Multi-objective Baby Search Algorithm.

Multi-objective optimization problems are commonplace in real-world applications, and evolutionary algorithms are successful in solving them. Baby search algorithm is a novel evolutionary algorithm proposed recently, which has excellent ability on exploration and exploitation. However, it is designed to cater to single-objective optimization problems, but in this paper, we expand and modify it for multi-objective optimization. We introduce the boundary selection strategy to choose individuals from the Pareto archive for generating new solutions. To determine the best position of each individual we combine Pareto domination relation with random selection. Additionally, we propose an adapted Levy flight method to find promising solutions. Eleven standard multi-objective testing instances, five prevailing indicators and five state-of-art algorithms are applied to evaluate our algorithm. Experiments results demonstrate that our algorithm performs well on IGD, HV, Spread and GD measures.