Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Bio-inspired Computing

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems.
• Existing algorithms often struggle with balancing exploration and exploitation.
• Nature-inspired mechanisms offer novel approaches to algorithm design.

Purpose of the Study:

• Introduce a new bio-inspired metaheuristic algorithm, the Pufferfish Optimization Algorithm (POA).
• To model POA based on the pufferfish's defense mechanism against predators.
• To evaluate POA's effectiveness in solving benchmark and real-world optimization problems.

Main Methods:

• Mathematical modeling of POA in two phases: exploration (predator attack) and exploitation (predator escape).
• Performance evaluation using the CEC 2017 test suite across various dimensions (10, 30, 50, 100).
• Comparative analysis against twelve established metaheuristic algorithms and real-world engineering problems.

Main Results:

• POA effectively balances exploration and exploitation during the search process.
• POA achieved superior performance on the CEC 2017 test suite compared to twelve other algorithms.
• POA demonstrated effective performance on constrained optimization problems and engineering design tasks.

Conclusions:

• The Pufferfish Optimization Algorithm (POA) is a promising new metaheuristic.
• POA's bio-inspired approach provides a robust and effective optimization strategy.
• POA offers a competitive alternative for both theoretical and practical optimization challenges.