A non-dominated sorting particle swarm optimizer for multiobjective optimization. This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization. NSPSO extends the basic form of PSO by making a better use of particles’ personal bests and offspring for more effective non-domination comparisons. Instead of a single comparison between a particle’s personal best and its offspring, NSPSO compares all particles’ personal bests and their offspring in the entire population. This proves to be effective in providing an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. By using the non-dominated sorting concept and two parameter-free niching methods, NSPSO and its variants have shown remarkable performance against a set of well-known difficult test functions (ZDT series). Our results and comparison with NSGA II show that NSPSO is highly competitive with existing evolutionary and PSO multiobjective algorithms.

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  1. Halassi, Abdoul-hafar: An attractor-based multiobjective particle swarm optimization (2017)
  2. Yu, Shiwei; Zheng, Shuhong; Gao, Siwei; Yang, Juan: A multi-objective decision model for investment in energy savings and emission reductions in coal mining (2017)
  3. Thanos, Aristotelis E.; Celik, Nurcin; Sáenz, Juan P.: An evolutionary sequential sampling algorithm for multi-objective optimization (2016)
  4. Yun, Yeboon; Nakayama, Hirotaka; Yoon, Min: Generation of Pareto optimal solutions using generalized DEA and PSO (2016)
  5. Alves, Maria João; Costa, João Paulo: An algorithm based on particle swarm optimization for multiobjective bilevel linear problems (2014)
  6. Attea, Bara’a A.; Khalil, Enan A.; Özdemir, Suat: Biologically inspired probabilistic coverage for mobile sensor networks (2014) ioport
  7. Devika, K.; Jafarian, A.; Nourbakhsh, V.: Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques (2014)
  8. Idoumghar, Lhassane; Chérin, Nicolas; Siarry, Patrick; Roche, Robin; Miraoui, Abdellatif: Hybrid ICA-PSO algorithm for continuous optimization (2013)
  9. Subashini, G.; Bhuvaneswari, M. C.: Comparison of multi-objective evolutionary approaches for task scheduling in distributed computing systems (2012)
  10. Marinaki, Magdalene; Marinakis, Yannis; Stavroulakis, Georgios E.: Fuzzy control optimized by a multi-objective partial swarm optimization algorithm for vibration suppression of smart structures (2011)
  11. Feng, Yixiong; Zheng, Bing; Li, Zhongkai: Exploratory study of sorting particle swarm optimizer for multiobjective design optimization (2010)
  12. Liu, Junwan; Li, Zhoujun; Hu, Xiaohua; Chen, Yiming: Biclustering of microarray data with MOSPO based on crowding distance (2009) ioport
  13. Sağ, Tahir; çunkaş, Mehmet: A tool for multiobjective evolutionary algorithms (2009)
  14. Tripathi, Praveen Kumar; Bandyopadhyay, Sanghamitra; Pal, Sankar Kumar: Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients (2007)
  15. Huang, V. L.; Suganthan, P. N.; Liang, J. J.: Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems (2006)
  16. Köppen, Mario; Veenhuis, Christian: Multi-objective particle swarm optimization by fuzzy-Pareto-dominance meta-heuristic (2006)
  17. Li, Xiaodong: A non-dominated sorting particle swarm optimizer for multiobjective optimization (2003)