A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

Abstract

Nature-inspired optimization (NIO) algorithms have gained quite a popularity among the researchers due to their good performance on difficult optimization problems. Recently, machine learning (ML) algorithms dealing with the generation of knowledge automatically from data have been often integrated into NIO algorithms to enhance their performance. One of the widely used popular NIO algorithms is an artificial bee colony (ABC) algorithm mimicking the intelligent foraging behaviour of real honeybees. In order to improve the performance of standard ABC, some hybridization studies of ABC and ML techniques have been performed to introduce more intelligent versions of ABC that can be used for solving the optimization problems arising in ML and other areas. This study presents a survey on the studies combining ABC with ML techniques for enhancing the performance of ABC algorithm and provides a discussion on how ML techniques have been adapted so far and can be employed for improving ABC further. We hope that this study would be very helpful for the researchers dealing with ML and NIO algorithms, particularly ABC.

Keywords:

• artificial intelligence
• nature-inspired optimization
• swarm intelligence
• artificial bee colony
• machine learning
• hybridization

PUBLIC INTEREST STATEMENT

One of the widely used nature-inspired algorithms is an artificial bee colony (ABC) algorithm mimicking the intelligent foraging behaviour of real honeybees. In order to improve the performance of standard ABC, some hybridization studies of ABC and machine learning (ML) techniques have been performed to introduce more intelligent versions of ABC, which can be used for solving the optimization problems arising in ML and other areas. This study presents a survey on the studies combining ABC with ML techniques for enhancing the performance of ABC algorithm and provides a discussion on how ML techniques have been adapted so far and can be employed for improving ABC further.

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Dervis Karaboga

Dervis Karaboga received a Ph.D. degree from Systems Engineering Department of University of Wales, College of Cardiff, UK, in 1994. His general scientific study field is artificial intelligence. Due to his scientific studies, he achieved to be in the list of “highly cited researchers” in the years of 2016–2020.

Bahriye Akay

Bahriye Akay received a Ph.D. degree in Computer Engineering from Erciyes University, Turkey in 2009. Her research areas include artificial intelligence, evolutionary computation, swarm intelligence techniques and software engineering. She has been selected “Highly Cited Researcher” in 2018.

Nurhan Karaboga

Nurhan Karaboga received a Ph.D. degree in electronics engineering from Erciyes University, Turkey in 1995. Currently, she works as a Professor in the same department. From 1992 to 1994, she also worked as an academic visitor at the University of Wales College of Cardiff, UK. Her current research interests include numerical methods, evolutionary computation algorithms, digital filter design and compressing sensing.