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Everything about Metaheuristic

Optimization technique

In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems. Their use is always of interest when exact or other (approximate) methods are not available or are not expedient, either because the calculation time is too long or because, for example, the solution provided is too imprecise. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: MetaheuristicA 5-minute orientation
  2. Skim a recent paper: Learning for multiple purposes: A Q-learning enhanced hybrid metaheuristic for parallel drone scheduling traveling salesman problemInfluential work since 2022
  3. Pick up a classic book: Parallel MetaheuristicsThe most-reprinted book on the subject
  4. Read the landmark paper: A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systemsCited 269 times
  5. Go sideways: List of metaphor-based metaheuristicsA closely related topic
  6. Go sideways: Memetic algorithmA closely related topic

📖 Definitions

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🗂️ The subject at a glance

  1. Properties
  2. Classification
  3. Applications
  4. Metaheuristic Optimization Frameworks
  5. Contributions

🏆 Landmark papers (most cited)

🆕 Influential since 2022

📚 Books worth reading

Ranked by number of editions on Open Library, a rough measure of how lasting a book is.

🔗 Related topics