A novel artificial bee colony optimiser with dynamic population size for multi-level threshold image segmentation
by Lianbo Ma; Xingwei Wang; Hai Shen; Min Huang
International Journal of Bio-Inspired Computation (IJBIC), Vol. 13, No. 1, 2019

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.

Online publication date: Wed, 06-Feb-2019

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