The calculation the search engines use to find the most relevant information in relation to a search query. A search algorithm is a massive collection of other algorithms, each with its own purpose and task.
The search engine uses a combination of algorithms and numerous ranking factors to deliver webpages ranked by relevance on its search engine results pages (SERPs).
Each search engine uses a search engine algorithm, and no two search engines use exactly the same formula to determine a page’s ranking.
Common Types of Search Algorithms
Linear Search Algorithm
Linear search algorithms are considered to be the most basic of all search algorithms as they require a minimal amount of code to implement. Also known as a sequential search, linear search algorithms are the simplest formula for search algorithms to use . Linear search algorithms are best for short lists that are unordered and unsorted. To find what is being searched for, the algorithm looks at the items as a list. Once it gets to the item being searched, the search is finished. Linear search is not a common way to search as it is a fairly inefficient algorithm compared to other available search algorithms
Binary Search Algorithm
A binary search algorithm, unlike linear search algorithms, exploits the ordering of a list. This algorithm is the best choice when alist has terms occurring in order of increasing size. The algorithm starts in the middle of the list. If the target is lower than the middle point, then it eliminates the upper half of the list; if the target is higher than the middle point, then it cuts out the lower half of the list. For larger databases, binary search algorithms will produce much faster results than linear search algorithms
Most famous Google Algorithms
Panda (2011)
The main focus was on fighting plagiarism, duplicate content, spam, and keyword abuse. Pages were ranked on a content quality scale, and the scores were used as a significant ranking factor. Five years later, Panda became part of Google’s root algorithm, significantly accelerating the implementation of minor updates and the speed of processing page content.
Penguin(2012)
Aiming at combating link farms, private blog networks, and irrelevant and spammy links, Penguin also addressed the issue of over-optimized anchor texts. The actions of the algorithm primarily affected those sites whose link profile did not pass the test and was marked as unnatural. This was due to the massive purchase of cheap low-quality backlinks. According to the latest guidelines, a healthy backlink profile should be balanced and contain as many different sources as possible, while avoiding suspicious or dangerous ones.
Hummingbird (2013)
This was another major update to tackle keyword abuse and low-quality content. There was a transition from processing individual keywords to determining the user’s search intentions. Instead of guessing the keywords that would get the best results, focus on the information itself, and how it would be applied. When processing and ranking links, the importance of synonyms, similar topics, and semantically similar searches have increased significantly.
Mobile Results (2015, 2018)
This update was never given a name, although it changed the use of search in many ways. The main focus was on pages without corresponding mobile versions and site performance on mobile devices in general. There was an important prioritization shift in rankings towards pages that were well adapted for viewing and working on mobile devices. Optimization included many aspects: from the size and types of content, to how well the content is served on the page, whether loading by external files is not blocked, and so on. Even now, the performance of mobile versions of pages remains a rather serious issue for many sites.
RankBrain (2015)
Continuing the fight against irrelevant and low-quality content, Google has also put a lot of emphasis on user experience with this update. RankBrain is a grading system built on the principle of machine learning and analysis. The update is considered to be a Hummingbird add-on, designed to improve the quality of the interpretation of user requests and their subsequent comparison with indexed pages. Evaluation by the RankBrain algorithm is one of the key factors underlying effective ranking in the formation of search results.
Medic (2018)
This update primarily affects healthcare websites. The target audience of the new algorithm is much wider and includes any online resources containing information that can affect significant aspects of users’ lives. For example, finance, law, education, and so on. The main signals that the Medic took into account to form the assessment: Your Money or Your Life (YMYL) and Expertise-Authority-Trust (E-A-T). Engaging experts from each industry and attribution has become a part of page content evaluation since the launch of this algorithm.
BERT (2018)
The combined efforts of Panda, Hummingbird, and RankBrain are the foundation for the next step in fighting low-quality content. An algorithm created by Google applies for the latest advances in natural language processing to evaluate text content and style. The search engine is better at identifying suitable keywords for generating organic results. Lack of context, clear subject matter, and style have become important signals when ranking pages.
