The artificial bee colony (ABC) algorithm
is a swarm-based metaheuristic optimization technique, developed by inspiring
foraging and dance behaviors of honey bee colonies. ABC consists of four phases
named as initialization, employed bee, onlooker bee and scout bee. The employed
bees try to improve their solution in employed bees phase. If an employed bee
cannot improve self-solution in a certain time, it becomes a scout bee. This
alteration is done in the scout bee phase. The onlooker bee phase is placed
where information sharing is done. Although a candidate solution improved by
onlookers is chosen among the employed bee population according to fitness
values of the employed bees, neighbor of candidate solution is randomly
selected. In this paper, we propose a selection mechanism for neighborhood of
the candidate solutions in the onlooker bee phase. The proposed selection
mechanism was based on information shared by the employed bees. Average fitness
value obtained by the employed bees is calculated and those better than the
aver- age fitness value are written to memory board. Therefore, the onlooker
bees select a neighbor from the memory board. In this paper, the proposed
ABC-based method called as iABC were applied to both five numerical benchmark
functions and an estimation of energy demand problem. Obtained results for the
problems show that iABC is better than the basic ABC in terms of solution
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