Flood algorithm: a novel metaheuristic algorithm for optimization problems

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• Metaheuristic algorithms are crucial for solving complex optimization problems efficiently.
• Algorithm performance varies across problem types, necessitating novel approaches.
• Existing methods require continuous development and validation.

Purpose of the Study:

• To introduce a novel metaheuristic algorithm, the flood algorithm (FA).
• To evaluate the FA's effectiveness on benchmark functions and a practical exam seating plan problem.
• To compare FA's performance against established metaheuristic algorithms.

Main Methods:

• Development of the flood algorithm (FA) based on natural flood water dynamics.
• Testing the FA on standard optimization benchmark functions.
• Application of the FA to a real-world exam seating arrangement problem.
• Comparative analysis of FA with other leading metaheuristic algorithms.

Main Results:

• The flood algorithm (FA) demonstrated competitive performance.
• FA achieved comparable solution accuracy to existing algorithms.
• FA exhibited efficient processing times, similar to other methods.

Conclusions:

• The proposed flood algorithm (FA) is a viable metaheuristic for optimization.
• FA offers a promising alternative for complex problem-solving.
• Further research can explore FA's application in diverse optimization domains.