IEICE TRANSACTIONS on Fundamentals
A Hybrid Fine-Tuned Multi-Objective Memetic Algorithm
Summary :
In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP).
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.3 pp.790-797
- Publication Date
- 2006/03/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1093/ietfec/e89-a.3.790
- Type of Manuscript
- PAPER
- Category
- Numerical Analysis and Optimization
Authors
Keyword
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Xiuping GUO, Genke YANG, Zhiming WU, Zhonghua HUANG, "A Hybrid Fine-Tuned Multi-Objective Memetic Algorithm" in IEICE TRANSACTIONS on Fundamentals,
vol. E89-A, no. 3, pp. 790-797, March 2006, doi: 10.1093/ietfec/e89-a.3.790.
Abstract: In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP).
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.3.790/_p
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@ARTICLE{e89-a_3_790,
author={Xiuping GUO, Genke YANG, Zhiming WU, Zhonghua HUANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Hybrid Fine-Tuned Multi-Objective Memetic Algorithm},
year={2006},
volume={E89-A},
number={3},
pages={790-797},
abstract={In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP).},
keywords={},
doi={10.1093/ietfec/e89-a.3.790},
ISSN={1745-1337},
month={March},}
Copy
TY - JOUR
TI - A Hybrid Fine-Tuned Multi-Objective Memetic Algorithm
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 790
EP - 797
AU - Xiuping GUO
AU - Genke YANG
AU - Zhiming WU
AU - Zhonghua HUANG
PY - 2006
DO - 10.1093/ietfec/e89-a.3.790
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2006
AB - In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP).
ER -
English
