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Natural selection methods for artificial bee colony with new versions of onlooker bee

  • Mohammed A. Awadallah
  • Mohammed Azmi Al-Betar
  • Asaju La’aro Bolaji
  • Emad Mahmoud Alsukhni
  • Hassan Al-Zoubi
Methodologies and Application
  • 3 Downloads

Abstract

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms simulate the foraging behavior of honey bees in their hive. ABC starts with a colony of artificial bees with sole aim of discovering the place of food sources with high nectar amount using the solution search equation in the employed bee and onlooker bee operators. However, the solution search equation is good in exploration and poor in exploitation. In this paper, the solution search equation of the onlooker bee is modified by using a value of the fittest food sources selected by a set of selection schemes inspired from the evolutionary algorithms. This is to guide the search process of onlooker bee toward the fittest food sources from the population in order to empower the exploitation capability and convergence. Four selection schemes are incorporated with the ABC algorithm to choose the fittest food sources in four versions as follows: global-best, tournament, linear rank, and exponential rank. For evaluation purposes, 10 classical benchmark optimization functions are used to study the sensitivity analysis of each ABC algorithm to its parameters. The performance of the proposed ABC versions is compared with the original ABC version in order to study the effectiveness of the modifications. In addition, a comparative evaluation of ABC algorithms is carried out against the state-of-the-art methods that worked on CEC2005 benchmark functions, CEC2015 benchmark functions, and two real-world cases of economic load dispatch problem. The experimental results show that the selection schemes incorporated within the search equation of the onlooker bee directly affects the performance of ABC algorithm.

Keywords

Artificial bee colony Swarm intelligence Selection scheme Global optimization Economic load dispatch 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAl-Aqsa UniversityGazaPalestine
  2. 2.Department of Information TechnologyAl-Huson University College, Al-Balqa Applied UniversityAl-Huson, IrbidJordan
  3. 3.Department of Computer ScienceFederal University WukariWukariNigeria
  4. 4.Computer Science DepartmentYarmouk UniversityIrbidJordan
  5. 5.Department of mathematics, Faculty of Science and Information TechnologyAl-Zaytoonah UniversityAmmanJordan

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