Borg: An Auto-Adaptive Many-Objective Evolutionary. This study introduces the Borg multi-objective evolutionary algorithm (MOEA) for many-objective, multimodal optimization. The Borg MOEA combines -dominance, a measure of convergence speed named -progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework. A comparative study on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites demonstrates Borg meets or exceeds six state of the art MOEAs on the majority of the tested problems. The performance for each test problem is evaluated using a 1,000 point Latin hypercube sampling of each algorithm’s feasible parameteri- zation space. The statistical performance of every sampled MOEA parameterization is evaluated using 50 replicate random seed trials. The Borg MOEA is not a single algorithm; instead it represents a class of algorithms whose operators are adaptively selected based on the problem. The adaptive discovery of key operators is of particular importance for benchmarking how variation operators enhance search for complex many-objective problems.

References in zbMATH (referenced in 11 articles )

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  1. Wang, Wenyu; Akhtar, Taimoor; Shoemaker, Christine A.: Integrating (\varepsilon)-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems (2022)
  2. Li, Wenhua; Wang, Rui; Zhang, Tao; Ming, Mengjun; Li, Kaiwen: Reinvestigation of evolutionary many-objective optimization: focus on the Pareto knee front (2020)
  3. Santiago, Alejandro; Dorronsoro, Bernabé; Nebro, Antonio J.; Durillo, Juan J.; Castillo, Oscar; Fraire, Héctor J.: A novel multi-objective evolutionary algorithm with fuzzy logic based adaptive selection of operators: FAME (2019)
  4. Seada, Haitham; Deb, Kalyanmoy: Non-dominated sorting based multi/many-objective optimization: two decades of research and application (2018)
  5. Jia, Chunhua; Zhu, Hong: An improved multiobjective particle swarm optimization based on culture algorithms (2017)
  6. Izquierdo, Joaquín; Campbell, Enrique; Montalvo, Idel; Pérez-García, Rafael: Injecting problem-dependent knowledge to improve evolutionary optimization search ability (2016)
  7. Li, Miqing; Yang, Shengxiang; Liu, Xiaohui: Bi-goal evolution for many-objective optimization problems (2015)
  8. Luo, Chang; Shimoyama, Koji; Obayashi, Shigeru: A study on many-objective optimization using the Kriging-surrogate-based evolutionary algorithm maximizing expected hypervolume improvement (2015)
  9. von Lücken, Christian; Barán, Benjamín; Brizuela, Carlos: A survey on multi-objective evolutionary algorithms for many-objective problems (2014)
  10. Woodruff, Matthew J.; Reed, Patrick M.; Simpson, Timothy W.: Many objective visual analytics: rethinking the design of complex engineered systems (2013) ioport
  11. Li, Yaohang: MOMCMC: an efficient Monte Carlo method for multi-objective sampling over real parameter space (2012)