Channa argus optimizer for solving numerical optimization and engineering problems.

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• Meta-heuristic algorithms are crucial for solving complex optimization problems.
• Existing algorithms often face challenges with premature convergence and balancing exploration/exploitation.
• Nature-inspired algorithms offer novel approaches to optimization challenges.

Purpose of the Study:

• Introduce the Channa Argus Optimizer (CAO), a novel swarm-based meta-heuristic algorithm.
• Emulate the hunting and escaping behavior of Channa Argus to enhance optimization.
• Validate the algorithm's performance on benchmark functions and real-world engineering problems.

Main Methods:

• The Channa Argus Optimizer (CAO) algorithm is developed, mimicking Channa Argus behavior.
• CAO is tested on 29 CEC2017 and 10 CEC2020 unconstrained benchmark functions.
• Performance is evaluated across various dimensions (10-D to 100-D) and compared against 7 other algorithms using statistical tests (Wilcoxon rank-sum, Friedman mean rank).
• The algorithm's robustness is assessed on 5 real-world constrained mechanical engineering problems.

Main Results:

• The Channa Argus Optimizer (CAO) demonstrated superior performance compared to 7 other algorithms.
• CAO achieved outstanding results, ranking first in benchmark function tests.
• The algorithm proved effective and robust in solving complex mechanical engineering optimization problems.

Conclusions:

• The Channa Argus Optimizer (CAO) is a highly competitive and effective meta-heuristic algorithm.
• CAO successfully balances exploration and exploitation, mitigating premature convergence.
• The algorithm shows significant potential for addressing complex optimization challenges in engineering and beyond.