A discrete wild horse optimizer for capacitated vehicle routing problem.

Area of Science:

• Operations Research
• Computer Science
• Artificial Intelligence

Background:

• The Wild Horse Optimizer (WHO) is a metaheuristic algorithm effective for continuous engineering problems.
• The Capacitated Vehicle Routing Problem (CVRP) is a complex combinatorial optimization challenge.
• Existing algorithms for CVRP have limitations in precision and quality of solutions.

Purpose of the Study:

• To propose a discrete version of the Wild Horse Optimizer (DWHO) tailored for the CVRP.
• To enhance the precision and solution quality of the WHO algorithm for discrete problems.
• To evaluate the performance of DWHO against other established algorithms for CVRP.

Main Methods:

• Developed the Discrete Wild Horse Optimizer (DWHO) by adapting the original WHO algorithm.
• Integrated three local search strategies: swap, reverse, and insertion operations.
• Incorporated the largest-order-value (LOV) decoding technique to improve solution representation.

Main Results:

• DWHO demonstrated superior solving capability compared to the basic Wild Horse Optimizer (BWHO) on most benchmark instances.
• Experimental results showed DWHO outperforming other algorithms like hybrid firefly, DSRACO, and DAEO.
• The proposed local search and decoding techniques significantly enhanced solution precision and quality.

Conclusions:

• DWHO offers a novel and effective approach for solving the Capacitated Vehicle Routing Problem.
• The discrete optimization framework developed can be applied to other complex discrete problems.
• The integration of local search and LOV decoding provides a robust method for enhancing metaheuristic performance.