Artificial Optimizer Algorithm for Power System Stabilizer design problem and multidisciplinary engineering...

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• Nature-inspired algorithms are crucial for solving complex optimization problems.
• Existing algorithms often struggle to balance exploration and exploitation effectively.
• The social behavior of wildlife offers a rich source for novel algorithmic designs.

Purpose of the Study:

• To introduce a novel stochastic optimization algorithm, the Velociraptor Group Optimization algorithm (VROA).
• To mathematically model the cooperative behavior of velociraptors for optimization.
• To evaluate VROA's efficacy in exploration and exploitation capabilities.

Main Methods:

• Developed the Velociraptor Group Optimization algorithm (VROA) based on velociraptor social intelligence.
• Implemented novel mechanisms for search (exploration) and hunting (exploitation).
• Tested VROA on 51 benchmark functions from CEC'17, CEC'20, and CEC'22, plus six engineering optimization problems.

Main Results:

• VROA demonstrated a noteworthy ability to balance exploration and exploitation.
• Statistical analyses using Wilcoxon rank-sum and Friedman's tests confirmed VROA's superiority.
• VROA outperformed recent optimizers on most standard test suites, achieving competent constrained solutions.

Conclusions:

• The Velociraptor Group Optimization algorithm (VROA) is an effective and accurate metaheuristic.
• VROA offers a robust approach to solving complex engineering and benchmark optimization problems.
• The algorithm shows significant potential for future applications in computational intelligence.