Adaptive Bee Colony in an Artificial Bee Colony for Solving Engineering Design Problems

TL;DR

This paper introduces adaptive and elitist variants of the Artificial Bee Colony algorithm, called A-ABC and E-ABC, which improve convergence speed and solution quality for engineering design problems.

Contribution

The study proposes adaptive population sizing and elitism strategies to enhance ABC's performance, validated on benchmarks and engineering problems.

Findings

A-ABC adapts colony size for better performance.

E-ABC accelerates convergence using elitism.

Proposed methods outperform basic and recent ABC variants.

Abstract

A wide range of engineering design problems have been solved by the algorithms that simulates collective intelligence in swarms of birds or insects. The Artificial Bee Colony or ABC is one of the recent additions to the class of swarm intelligence based algorithms that mimics the foraging behavior of honey bees. ABC consists of three groups of bees namely employed, onlooker and scout bees. In ABC, the food locations represent the potential candidate solution. In the present study an attempt is made to generate the population of food sources (Colony Size) adaptively and the variant is named as A-ABC. A-ABC is further enhanced to improve convergence speed and exploitation capability, by employing the concept of elitism, which guides the bees towards the best food source. This enhanced variant is called E-ABC. The proposed algorithms are validated on a set of standard benchmark problems with varying dimensions taken from literature and on five engineering design problems. The numerical results are compared with the basic ABC and three recent variant of ABC. Numerically and statistically simulated results illustrate that the proposed method is very efficient and competitive.