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Feature information prediction algorithm for dynamic multi-objective optimization problems
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DOI：10.1016/j.ejor.2021.01.028.png)
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Abstract
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Dynamic multi-objective optimization problems (DMOPs) contain multiple conflicting goals while track -ing the changing Pareto-optimal front (PF) or Pareto-optimal set (PS). Most algorithms treat the solutions of DMOPs as if they were dealing with static multi-objective optimization problems. However, solutions under different environments may obey different distributions. To solve some of the existing limitations of currently available methods, a dynamic multi-objective optimization algorithm based on feature in-formation prediction (FIP) is proposed. To identify the distribution of solutions after an environmental change, joint distribution adaptation (JDA) is used to construct a mapping function. The feature informa-tion, which is extracted from the objective space at the current time step, is mapped to a higher dimen-sional space. Then the feature information of decision space at the next time step is obtained using the interior point method. Based on this information, the initial population at the next time step is gener-ated when a change is detected. The performance of FIP is validated by comparing it with respect to four state-of-the-art evolutionary algorithms on eight benchmark functions. Experimental results demonstrate that FIP can quickly cover the front with rapidly changing environments. (c) 2021 Published by Elsevier B.V.
Keywords:
Evolutionary computations
Dynamic multi-objective optimization
Different distributions
Feature information prediction
Joint distribution adaptation
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