A multi-objective African vultures optimization algorithm with binary hierarchical structure and tree topology for...

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Big Data Analytics

Background:

• Big data optimization (Big-Opt) problems pose significant challenges due to the scale and complexity of large datasets.
• Traditional data processing methods are often insufficient for effectively managing and optimizing big data.

Purpose of the Study:

• To introduce a novel multi-objective optimization algorithm, the multi-objective African vulture optimization algorithm with binary hierarchical structure and tree topology (MO_Tree_BHSAVOA).
• To address the unique challenges presented by big data optimization problems.

Main Methods:

• Incorporation of a binary hierarchical structure (BHS) to balance exploration and exploitation.
• Utilizing shift density estimation for selection pressure in population evolution.
• Employing a tree topology to enhance escape from local optima and preserve non-dominated solutions.

Main Results:

• The MO_Tree_BHSAVOA demonstrated highly competitive performance.
• Statistical analysis using Friedman's test confirmed the superiority of MO_Tree_BHSAVOA over other multi-objective optimization algorithms.
• The algorithm was evaluated on benchmark functions and real-world constrained problems.

Conclusions:

• The proposed MO_Tree_BHSAVOA is effective for big data optimization.
• The algorithm shows significant potential for addressing complex optimization challenges in big data environments.