Convergence analysis of flow direction algorithm and its improvement

Flow direction algorithm (FDA) is a new physics-based optimization algorithm for solving global optimization problems. Although the FDA has shown effectiveness in many areas, there has been a lack of rigorous theoretical guarantees. This paper first proves that FDA is globally convergent with probab...

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Bibliographic Details
Published in:Soft computing (Berlin, Germany) Germany), 2023-10, Vol.27 (20), p.14791-14818
Main Authors: Ye, Wenwen, Li, Shengping
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Benchmarks
Computational Intelligence
Control
Convergence
Effectiveness
Engineering
Genetic algorithms
Global optimization
Guarantees
Heuristic
Markov processes
Mathematical Logic and Foundations
Mechatronics
Normal distribution
Optimization
Optimization algorithms
Physics
Robotics
Statistical analysis
Statistical tests
Stochastic models
Wireless sensor networks
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Summary:Flow direction algorithm (FDA) is a new physics-based optimization algorithm for solving global optimization problems. Although the FDA has shown effectiveness in many areas, there has been a lack of rigorous theoretical guarantees. This paper first proves that FDA is globally convergent with probability 1 by establishing a Markov process model. Furthermore, to enhance the FDA’s exploration and exploitation abilities, we propose an improved FDA algorithm (IFDA) by introducing random opposition-based learning and an adaptive neighbour generation strategy. Finally, extensive experiments and statistical tests are investigated on the classical benchmark functions, CEC 2019 benchmark function, and wireless sensor network coverage optimization problem with several state-of-the-art algorithms, demonstrating the proposed algorithm’s efficiency and effectiveness.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08551-9