A Collection of Multi-Objective Optimization Algorithms

This article presents a collection of multi-objective optimization algorithms, which includes but is not limited to: MOEA/D, MOPSO, NNIA, and NSGA-II. These practical algorithms can be widely applied across various fields such as machine learning, artificial intelligence, and automation systems. MOEA/D (Multi-objective Evolutionary Algorithm based on Decomposition) is a popular algorithm that optimizes multiple objectives simultaneously by decomposing the multi-objective problem into several single-objective subproblems. Its implementation typically involves weight vector generation and neighborhood maintenance mechanisms. MOPSO (Multi-objective Particle Swarm Optimization) is a particle swarm-based approach that demonstrates excellent performance in solving multi-objective optimization problems. The algorithm maintains an external archive to store non-dominated solutions and uses techniques like crowding distance for diversity preservation. NNIA (Non-dominated Neighbor Immune Algorithm) is an emerging algorithm that effectively handles multi-objective optimization problems by simulating immune system mechanisms. It employs selection techniques based on non-dominated sorting and neighbor-based density estimation. NSGA-II (Non-dominated Sorting Genetic Algorithm II) remains a classical algorithm with high accuracy in multi-objective problem solving. It features fast non-dominated sorting, crowding distance computation, and elitism preservation in its genetic operations. In summary, this collection of multi-objective optimization algorithms provides excellent methods and approaches for solving complex multi-objective optimization problems, offering diverse implementation strategies and algorithmic frameworks for different application scenarios.