An improved global best guided artificial bee colony algorithm for continuous optimization problems

Artificial bee colony (ABC) has proved to be a good optimizer on many engineering problems. However, the standard ABC converges slowly because of poor exploitation. Recently, a new ABC variant called global best guided ABC (namely GABC) was proposed. Some simulation studies proved that GABC could find more accurate solutions than the standard ABC. In this paper, an improved ABC (namely IABC) is proposed to strengthen the performance of GABC. In IABC, the onlooker bees use a modified solution search equation to improve the exploitation capability. Experiment verification is carried out on ten classical benchmark functions. Results show our new approach IABC is superior to the standard ABC, GABC and another improved ABC variant.