Journal Article10.1007/S00500-014-1480-9
A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment
Fangqing Gu,Hai-Lin Liu,Kay Chen Tan +2 more
- 01 Nov 2015
- Vol. 19, Iss: 11, pp 3249-3259
16
TL;DR: A hybrid EMOA is proposed, which divides the population into several smaller subpopulations according to their distribution in the objective space, and a hybrid performance measure estimates the performance of these EMOAs.
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Abstract: There are several studies on hybrid multi-operator recombination methods, while few works have been proposed in the area of combining different fitness assignment in a framework. On the other hand, it is known that fitness assignment has a marked impact on the performance of evolutionary multiobjective optimization algorithm (EMOA). In this paper, a hybrid EMOA is proposed, which divides the population into several smaller subpopulations according to their distribution in the objective space. Each subpopulation is evolved by an individual EMOA, and a hybrid performance measure estimates the performance of these EMOAs. We focus on the fitness assignment and assume that all EMOAs used in the subpopulations adopt the same recombination operator. To evaluate performance of the proposed algorithm, we compare it with MOEA/D-M2M, MOE-A/D, SMS-EMOA and NSGA-II on 16 test instances. Experimental results show that the proposed algorithm performs better than or similar to those compared EMOAs.
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Citations
Borg: An auto-adaptive many-objective evolutionary computing framework
David Hadka,Patrick M. Reed +1 more
TL;DR: The Borg MOEA combines -dominance, a measure of convergence speed named -progress, randomized restarts, and auto-adaptive multioperator recombination into a unified optimization framework for many-objective, multimodal optimization.
741
An Adaptive Resource Allocation Strategy for Objective Space Partition-Based Multiobjective Optimization
Huangke Chen,Guohua Wu,Witold Pedrycz,Ponnuthurai Nagaratnam Suganthan,Lining Xing,Xiaomin Zhu +5 more
TL;DR: This work designs an objective space partition-based adaptive MOEA, called OPE-MOEA, to improve population convergence, while maintaining population diversity and significantly outperforms the five algorithms on 28 MOP benchmarks in terms of the metric hypervolume.
100
Integrated rescheduling and preventive maintenance for arrival of new jobs through evolutionary multi-objective optimization
Dujuan Wang,Feng Liu,Jian-Jun Wang,Yanzhang Wang +3 more
- 01 Apr 2016
TL;DR: This paper hybridizes differential evolution mutation operation with NSGA-II to enhance diversity, constitute high-quality initial solution based on assignment model for exploitation, and incorporate analytic property of non-dominated solutions for exploration to address key problem of balancing between exploration and exploitation.
21
Relevant feature selection and ensemble classifier design using bi-objective genetic algorithm
TL;DR: A novel bi-objective genetic algorithm-based ensemble classification method (CCBOGA) is devised to ensemble the individual classifiers designed using obtained reduced datasets and it is observed that the constructed ensemble classifier performs better than the individualclassifiers.
16
Population Decomposition-Based Greedy Approach Algorithm for the Multi-Objective Knapsack Problems
TL;DR: Experimental studies on a set of test instances indicate that the MOEA/D-M2M with the improved greedy strategy is superior to MOGLS and MOEA /D in terms of performance.
15
References
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No free lunch theorems for optimization
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Qingfu Zhang,Hui Li +1 more
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Kaisa Miettinen
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TL;DR: This paper is concerned with the development of methods for dealing with the role of symbols in the interpretation of semantics.
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