Optimization

Hunger Games Search

Hunger Games Search (HGS) is a general-purpose population-based optimization technique with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of “Hunger” as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods.

Implementation of the HGS algorithm is available at https://aliasgharheidari.com/HGS.html.

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Few-Shot Learning 2 22.22%
Meta-Learning 2 22.22%
Combinatorial Optimization 2 22.22%
Domain Adaptation 1 11.11%
Graph Learning 1 11.11%
Efficient Exploration 1 11.11%

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🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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