org/10.3390/biomimetics11070516
LA - en
ER -

CSL-JSON

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"author": [
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"PMCID": "PMC13406755",
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