Multi-Objective Search Algorithm Based on Particle Swarm Optimization

This article introduces a multi-objective search algorithm based on Particle Swarm Optimization (PSO). This algorithm effectively solves optimization problems with multiple objectives by simulating the foraging behavior of bird flocks, where particles continuously update their positions to search for optimal solutions. The PSO algorithm features key components including velocity calculation using inertia weights, personal best (pbest) and global best (gbest) tracking, and Pareto dominance evaluation for multi-objective optimization. It has wide applications in engineering optimization, image processing, and data mining domains.

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