Awesome Multi-Objective Optimization

A curated list of awesome multi-objective optimization research resources. Inspired by awesome-360-vision, awesome-architecture-search, awesome-deep-vision, awesome-adversarial-machine-learning and awesome-deep-learning-papers. Still working on it, any suggestions of missing reference are welcome.

Table of Contents

• Papers
  • Reinforcement Learning
    • Single Policy
    • Multiple Policy
  • Evolutionary Algorithms
    • Dominance Based
    • Aggregation Based
    • Indicator Based

Papers

Reinforcement Learning

Survey

• A survey of multi-objective sequential decision-making. [pdf]
  • Roijers, Diederik M., et al. JAIR 2013
• Multiobjective reinforcement learning: A comprehensive overview. [pdf]
  • C. Liu, X. Xu, D. Hu IEEE SMC 2015

Single Policy

Weighted-Sum Approach

• Learning to solve multiple goals. [pdf]
  • J. Karlsson.
• Multiple-goal reinforcement learning with modular sarsa (0). [pdf]
  • N. Sprague, D. Ballard. IJCAI 2003
• A multiple goal reinforcement learn-ing method for complex vehicle overtaking maneuver. [pdf]
  • DCK Ngai, NHC Yung. WCICA 2010
• Self-adaptive multi-objective optimization method design based on agent reinforcement learning for elevator group control systems. [pdf]
  • F. Zeng, Q. Zong, Z. Sun, L. Dou. IEEE Trans. Intell. Trans. Syst 2011
• Managing power consumption and performance of computing systems using reinforcement learning. [pdf]
  • Tesauro, Gerald, et al. NIPS 2018

W-Learning Approach

• Action selection methods using reinforcement learning. [pdf]
  • M Humphrys.

Analytic Hierarchy Process Approach

• A multi-objective optimization genetic algorithm incorporating preference information. [pdf]
  • X Shen, Y Guo, Q Chen, W Hu.
• Multi-objective reinforcement learning algorithm for MOSDMP in unknown environment. [pdf]
  • Zhao, Yun, Qingwei Chen, and Weili Hu. WCICA 2010

Ranking Approach

• Multi-criteria reinforcement learning. [pdf]
  • Z Gábor, Z Kalmár, C Szepesvári. ICML 1998
• Reinforcement learning with bounded risk. [pdf]
  • P Geibel. ICML 2001
• Multi-objective reinforcement learning based routing in cognitive radio networks: Walking in a random maze. [pdf]
  • K. Zheng, H. Li, RC. Qiu, S. Gong. ICNC 2012

Geometric Approach

• A geometric approach to multi-criterion reinforcement learning. [pdf]
  • S. Mannor, N. Shimkin. JMLR 2014
• The steering approach for multi-criteria reinforcement learning. [pdf]
  • S. Mannor, N. Shimkin. NIPS 2002

Multiple Policy

Convex Hull Approach

• Learning all optimal policies with multiple criteria. [pdf]
  • Barrett, Leon, and Srini Narayanan. ICML 2008

Varying Parameter Approach

• Importance sampling for reinforcement learning with multiple objectives. [pdf]
  • CR Shelton.

Evolutionary Algorithms

Survey

• Multiobjective evolutionary algorithms: A survey of the state of the art [pdf]
  • Zhou, Aimin, et al.
• Many-Objective Evolutionary Algorithms: A Survey [pdf]
  • B Li, J Li, K Tang, X Yao. CSUR 2015

Dominance Based

• A fast and elitist multiobjective genetic algorithm: NSGA-II [pdf] [Official] [python] [C++] [Java]
  • K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. IEEE Transactions on Evolutionary Computation 2012
• SPEA2: Improving the Strength Pareto Evolutionary Algorithm [pdf] [official]
  • E. Zitzler, M. Laumanns, L. Thiele. TIK-report 2001
• ISPEA: improvement for the strength Pareto evolutionary algorithm for multiobjective optimization with immunity [pdf]
  • M. Hongyun, L. Sanyang. ICCIMA 2003
• Applications of Vector Evaluated Genetic Algorithms (VEGA) in Ultimate Limit State Based Ship Structural Design [pdf]
  • O. Hughes, M. Ma, JK. Paik. ASME 2014
• Shift-based density estimation for Pareto-based algorithms in many-objective optimization [pdf]
  • M Li, S Yang, X Liu. IEEE Transactions on Evolutionary Computation 2014
• The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation (PAES) [pdf]
  • Knowles, Joshua, and David Corne. CEC 1999

Aggregation Based

• MOEA/D: A multiobjective evolutionary algorithm based on decomposition [pdf] [R] [python] [java]
  • Q Zhang, H Li. IEEE Transactions on Evolutionary Computation 2007
• Normal-boundary intersection: A new method for generating the pareto surface in nonlinear multicriteria optimization problems [pdf]
  • I Das, JE Dennis. SIAM Journal on Optimization 1998
• Many-objective directed evolutionary line search [pdf]
  • Hughes, E. James. GECCO 2011
• Ranking methods for many-objective optimization [pdf]
  • M. Garza-Fabre, GT Pulido, CAC Coello. MICAI 2009
• A decomposition based evolutionary algorithm for many objective optimization with systematic sampling and adaptive epsilon control [pdf]
  • Asafuddoula, Md, Tapabrata Ray, and Ruhul Sarker. EMO 2013
• Ranking-dominance and many-objective optimization [pdf]
  • Kukkonen, Saku, and Jouni Lampinen. CEC 2017

Indicator Based

• HypE: An algorithm for fast hypervolume-based many-objective optimization [pdf]
  • J. Bader, E. Zitzler.
• An EMO algorithm using the hypervolume measure as selection criterion [pdf]
  • M. Emmerich, N. Beume, B. Naujoks. EMO 2015
• Approximation-guided evolutionary multi-objective optimization. [pdf]
  • Bringmann, Karl, et al. IJCAI 2011
• On the properties of the R2 indicator. [pdf]
  • Brockhoff, Dimo, T. Wagner, and H. Trautmann. GECCO 2012
• MOMBI: A new metaheuristic for many-objective optimization based on the R2 indicator. [pdf]
  • Gómez, Raquel Hernández, and Carlos A. Coello Coello. CEC 2013
• A ranking method based on the R2 indicator for many-objective optimization. [pdf]
  • Díaz-Manríquez, Alan, et al. CEC 2013

License

To the extent possible under law, Anjie Zheng has waived all copyright and related or neighboring rights to this work.