Dynamic constrained multi-objective optimization based on adaptive combinatorial response mechanism
In dynamic multi-objective optimization problems (DMOPs), objective functions, problem parameters, and constraints may change over time. Mainly, DMOPs use response mechanisms to generate the initial population after the environment changes. In this research, we develop an adaptive version of the com...
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| Published in: | Applied soft computing Vol. 155; p. 111398 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.04.2024
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| Subjects: | |
| ISSN: | 1568-4946, 1872-9681 |
| Online Access: | Get full text |
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| Summary: | In dynamic multi-objective optimization problems (DMOPs), objective functions, problem parameters, and constraints may change over time. Mainly, DMOPs use response mechanisms to generate the initial population after the environment changes. In this research, we develop an adaptive version of the combinational response mechanism (ACRM). ACRM uses three response mechanisms based on diversity, prediction, and memory to form the initial population. In ACRM, the number of solutions generated by a response mechanism is determined by reinforcement learning according to the severity of environmental changes. The background knowledge is transferred to reinforcement learning using the Q-value initialization method. Thus, in the early stages of optimization, when the experience gained from the environment is low, the proposed algorithm improves its performance using background knowledge. Also, we develop a new combinational constraint handling technique (CCHT). This method uses the dynamic information of the environment (i.e. the ratio of feasible solutions) to choose the appropriate constraint handling technique. The results of the tests on 23 dynamic test functions and seven dynamic constrained test functions indicate that the performance of the proposed algorithm can compete with advanced evolutionary algorithms in terms of the degree of convergence and variety of solutions.
Permanent link to reproducible Capsule: <https://doi.org/10.24433/CO.1949267.v1>.
•Developing an adaptive CRM that uses reinforcement learning.•Extracting valuable information from the environment to adapt to changes.•Using reinforcement learning to determine the number of solutions.•Proposing a new combinational constraint-handling technique (CCHT).•Generate six new DCMOPs by combining ACRM with six constraint-handling techniques. |
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| ISSN: | 1568-4946 1872-9681 |
| DOI: | 10.1016/j.asoc.2024.111398 |