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Title: A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment
Authors: Koo, W.T.
Goh, C.K.
Tan, K.C. 
Keywords: Dynamic
Evolutionary algorithm
Issue Date: Jun-2010
Citation: Koo, W.T.,Goh, C.K.,Tan, K.C. (2010-06). A predictive gradient strategy for multiobjective evolutionary algorithms in a fast changing environment. Memetic Computing 2 (2) : 87-110. ScholarBank@NUS Repository.
Abstract: An essential feature of a dynamic multiobjective evolutionary algorithm (MOEA) is to converge quickly to the Pareto-optimal Set before it changes. In cases where the behavior of the dynamic problem follows a certain trend, convergence can be accelerated by anticipating the characteristics of future changes in the problem. A prediction model is usually used to exploit past information and estimate the location of the new Pareto-optimal Set. In this work, we propose the novel approach of tracking and predicting the changes in the location of the Pareto Set in order to minimize the effects of a landscape change. The predicted direction and magnitude of the next change, known as the predictive gradient, is estimated based on the history of previously discovered solutions using a weighted average approach. Solutions updated with the predictive gradient will remain in the vicinity of the new Pareto-optimal Set and help the rest of the population to converge. The prediction strategy is simple to implement, making it suitable for fast-changing problems. In addition, a new memory technique is introduced to exploit any periodicity in the dynamic problem. The memory technique selects only the more promising stored solutions for retrieval in order to reduce the number of evaluations used. Both techniques are incorporated into a variant of the multi-objective evolutionary gradient search (MO-EGS) and two other MOEAs for dynamic optimization and results indicate that they are effective at improving performance on several dynamic multiobjective test problems. © Springer-Verlag 2009.
Source Title: Memetic Computing
ISSN: 18659284
DOI: 10.1007/s12293-009-0026-7
Appears in Collections:Staff Publications

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