The existing algorithms for optimal heuristic search

The gap

Evidence profile

Sourced from the conclusions and future work and stated research gap and limitations section of the source papers, classified as general, drawn from work published between 2024 and 2026, spanning 4 journals. Those papers have been cited 372 times in total.

Supporting evidence — 4 representative gaps

• A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation (2026) · PLOS One · doi

  In addition, future research may explore alternative meta-heuristic optimization algorithms—such as Particle Swarm Optimization, Whale Optimization Algorithm, Hunger Games Search, and Differential Evolution—which could provide complementary search behaviors and potentially further enhance model performance.

  generalconclusionsevidence 5/5
  Keywords: optimization search addition future explore alternative meta heuristic algorithms particle swarm whale algorithm hunger games
• Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning (2024) · Cluster Computing · cited 372× · doi

  Nowadays, the use of meta-heuristic algorithms to optimize all kinds of real-world problems has become necessary because they can be implemented simply and easily escape the local optimality trap. However, each meta-heuristic algorithm has a different approach to each other because each of these algorithms is inspired by a different natural phenomenon, implemented and formulated. Meta-heuristic algorithms have different performances from each other because they use different operators from each other.

  generalfuture workevidence 5/5
  Keywords: different meta heuristic algorithms implemented algorithm operators nowadays optimize kinds real world problems become necessary
• Beyond Pruning: Leveraging Dominance Relations for Heuristic Propagation (2026) · Proceedings of the International Conference on Automated Planning and Scheduling · doi

  The existing algorithms for optimal heuristic search may incur an exponential search space even with almost perfect heuristics. The existing algorithms do not share information among different regions of the search space. The existing algorithms do not leverage dominance relations for heuristic propagation.

  generalstated research gapevidence 5/5
  Keywords: existing algorithms optimal heuristic search incur exponential space
• Optimal DG allocation using the Dingo Optimization Algorithm: robust power loss reduction with concomitant voltage stability improvement in distribution and transmission networks (2026) · Scientific Reports · doi

  The paper identifies the lack of topological scalability in traditional metaheuristic algorithms as a limitation. The paper notes that the Dingo Optimization Algorithm may not be suitable for all types of optimization problems. The paper does not provide a comprehensive comparison of the Dingo Optimization Algorithm with other optimization algorithms.

  generallimitations sectionevidence 5/5
  Keywords: paper identifies lack topological scalability traditional metaheuristic algorithms

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