A multi-objective artificial bee colony algorithm based on division of the searching space

This paper presents a new multi-objective artificial bee colony algorithm called dMOABC by dividing the whole searching space S into two independent parts S 1 and S 2. In this algorithm, two ”basic” colonies are assigned to search potential solutions in regions S 1 and S 2, while the so-called ”synthetic” colony explores in S. This multi-colony model could enable the good diversity of the population, and three colonies share information in a special way. A fixed-size external archive is used to store the non-dominated solutions found so far. The diversity over the archived solutions is controlled by utilizing a self-adaptive grid. For basic colonies, neighbor information is used to generate new food sources. For the synthetic colony, besides neighbor information, the global best food source gbest selected from the archive, is also adopted to guide the flying trajectory of both employed and onlooker bees. The scout bees are used to get rid of food sources with poor qualities. The proposed algorithm is evaluated on a set of unconstrained multi-objective test problems taken from CEC09, and is compared with 11 other state-of-the-art multi-objective algorithms by applying Friedman test in terms of four indicators: HV, SPREAD, EPSILON and IGD. It is shown by the test results that our algorithm significantly surpasses its competitors.