The hybrid bacterial foraging algorithm based on many-objective optimizer.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• Multi-objective optimization problems present challenges in balancing competing objectives.
• Existing algorithms often struggle with convergence and diversity in many-objective scenarios.
• Bacterial foraging algorithms offer a biologically inspired approach to optimization.

Purpose of the Study:

• To introduce a new hybrid algorithm, the Hybrid Multi-Objective Optimized Bacterial Foraging Algorithm (HMOBFA).
• To enhance the performance of multi-objective optimization by combining novel strategies.
• To address the limitations of classical methods in handling many-objective problems.

Main Methods:

• The HMOBFA integrates a crossover-archives strategy with distinct selection principles for diversity and convergence.
• A life-cycle optimization strategy is employed to ensure population variability and avoid redundant searches.
• The algorithm's performance is evaluated using standard criterion functions.

Main Results:

• The HMOBFA demonstrates significant performance enhancements compared to classical multi-objective methods.
• The algorithm effectively handles many-objective issues, showing improvements in complexity, convergence, and diversity.
• Comparative analysis confirms the superiority of HMOBFA over existing approaches.

Conclusions:

• The HMOBFA is a highly effective algorithm for multi-objective optimization.
• It offers a robust and improved alternative for solving many-objective problems.
• The hybrid approach successfully balances convergence and diversity in complex optimization tasks.