# local search

> method for problem solving in optimization

**Wikidata**: [Q1868524](https://www.wikidata.org/wiki/Q1868524)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Local_search_(optimization))  
**Source**: https://4ort.xyz/entity/local-search

## Summary
Local search is a method for problem solving in optimization. It is an informed (heuristic) search approach that focuses on improving a current candidate solution by exploring nearby alternatives.

## Key Facts
- Local search is described as a “method for problem solving in optimization.”  
- Local search is a subclass of **informed search algorithm**, meaning it is a type of search algorithm that uses preliminary (generally heuristic) information.  
- Local search is associated with the Wikipedia title **“Local search (optimization)”**.  
- Local search has documented aliases including **LS**, **局所探索**, **逐次改善法**, and **近傍探索法**.  
- Local search is connected in the knowledge graph to heuristic search variants including **beam search** and **local beam search**.  
- Local search has a Wikidata sitelink count of **16**.  
- The Wikipedia entry for local search (optimization) is available in multiple languages, including **en, de, fr, it, ja, ko, ar, fa, he, bg**.  
- External identifiers associated with local search include **Freebase ID /m/01tl76** and **Quora topic “Local-Search.”**  
- Local search is indexed in controlled vocabularies/registries including **STW Thesaurus for Economics (ID: 29776-1, exact match)** and **Encyclopedia of China (Third Edition) ID: 41700**.  

## FAQs
### Q: What is local search in optimization?
A: Local search is a method for problem solving in optimization. It is an informed (heuristic) search approach that uses preliminary information to guide the search for better solutions.

### Q: Is local search an informed search algorithm?
A: Yes. Local search is classified as a subclass of **informed search algorithm**, a category of search algorithms that generally use heuristic information.

### Q: What are other names for local search?
A: Local search is also known as **LS**, **局所探索**, **逐次改善法**, and **近傍探索法**. These aliases are used across different languages and contexts.

### Q: How is local search related to beam search?
A: Local search is related to heuristic search variants such as **beam search** and **local beam search**. In the provided knowledge graph context, these appear as related classes within heuristic/informed search.

## Why It Matters
Local search matters because it is a recognized method for solving optimization problems using informed (typically heuristic) guidance. In optimization, the goal is to find good solutions under constraints, and local search provides a general approach for iteratively seeking improvements by examining alternatives “near” a current candidate solution. Its classification under informed search highlights that it leverages preliminary information rather than searching blindly, which is central to many practical optimization workflows.

Local search is also significant as a conceptual hub: it is connected to other heuristic search approaches (including beam search and local beam search) and is widely referenced across knowledge systems and identifiers (e.g., Wikipedia across multiple languages, Freebase, Quora topic indexing, and thesaurus/encyclopedia IDs). This breadth of representation indicates that local search is a commonly recognized and reusable idea in optimization and heuristic search discussions, making it a useful term for organizing and retrieving related methods in both educational and applied contexts.

## Notable For
- Being explicitly classified as a **subclass of informed search algorithm** (heuristic-guided search).  
- Being represented as a distinct optimization concept with a dedicated Wikipedia topic: **“Local search (optimization)”**.  
- Having multiple cross-lingual aliases, including **局所探索**, **逐次改善法**, and **近傍探索法**.  
- Having broad cross-reference coverage via identifiers such as **Freebase (/m/01tl76)** and **STW Thesaurus for Economics (29776-1)**.  

## Body
### Classification and Concept
- **Entity:** local search  
- **Description:** method for problem solving in optimization  
- **Subclass of:** **informed search algorithm**  
  - Informed search algorithms are search algorithms where preliminary information is available (generally heuristic).

### Relationships to Other Search Methods
- **Related classes (knowledge graph context):**
  - **beam search** — heuristic search algorithm  
  - **local beam search** — heuristic local search algorithm  
- These relationships position local search alongside heuristic search variants used in optimization/search contexts.

### Names and Aliases
- **Aliases:**  
  - LS  
  - 局所探索  
  - 逐次改善法  
  - 近傍探索法  

### Reference Footprint and Identifiers
- **Wikipedia title:** Local search (optimization)  
- **Wikipedia languages listed:** ar, bg, de, en, fa, fr, he, it, ja, ko  
- **Sitelink count:** 16  
- **Freebase ID:** /m/01tl76 (reference publication date: 2013-10-28)  
- **Quora topic:** Local-Search  
- **STW Thesaurus for Economics ID:** 29776-1 (exact match)  
- **Microsoft Academic ID (discontinued):** 135320971  
- **Encyclopedia of China (Third Edition) ID:** 41700  

### Related Person (Graph Association)
- **Related:** Daniel Diaz — French computer scientist (born 1965-02-20; citizenship: France; occupations: computer scientist, programmer, academic).

## References

1. Freebase Data Dumps. 2013
2. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)