The Pine Cone Optimization Algorithm (PCOA)

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Nature-Inspired Computing

Background:

• Optimization problems are prevalent across science and engineering disciplines.
• Existing nature-inspired algorithms often require further enhancement for complex challenges.
• Novel approaches are needed to improve efficiency and accuracy in computational problem-solving.

Purpose of the Study:

• To introduce a novel nature-inspired optimization algorithm, the Pine Cone Optimization algorithm (PCOA).
• To simulate pine tree reproduction mechanisms for developing advanced optimization operators.
• To evaluate PCOA's performance on benchmark mathematical and engineering design problems.

Main Methods:

• Development of the Pine Cone Optimization algorithm (PCOA) based on pine tree reproduction (pollination, seed dispersal).
• Utilizing novel operators to mimic natural reproductive processes.
• Performance evaluation using standard benchmark functions (CEC2017, CEC2019) and engineering design problems (CEC2006, CEC2011).

Main Results:

• PCOA exhibited superior accuracy compared to well-known algorithms like PSO, DE, and WOA.
• PCOA demonstrated competitive results against state-of-the-art algorithms such as LSHADE and EBOwithCMAR.
• The algorithm showed reasonable convergence speed and time complexity, with a high rank in the Friedman test.

Conclusions:

• The Pine Cone Optimization algorithm (PCOA) is a highly effective tool for addressing complex optimization tasks.
• PCOA offers a promising alternative for researchers and practitioners in science, engineering, and industry.
• The algorithm's performance indicates its potential for real-world applications requiring robust optimization solutions.