Two modified Artificial Bee Colony algorithms inspired by Grenade Explosion Method

• Two modified versions of Artificial Bee Colony (ABC) algorithm inspired by Grenade Explosion Method (GEM) are proposed.
• Experiments show that the new algorithms have similar performance and outperform the competitors.
• The new algorithms can effectively enhance ABC׳s exploitation ability.
• The new algorithms can effectively solve global optimization problems.
• It is necessary for an algorithm to provide adequate random solutions in early cycles.

Artificial Bee Colony (ABC) algorithm, a popular swarm intelligence technique based on the intelligent foraging behavior of honey bees, is good at exploration but poor at exploitation. Grenade Explosion Method (GEM) which mimics the mechanism of a grenade explosion has high reliability and fast convergence. Two modified versions of ABC inspired by GEM, namely GABC1 and GABC2, are first proposed to enhance the classical ABC׳s exploitation ability. GEM is embedded in the employed bees׳ phase of GABC1, whereas it is embedded in the onlooker bees׳ phase of GABC2. The performance differences between GABC1 and GABC2 were assessed on two sets of well-known benchmark functions and compared with that of the classical ABC and several other improved ABC algorithms. The experiments show that GABC1 has similar or better performance than GABC2 in most cases, but GABC2 performs more robust and effective than GABC1 on all the functions, they significantly outperform the competitors. These results suggest that the proposed algorithms can effectively serve as alternatives for solving global optimization problems.