# An Improved Artificial Bee Colony Algorithm with a Probabilistic Crossover and Lock Mechanism

## TL;DR

This paper improves the Artificial Bee Colony algorithm to better solve complex resource allocation problems in liquid transportation.

## Contribution

The novel integration of a probabilistic crossover and a gene-level lock mechanism enhances ABC's performance in discrete optimization.

## Key findings

- Combining crossover and lock mechanisms reduced mean cost to 14.94, outperforming alternatives.
- The improved ABC achieved lower costs than manual planning and other metaheuristics.
- The method showed consistent performance across different dataset sizes.

## Abstract

The Artificial Bee Colony (ABC) algorithm is a simple and effective population-based optimization method, but it may exhibit unstable convergence and weak exploitation capability in discrete and highly constrained problems. This study proposes an improved ABC framework that integrates a probabilistic Uniform crossover operator and a gene-level lock mechanism to enhance convergence stability and local refinement. The framework is applied to an integrated multi-resource allocation problem in liquid transportation, which has not previously been addressed within the ABC literature. The problem requires the simultaneous assignment of drivers, trucks, trailers, and ISO tanks under operational and regulatory constraints. Comparative analysis of different ABC configurations shows that integrating only Uniform crossover reduced the mean cost to 17.78, adding only the lock mechanism reduced it to 29.78, and combining both further decreased it to 14.94, indicating a complementary effect between the two mechanisms. The proposed configuration consistently achieved the lowest mean costs across small, medium, and large datasets. Compared with established metaheuristic algorithms and expert manual planning (34.72), the method produced lower-cost and feasible solutions, demonstrating both algorithmic robustness and practical relevance.