Adaptive Bee Colony in an Artificial Bee Colony for Solving Engineering Design Problems
Tarun Kumar Sharma, Millie Pant, V. P. Singh

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Multi-Objective Optimization Algorithms
