EPO (Emperor Penguin Optimization)

Information

• EPO (Emperor Penguin Optimization) is based on the observation that emperor penguins exhibit a group foraging behavior that helps them find food in the harsh Antarctic environment.
• EPO uses a population of solutions, called penguins, that move through the search space in search of the optimum solution.
• The penguins are divided into two groups: leaders and followers. Leaders explore the search space while followers exploit the already discovered regions.
• EPO introduces a new concept of "huddling", inspired by the way emperor penguins form groups to conserve heat. The huddling process helps the penguins share information and improve their search process.
• EPO also incorporates an adaptive weight factor to balance exploration and exploitation of the search space.
• The algorithm is easy to implement and has shown promising results in solving a range of optimization problems, including function optimization, feature selection, and classification.