Multi-Objective Optimization Algorithms Collection

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This collection features several multi-objective optimization algorithms that are highly effective for solving complex problems:

- MOEAD (Multi-objective Evolutionary Algorithm based on Decomposition): This algorithm decomposes multi-objective problems into single-objective subproblems using aggregation functions, and employs differential evolution operators with neighborhood-based mating selection for efficient optimization.

- MOPSO (Multi-Objective Particle Swarm Optimization): A population-based approach where particles explore the search space using velocity and position updates, featuring archive maintenance mechanisms and special crowding distance calculations for preserving Pareto front diversity.

- NNIA (Non-dominated Neighbor Immune Algorithm): Inspired by immune system principles, this algorithm uses non-dominated neighbor selection, antibody cloning, and hyper-mutation operations to maintain population diversity while converging to Pareto optimal solutions.

- NSGA2 (Non-dominated Sorting Genetic Algorithm II): An improved version featuring fast non-dominated sorting, crowding distance computation for diversity preservation, and elitist selection strategy for maintaining optimal solutions across generations.

These algorithms are specifically designed to handle multiple conflicting objectives, providing robust solutions for complex optimization problems. Through proper implementation of these algorithms, engineers and researchers can effectively address multi-criteria decision-making scenarios and discover superior Pareto-optimal solutions.