A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer.

Area of Science:

• Computational Intelligence
• Nature-Inspired Algorithms
• Optimization Techniques

Background:

• Optimization problems are prevalent across scientific and engineering disciplines.
• Existing algorithms often face challenges with high-dimensional or constrained problems.
• Nature-inspired algorithms offer novel approaches to tackle complex optimization tasks.

Purpose of the Study:

• Introduce a novel nature-inspired optimization algorithm, the Grizzly Bear Fat Increase Optimizer (GBFIO).
• Mimic grizzly bear foraging and survival behaviors to develop a new optimization strategy.
• Evaluate the GBFIO's effectiveness on diverse benchmark functions and engineering design problems.

Main Methods:

• Modeled three key grizzly bear behaviors: food sourcing, hunting/defense, and fishing.
• Applied the GBFIO to 31 standard benchmark functions and 30 CEC2017 test functions.
• Tested the algorithm on four constrained engineering design problems (spring, beam, vessel, reducer).

Main Results:

• The GBFIO effectively solved various optimization problems, particularly high-dimensional ones.
• Demonstrated strong performance across unimodal, multimodal, and rotated/shifted benchmark functions.
• Achieved superior or competitive results compared to other established optimization algorithms.

Conclusions:

• The Grizzly Bear Fat Increase Optimizer (GBFIO) is a robust and efficient new optimization algorithm.
• GBFIO shows significant potential for solving complex, high-dimensional, and constrained optimization problems.
• The algorithm's nature-inspired approach offers a competitive alternative in the field of computational intelligence.