Improved Energy Valley Optimizer with Levy Flight for Optimization Problems
Revolutionizing Optimization: The Levy-Enhanced Energy Valley Optimizer (LEVO)
Optimization algorithms have become the cornerstone of solving complex mathematical and engineering problems. The Levy-enhanced Energy Valley Optimizer (LEVO) represents a groundbreaking hybrid approach that combines the foundational principles of the Energy Valley Optimizer (EVO) with Levy flights for superior performance.
Key Innovations in LEVO
LEVO builds on the EVO, which is inspired by the stability and decay of particles in physics. By integrating Levy flights, which involve random walks with variable step lengths, LEVO enhances the exploration of the solution space, effectively avoiding local optima. Key features include:
- Enhanced Exploration: Levy flights enable large jumps, expanding the search space and identifying optimal solutions more efficiently.
- Broad Applicability: LEVO outperforms traditional algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA) in 15 benchmark tests.
- Efficiency: LEVO demonstrated exceptional performance in both small and large-dimensional problems, achieving results comparable to higher-resource configurations with fewer iterations.
Performance Insights
LEVO was tested against unimodal, multimodal, and composite benchmark functions, showcasing superior performance metrics:
- Unimodal Functions: LEVO consistently minimized objective functions with higher precision.
- Multimodal Functions: LEVO adeptly navigated complex landscapes with multiple optima, outperforming alternative methods in terms of accuracy and computational efficiency.
- Composite Functions: The algorithm excelled in solving intricate mathematical formulations, achieving convergence faster than its predecessors.
Practical Implications
LEVO’s enhanced efficiency and effectiveness make it a robust tool for tackling large-scale optimization problems in fields such as data science, engineering, and resource management
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Tags:
- Optimization Algorithms
- Energy Valley Optimizer
- Levy Flight
- Metaheuristics
- Computational Efficiency
Figure 1: (A) particles stability band (B) Emission process (C) Three types of decay [1].
Figure 2: Different forms of decay [1].
Figure 3: Convergence curves of algorithms on six of the test functions.
Figure 4: Behaviors of LEVO in small and large dimensions in unimodal test functions.
Figure 5: Behaviors of LEVO in small and large dimensions in multimodal test functions.