Book Chapter10.1007/3-540-44719-9_7
Adapting Weighted Aggregation for Multiobjective Evolution Strategies
Yaochu Jin,Tatsuya Okabe,Bernhard Sendhoff +2 more
- 07 Mar 2001
- pp 96-110
161
TL;DR: The conventional weighted aggregation method is extended to realize multi-objective optimization and it is found that the population is able to approach the Pareto front, although it will not keep all the found Pare to solutions in the population.
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Abstract: The conventional weighted aggregation method is extended to realize multi-objective optimization. The basic idea is that systematically changing the weights during evolution will lead the population to the Pareto front. Two possible methods are investigated. One method is to assign a uniformly distributed random weight to each individual in the population in each generation. The other method is to change the weight periodically with the process of the evolution. We found in both cases that the population is able to approach the Pareto front, although it will not keep all the found Pareto solutions in the population. Therefore, an archive of non-dominated solutions is maintained. Case studies are carried out on some of the test functions used in [1] and [2]. Simulation results show that the proposed approaches are simple and effective.
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Citations
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- 08 Jul 2006
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References
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TL;DR: In this article, the authors provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions, each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front.
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