Hsu Chen Liao
ABSTRACT
The multi-objective gene-pool optimal mixing evolutionary algorithm with interleaved multi-start scheme (MO-GOMEA) is a powerful, parameterless model-based genetic algorithm that excels at solving multi-objective combinatorial optimization problems. In this paper, we propose a new mixing mechanism, adaptive donor selection mixing (ADSM) and further integrate it into MO-GOMEA to form a new variant, ADSM-MO-GOMEA. The proposed ADSM mechanism adaptively switches between cluster-guided and elitist-guided mixing, with the latter having a customized donor selection for the receiver based on empirical observations and mathematical derivation. The empirical results on multiple benchmark problems indicate that ADSM-MO-GOMEA improves the effectiveness over the original MO-GOMEA and achieves a lower inverted generational diversity and higher front occupation within the given limited number of evaluations.
- Fulya Altiparmak, Mitsuo Gen, Lin Lin, and Turan Paksoy. 2006. A genetic algorithm approach for multi-objective optimization of supply chain networks. Computers & industrial engineering 51, 1 (2006), 196--215.Google Scholar
- Peter AN Bosman. 2010. The anticipated mean shift and cluster registration in mixture-based EDAs for multi-objective optimization. In Proceedings of the 12th annual conference on Genetic and evolutionary computation. 351--358.Google ScholarDigital Library
- Peter AN Bosman and Dirk Thierens. 2003. The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE transactions on evolutionary computation 7, 2 (2003), 174--188.Google ScholarDigital Library
- Peter AN Bosman and Dirk Thierens. 2012. Linkage neighbors, optimal mixing and forced improvements in genetic algorithms. In Proceedings of the 14th annual conference on Genetic and evolutionary computation. 585--592.Google ScholarDigital Library
- Peter AN Bosman and Dirk Thierens. 2013. More concise and robust linkage learning by filtering and combining linkage hierarchies. In Proceedings of the 15th annual conference on Genetic and evolutionary computation. 359--366.Google ScholarDigital Library
- Ping-Lin Chen, Chun-Jen Peng, Chang-Yi Lu, and Tian-Li Yu. 2017. Two-edge graphical linkage model for DSMGA-II. In Proceedings of the Genetic and Evolutionary Computation Conference. 745--752.Google ScholarDigital Library
- Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation 6, 2 (2002), 182--197.Google Scholar
- Larry Jenkins. 2002. A bicriteria knapsack program for planning remediation of contaminated lightstation sites. European Journal of Operational Research 140, 2 (2002), 427--433.Google ScholarCross Ref
- Nazan Khan, David E Goldberg, and Martin Pelikan. 2002. Multi-objective Bayesian optimization algorithm. In Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation. 684--684.Google ScholarDigital Library
- Michael M Kostreva, Wlodzimierz Ogryczak, and David W Tonkyn. 1999. Relocation problems arising in conservation biology. Computers & Mathematics with Applications 37, 4--5 (1999), 135--150.Google ScholarCross Ref
- Ngoc Hoang Luong and Peter AN Bosman. 2012. Elitist archiving for multi-objective evolutionary algorithms: To adapt or not to adapt. In International Conference on Parallel Problem Solving from Nature. Springer, 72--81.Google ScholarDigital Library
- Ngoc Hoang Luong, Han La Poutré, and Peter AN Bosman. 2014. Multi-objective gene-pool optimal mixing evolutionary algorithms. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation. 357--364.Google ScholarDigital Library
- Ngoc Hoang Luong, Han La Poutré, and Peter AN Bosman. 2018. Multi-objective gene-pool optimal mixing evolutionary algorithm with the interleaved multi-start scheme. Swarm and Evolutionary Computation 40 (2018), 238--254.Google ScholarCross Ref
- R Timothy Marler and Jasbir S Arora. 2004. Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimization 26 (2004), 369--395.Google Scholar
- Mitchell Olsthoorn and Annibale Panichella. 2021. Multi-objective test case selection through linkage learning-based crossover. In International Symposium on Search Based Software Engineering. Springer, 87--102.Google ScholarDigital Library
- Martin Pelikan, David E Goldberg, and Kumara Sastry. 2001. Bayesian optimization algorithm, decision graphs, and Occam's razor. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), Vol. 519526. Citeseer.Google Scholar
- Martin Pelikan, Alexander Hartmann, and Tz-Kai Lin. 2007. Parameter-less hierarchical bayesian optimization algorithm. In Parameter setting in evolutionary algorithms. Springer, 225--239.Google Scholar
- Martin Pelikan, Kumara Sastry, and David E Goldberg. 2005. Multiobjective hBOA, clustering, and scalability. In Proceedings of the 7th annual conference on Genetic and evolutionary computation. 663--670.Google ScholarDigital Library
- Meir J Rosenblatt and Zilla Sinuany-Stern. 1989. Generating the discrete efficient frontier to the capital budgeting problem. Operations Research 37, 3 (1989), 384--394.Google ScholarDigital Library
- Günter Rudolph. 1997. Convergence properties of evolutionary algorithms.Google Scholar
- Ma Guadalupe Castillo Tapia and Carlos A Coello. 2007. Applications of multi-objective evolutionary algorithms in economics and finance: A survey. In 2007 IEEE congress on evolutionary computation. IEEE, 532--539.Google Scholar
- Dirk Thierens. 2010. The linkage tree genetic algorithm. In International Conference on Parallel Problem Solving from Nature. Springer, 264--273.Google ScholarDigital Library
- Dirk Thierens and Peter AN Bosman. 2011. Optimal mixing evolutionary algorithms. In Proceedings of the 13th annual conference on Genetic and evolutionary computation. 617--624.Google ScholarDigital Library
- David A Van Veldhuizen and Gary B Lamont. 2000. Multiobjective optimization with messy genetic algorithms. In Proceedings of the 2000 ACM symposium on Applied computing-Volume 1. 470--476.Google ScholarDigital Library
- Richard A Watson, Gregory S Hornby, and Jordan B Pollack. 1998. Modeling building-block interdependency. In International Conference on Parallel Problem Solving from Nature. Springer, 97--106.Google ScholarCross Ref
- Qingfu Zhang and Hui Li. 2007. MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on evolutionary computation 11, 6 (2007), 712--731.Google ScholarDigital Library
- Aimin Zhou, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, and Qingfu Zhang. 2011. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and evolutionary computation 1, 1 (2011), 32--49.Google Scholar
- Eckart Zitzler and Lothar Thiele. 1999. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE transactions on Evolutionary Computation 3, 4 (1999), 257--271.Google Scholar
- Jesse B Zydallis and Gary B Lamont. 2003. Explicit building-block multiobjective evolutionary algorithms for NPC problems. In The 2003 Congress on Evolutionary Computation, 2003. CEC'03., Vol. 4. IEEE, 2685--2695.Google ScholarCross Ref
Index Terms
- Adaptive Donor Selection Mixing for Multi-objective Optimization: an Enhanced Variant of MO-GOMEA
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