The paper introduces SPEA2, an improved version of the Strength Pareto Evolutionary Algorithm (SPEA) for multiobjective optimization. SPEA2 incorporates a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. These improvements aim to enhance the algorithm's performance and address the weaknesses of its predecessor. The main differences between SPEA and SPEA2 include: 1. **Fitness Assignment**: SPEA2 uses a more detailed fitness assignment scheme that considers the number of individuals an individual dominates and is dominated. 2. **Density Estimation**: It employs a nearest neighbor density estimation technique to incorporate density information. 3. **Archive Truncation**: A new archive truncation method is introduced to preserve boundary solutions. The paper also outlines the main loop of the SPEA2 algorithm, detailing the fitness assignment, environmental selection, and other key steps. Experimental results comparing SPEA2 with SPEA, PESA, and NSGA-II on various test problems show that SPEA2 consistently outperforms its predecessor and other modern elitist methods, particularly in terms of solution diversity and performance over time. The study concludes that SPEA2 is a significant improvement over SPEA and other algorithms, especially in multiobjective optimization problems with more objectives.The paper introduces SPEA2, an improved version of the Strength Pareto Evolutionary Algorithm (SPEA) for multiobjective optimization. SPEA2 incorporates a fine-grained fitness assignment strategy, a density estimation technique, and an enhanced archive truncation method. These improvements aim to enhance the algorithm's performance and address the weaknesses of its predecessor. The main differences between SPEA and SPEA2 include: 1. **Fitness Assignment**: SPEA2 uses a more detailed fitness assignment scheme that considers the number of individuals an individual dominates and is dominated. 2. **Density Estimation**: It employs a nearest neighbor density estimation technique to incorporate density information. 3. **Archive Truncation**: A new archive truncation method is introduced to preserve boundary solutions. The paper also outlines the main loop of the SPEA2 algorithm, detailing the fitness assignment, environmental selection, and other key steps. Experimental results comparing SPEA2 with SPEA, PESA, and NSGA-II on various test problems show that SPEA2 consistently outperforms its predecessor and other modern elitist methods, particularly in terms of solution diversity and performance over time. The study concludes that SPEA2 is a significant improvement over SPEA and other algorithms, especially in multiobjective optimization problems with more objectives.