An Improved Crested Porcupine Optimization Algorithm Incorporating Butterfly Search and Triangular Walk Strategies.

Area of Science:

• Computational Intelligence
• Swarm Intelligence Algorithms
• Optimization Techniques

Background:

• The Crested Porcupine Optimizer (CPO) is a novel swarm intelligence algorithm effective for balancing exploration and exploitation.
• However, CPO faces limitations in convergence speed and precise local exploitation.

Purpose of the Study:

• To introduce an enhanced variant, the Butterfly Search and Triangular Walk Crested Porcupine Optimizer (BTCPO).
• To address the limitations of CPO by improving convergence speed and local exploitation precision.

Main Methods:

• BTCPO integrates Triangular Walk for enhanced local exploitation.
• Butterfly Search is incorporated to increase global exploration diversity.
• The algorithm dynamically balances exploration and exploitation capabilities.

Main Results:

• BTCPO demonstrated superior performance against CPO and seven other state-of-the-art algorithms on 23 benchmark functions and the CEC2021 test suite.
• Convergence speed was improved by approximately 25% compared to the original CPO.
• BTCPO showed high efficiency and utility in solving engineering design problems like truss, welded beam, and cantilever beam optimization.

Conclusions:

• BTCPO offers significant theoretical and practical advantages over existing optimization algorithms.
• The proposed BTCPO is a robust and effective approach for tackling complex optimization challenges.