Title: A novel artificial bee colony optimiser with dynamic population size for multi-level threshold image segmentation

Authors: Lianbo Ma; Xingwei Wang; Hai Shen; Min Huang

Addresses: College of Software, Northeastern University, Shenyang, 110819, China ' College of Software, Northeastern University, Shenyang, 110819, China ' College of Physics Science and Technology, Shenyang Normal University, Shenyang, 110034, China ' College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

Abstract: Existing swarm intelligence (SI) models are usually derived from fixed-population biological system. However, this approach inevitably causes unnecessary computational cost. In addition, the population size of these models is usually hard to be pr-determined appropriately. In this contribution, this paper exploits a general varying-population swarm model (VPSM) with life-cycle foraging rules based on the population growth dynamic principle. This model essentially improves individual-level adaptability and population-level emergence to self-adapt towards an optimal population size. Then, a novel VPSM-based artificial bee colony optimiser is instantiated with orthogonal Latin squares approach and crossover-based social learning strategies. A comprehensive experimental analysis is implemented in which the proposed algorithm is benchmarked against classical bio-mimetic algorithms on CEC2014 test suites. Then, this algorithm is applied for multi-level image segmentation. Computation results show the performance superiority of the proposed algorithm.

Keywords: artificial bee colony algorithm; population varying; image segmentation.

DOI: 10.1504/IJBIC.2019.097723

International Journal of Bio-Inspired Computation, 2019 Vol.13 No.1, pp.32 - 44

Accepted: 17 Jun 2016
Published online: 06 Feb 2019 *

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