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On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment
Abstract
Multiple-objective metaheuristics, e.g., multiple-objective evolutionary algorithms, constitute one of the most active fields of multiple-objective optimization. Since 1985, a significant number of different methods have been proposed. However, only few comparative studies of the methods were performed on large-scale problems. We continue two comparative experiments on the multiple-objective 0/1 knapsack problem reported in the literature. We compare the performance of two multiple-objective genetic local search (MOGLS) algorithms to the best performers in the previous experiments using the same test instances. The results of our experiment indicate that our MOGLS algorithm generates better approximations to the nondominated set in the same number of functions evaluations than the other algorithms.
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Cites
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Zitzler, E., & Thiele, L. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797969 · Cited by 8,798
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
Srinivas, N., & Deb, K. (1994) · Evolutionary Computation · doi:10.1162/evco.1994.2.3.221 · Cited by 6,935
Glover, F. (1990) · ORSA Journal on Computing · doi:10.1287/ijoc.2.1.4 · Cited by 5,647
Glover, F. (1989) · ORSA Journal on Computing · doi:10.1287/ijoc.1.3.190 · Cited by 5,106
Multiple Criteria Optimization: Theory, Computation, and Application
Laundy, R. S. (1989) · Cited by 3,420
Simulated annealing: theory and applications.
(1988) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(88)90023-7 · Cited by 2,966
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Schaffer, J. D. (1985) · international conference on Genetic algorithms · Cited by 2,654
A niched Pareto genetic algorithm for multiobjective optimization
Horn, J., Nafpliotis, N., & Goldberg, D. E. (2002) · Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence · doi:10.1109/icec.1994.350037 · Cited by 2,433
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Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
Evolutionary many-objective optimization: A short review
Ishibuchi, H., Tsukamoto, N., & Nojima, Y. (2008) · 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) · doi:10.1109/cec.2008.4631121 · Cited by 914
Expensive Multiobjective Optimization by MOEA/D With Gaussian Process Model
Zhang, Q., Liu, W., Tsang, E., & Virginas, B. (2010) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2009.2033671 · Cited by 746
The performance of a new version of MOEA/D on CEC09 unconstrained MOP test instances
Zhang, Q., Liu, W., & Li, H. (2009) · 2009 IEEE Congress on Evolutionary Computation · doi:10.1109/cec.2009.4982949 · Cited by 624
Deep reinforcement learning based computation offloading and resource allocation for MEC
Li, J., Gao, H., Lv, T., & Lu, Y. (2018) · 2018 IEEE Wireless Communications and Networking Conference (WCNC) · doi:10.1109/wcnc.2018.8377343 · Cited by 548
Metadata
- Authors
- Jaszkiewicz, A.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2002-08
- Volume
- 6
- Issue
- 4
- Pages
- 402-412
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2155345556
- Cited by
- 474
- References
- 25
- FWCI
- 15.94
- Type
- journal article
- Language
- en
- Open access
- closed
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