The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling
Abstract
This paper shows how the performance of evolutionary multiobjective optimization (EMO) algorithms can be improved by hybridization with local search. The main positive effect of the hybridization is the improvement in the convergence speed to the Pareto front. On the other hand, the main negative effect is the increase in the computation time per generation. Thus, the number of generations is decreased when the available computation time is limited. As a result, the global search ability of EMO algorithms is not fully utilized. These positive and negative effects are examined by computational experiments on multiobjective permutation flowshop scheduling problems. Results of our computational experiments clearly show the importance of striking a balance between genetic search and local search. In this paper, we first modify our former multiobjective genetic local search (MOGLS) algorithm by choosing only good individuals as initial solutions for local search and assigning an appropriate local search direction to each initial solution. Next, we demonstrate the importance of striking a balance between genetic search and local search through computational experiments. Then we compare the modified MOGLS with recently developed EMO algorithms: the strength Pareto evolutionary algorithm and revised nondominated sorting genetic algorithm. Finally, we demonstrate that a local search can be easily combined with those EMO algorithms for designing multiobjective memetic algorithms.
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Cites
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A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design
Juang, C.-F. (2004) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2003.818557 · Cited by 964
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
Self-citationExpensive 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
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Metadata
- Authors
- Ishibuchi, H., Yoshida, T., & Murata, T.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2003-04
- Volume
- 7
- Issue
- 2
- Pages
- 204-223
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2122510010
- Cited by
- 773
- References
- 34
- FWCI
- 85.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ishibuchi, H., Yoshida, T., & Murata, T. (2003). Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2003.810752
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