方法对比

	NSGA-III	多目标优化	粒子群优化 (PSO)
领域	运筹学	仿真	优化
方法族	Machine learning	Process / pipeline	Process / pipeline
起源年份	2014	1896 (concept); 1989–2002 (evolutionary algorithms era)	1995
提出者	Kalyanmoy Deb and Himanshu Jain	Vilfredo Pareto (concept); modern computational formulation by Goldberg and Deb et al.	—
类型	algorithm	Optimization framework	Population-based metaheuristic / swarm intelligence
开创性文献	Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577-601.	Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Chichester. ISBN: 9780471873396	Kennedy, J. & Eberhart, R. (1995). Particle Swarm Optimization. IEEE International Conference on Neural Networks (ICNN), 1942-1948.
别名	NSGA-III algorithm, NSGA-III evolutionary, many-objective optimization	MOO, Multi-Criteria Optimization, Vector Optimization, Pareto Optimization	PSO, swarm intelligence optimization, Parçacık Sürü Optimizasyonu (PSO)
相关	2	3	6
摘要	NSGA-III (Non-dominated Sorting Genetic Algorithm III), developed by Kalyanmoy Deb and Himanshu Jain in 2014, is a state-of-the-art evolutionary algorithm for many-objective optimization problems. It extends the popular NSGA-II algorithm with reference-point-based selection, enabling effective handling of problems with three or more conflicting objectives.	Multi-Objective Optimization (MOO) is a mathematical and computational framework for finding solutions that simultaneously optimize two or more conflicting objective functions. Rather than collapsing all goals into a single scalar, MOO produces a set of trade-off solutions — the Pareto front — from which a decision-maker selects according to preference. It is widely used in engineering design, operations research, logistics, economics, and policy analysis.	Particle Swarm Optimization (PSO) is a population-based metaheuristic algorithm introduced by Kennedy and Eberhart in 1995, inspired by the collective movement of bird flocks and fish schools. Each candidate solution — called a particle — moves through the search space by updating its velocity and position based on its own best experience and the best experience of the entire swarm, enabling fast convergence across continuous optimization problems.