A bio-inspired algorithm which mimic the huddling behavior of Emperor Penguin.
Video Chapters:
Introduction: 00:00
What is emperor penguin optimization: 00:23
Emperor Penguin: 00:58
Emperor Penguin huddle: 01:50
Emperor Penguin Optimizer Flowchart: 03:19
Generate Emperor Penguin huddle boundary: 04:34
Calculate Temperature around huddle: 06:18
Calculate Distance between Emperor Penguins: 07:45
Relocate Effective Mover: 09:45
Emperor Penguin Optimization Steps: 10:52
Conclusion: 12:38
Emperor Penguin Optimizer is a Novel Bio-Inspired Metaheuristic Algorithm which is inspired by the huddling behavior of Emperor Penguin. Penguins are Aquatic Flightless Birds. Penguins spends their 50% life on the land and 50% life in the water. Penguin largest species is known as Emperor Penguin. Both male and female emperor penguins are similar in size. Emperor penguin is the only species which use huddle for their survival in Antarcticwinter. Huddle is used as a defense against cold to survive through though Antarctic winter. Huddling is used to conserve energy and increase temperature during winter. Without Huddle they lose energy and freeze.
Four Important phases to describehuddling behavior of Emperor penguins in Emperor Penguin Optimization Algorithm:
Step 01: Generate and determine the huddle boundary for Emperor Penguins.
Step 02: Calculate temperature around the Huddle.
Step 03: Determine the distance between the Emperor Penguins.
Step 04: Find out Effective Mover and Relocate.
Emperor Penguin Optimizer Pseudocode:Step 01: Initialize the Emperor Penguins population randomly in the search space.
Step 02: Initialize all important initial parameters such as Maximum Iterations, Temperature, A, C.
Step 03: Calculate fitness values for all search agents.
Step 04: Determine the huddle boundary for Emperor penguins using:
Step 05: Calculate temperature profile (T') around the huddle using:
Step 06: Compute distance between the emperor penguins using:
Step 07: Update the position of emperor penguins.
Step 08: If any emperor penguin goes beyond the huddle boundary improve its position.
Step 09: Calculate fitness values for each search agent and update new optimal solution position.
Step 10: If stopping criteria met Stop else Goto Step 05.
Step 11: Return Best Emperor Penguins/ Optimal Solutions.
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