A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem.

Area of Science:

• Operations Research
• Computer Science
• Artificial Intelligence

Background:

• The quadratic assignment problem (QAP) is a computationally challenging NP-hard optimization problem with diverse real-world applications.
• Biogeography-based optimization (BBO) is an emerging metaheuristic inspired by species migration, known for competitive performance.
• Classical BBO's mutation operator can degrade solution quality for QAP.

Purpose of the Study:

• To enhance the performance of biogeography-based optimization for solving the quadratic assignment problem.
• To address the limitations of the standard mutation operator in BBO when applied to QAP.

Main Methods:

• A hybrid optimization approach was developed, integrating Biogeography-based Optimization (BBO) with Tabu Search.
• The mutation operator in the classical BBO algorithm was replaced by a Tabu Search procedure.
• The proposed hybrid method was evaluated using benchmark instances from the QAPLIB library.

Main Results:

• The hybrid BBO-Tabu Search method demonstrated superior performance in solving QAP instances.
• Experiments showed the method finds high-quality solutions within practical computational timeframes.
• Out of 61 tested QAPLIB instances, the hybrid approach achieved the best known solutions for 57.

Conclusions:

• The proposed hybrid technique effectively overcomes the weaknesses of classical BBO for QAP.
• This novel approach offers a robust and efficient method for tackling complex quadratic assignment problems.
• The results highlight the potential of hybrid metaheuristics in combinatorial optimization.