Sharpbelly Fish Optimization Algorithm: A Bio-Inspired Metaheuristic for Complex Engineering.

Area of Science:

• Computational Intelligence
• Bio-inspired Optimization
• Metaheuristic Algorithms

Background:

• Optimization problems are prevalent in science and engineering.
• Existing metaheuristic algorithms face challenges in balancing exploration and exploitation.
• Bio-inspired approaches offer novel strategies for complex optimization tasks.

Purpose of the Study:

• Introduce a novel bio-inspired metaheuristic algorithm, the sharpbelly fish optimizer (SFO).
• Evaluate SFO's performance on benchmark functions and engineering design problems.
• Demonstrate SFO's effectiveness in handling nonlinear, constrained, and multimodal optimization.

Main Methods:

• Develop the sharpbelly fish optimizer (SFO) based on four ecological behaviors of sharpbelly fish.
• Test SFO on the CEC2022 benchmark suite across various dimensions.
• Apply SFO to constrained engineering design problems: pressure vessel, speed reducer, and gear train.

Main Results:

• SFO achieved competitive or superior optimization accuracy and convergence speed compared to seven state-of-the-art algorithms.
• SFO demonstrated strong robustness and high solution quality in engineering design applications.
• The algorithm effectively balances global exploration and local exploitation.

Conclusions:

• The sharpbelly fish optimizer (SFO) is a promising general-purpose optimization tool.
• SFO shows significant potential for addressing complex real-world engineering problems.
• The bio-inspired strategies enhance SFO's ability to tackle challenging optimization tasks.