A Modified Marriage in Honey-Bee Optimization for Function Optimization Problems

This paper proposes a modified marriage in honey-bee optimization for solving multiobjective optimization problems. Unlike the original marriage in honey-bee optimization, the proposed algorithm divides the objective space into several colonies, each of which has its own queen. The fitness of each solution is based on 3 parameters: the size of the colony, the number of dominating solutions, and the number of dominated solutions. The nondominated solutions with highest fitness values are preferentially assigned to be the queens while the rest are assigned to be the drones. Next, all drones are assigned to the colony according to their distances from the queens of the colonies. In order to maximize a genetic variance in the population, the multiple mating is used. The multiple mating requires the queen to mate with drones from the other colonies. The proposed algorithm has been evaluated and compared to two state-of-the-art metaheuristic algorithms: the Pareto archived evolution strategy and the nondominated sorting genetic algorithm. The experimental results on 5 different ZDT benchmark functions illustrate that the proposed algorithm is able to converge to the true Pareto fronts and has better spread of solutions, as compared with the published results of the two state-of-the-art algorithms.