In recommender systems, it is well-established that both accuracy and diversity are crucial for generating high-quality recommendation lists. However, achieving a balance between these two typically conflicting objectives remains a significant challenge. In this work, we address this challenge by proposing four novel hybrid multi-objective algorithms inspired by the Non-dominated Neighbor Immune Algorithm (NNIA), Archived Multi-Objective Simulated Annealing (AMOSA), and Non-dominated Sorting Genetic Algorithm-II (NSGA-II), aimed at simultaneously enhancing both accuracy and diversity through multi-objective optimization. Our approach follows a three-stage process: First, we generate an initial top-s list using item-based collaborative filtering for a given user. Second, we solve a bi-objective optimization problem to identify Pareto-optimal top-k  recommendation lists, where s<<k, using the proposed hybrid algorithms. Finally, we select an optimal personalized top-s list from the Pareto-optimal solutions. We evaluate the performance of the proposed algorithms on real-world datasets and compare them with existing methods using conventional metrics in recommender systems such as accuracy, diversity, and novelty. Additionally, we assess the quality of the Pareto frontiers using metrics including the spacing metric, mean ideal distance, diversification metric, and spread of non-dominated solutions. Results demonstrate that some of our proposed algorithms significantly improve both accuracy and diversity, offering a novel contribution to multi-objective optimization in recommender systems.