Artificial Bee Colony Optimizer Based on Bee Life-Cycle for Stationary and Dynamic Optimization

This paper proposes a novel optimization scheme by hybridizing an artificial bee colony optimizer (HABC) with a bee life-cycle mechanism, for both stationary and dynamic optimization problems. The main innovation of the proposed HABC is to develop a cooperative and population-varying scheme, in which individuals can dynamically shift their states of birth, foraging, death, and reproduction throughout the artificial bee colony life cycle. That is, the bee colony size can be adjusted dynamically according to the local fitness landscape during algorithm execution. This new characteristic of HABC helps to avoid redundant search and maintain diversity of population in complex environments. A comprehensive experimental analysis is implemented that the proposed algorithm is benchmarked against several state-of-the-art bio-inspired algorithms on both stationary and dynamic benchmarks. Then the proposed HABC is applied to the real-world applications including data clustering and image segmentation problems. Statistical analysis of all these tests highlights the significant performance improvement due to the life-cycle mechanism and shows that the proposed HABC outperforms the reference algorithms.

I. Introduction

Swarm intelligence (SI) as classical bio-inspired computational intelligence draws inspiration from collective behaviors exhibited by animal groups [1]–[7]. Generally, each SI paradigm is a population of unsophisticated individuals that possesses a collective intelligence. The individuals within such population can interact locally with their environment to cause coherent functional patterns emerge. It is worthy noted that in most SI algorithms, their populations are usually blindly determined by using a fixed size, which is a departure from the life-cycle mode of evolutionary mechanisms of biological populations.