In this paper, we extend the chicken swarm optimization (CSO) to solve multi-objective optimization problems. Our extention aims to balance between diversity and convergence when searching for the optimal Pareto solutions. We use aggregation function to define the social hierarchy and simulate the behavior of chickens during the search for food in the objective search space while applying epsilon dominance and crowding distance to preserve the diversity of the solutions population. We also address the integration of the archive population that guides the chicken swarm towards the Pareto optimal solutions.

The proposed algorithm is validated on twelve test functions and compared with five well-known meta-heuristics. The results show the ability of MOCSO algorithm to provide a better spread of solutions with faster convergence.