Multi-objective Optimization Algorithms

  • Qing-Hua WuEmail author
  • Jiehui Zheng
  • Zhaoxia Jing
  • Xiaoxin Zhou
Part of the Energy Systems in Electrical Engineering book series (ESIEE)


In the LSIES, multiple benefits of different operating interests are taken into consideration. Hence, the planning and operation of LSIES are formulated as multi-objective optimization problems, which should be tackled using the multi-objective optimization algorithms. This chapter presents three multi-objective optimization algorithms, i.e., the multi-objective group search optimizer with adaptive covariance and Lévy flights (MGSO-ACL), multi-objective group search optimizer with adaptive covariance and chaotic search (MGSOACC), and multi-objective evolutionary predator and prey strategy (EPPS). Simulation studies conducted on benchmark functions are also carried out to investigate the performance of these algorithms. In later chapters, these algorithms are employed to deal with the planning and operating problems of LSIES.


Multi-objective optimization algorithms Non-dominated sorted genetic algorithm Multi-objective group search optimizer Multi-objective evolutionary predator and prey strategy 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Qing-Hua Wu
    • 1
    Email author
  • Jiehui Zheng
    • 1
  • Zhaoxia Jing
    • 2
  • Xiaoxin Zhou
    • 3
  1. 1.School of Electric Power EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.School of Electric Power EngineeringSouth China University of TechnologyGuangzhouChina
  3. 3.China Electric Power Research InstituteBeijingChina

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