Coevolutionary Structure-Redesigned-Based Bacterial Foraging Optimization.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• Standard Bacterial Foraging Optimization (BFO) can be computationally intensive.
• Cooperation is a natural phenomenon observed in living organisms for task efficiency.

Purpose of the Study:

• To develop a computationally efficient optimization algorithm.
• To maintain the search capabilities of BFO while reducing complexity.
• To enhance convergence speed and accuracy through cooperative strategies.

Main Methods:

• Introduced Coevolutionary Structure-Redesigned-Based Bacteria Foraging Optimization (CSRBFO).
• Replaced nested loops with a general loop and eliminated the reproduction step of BFO.
• Incorporated a coevolutionary strategy for cooperative learning during chemotaxis.
• Implemented a three-stage evolutionary strategy with diverse learning methods in the elimination & dispersal step.
• Added convergence status evaluation to guide evolutionary stages.

Main Results:

• CSRBFO demonstrated significantly superior performance compared to seven well-known heuristic algorithms.
• The algorithm was tested on 24 benchmark functions, showing strong results in most cases.
• The combination of coevolutionary strategy and convergence evaluation effectively balanced exploration and exploitation.

Conclusions:

• CSRBFO offers a more efficient and effective approach to optimization problems.
• The proposed modifications successfully reduce computational complexity without sacrificing critical search capabilities.
• The coevolutionary approach and adaptive strategies enhance the algorithm's performance in complex search spaces.