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Dynamic Constrained Multi-Objective Optimization via Multi-Tribe Feasibility Prediction Based Knowledge Transfer
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DOI：10.1109/tevc.2026.3722852.png)
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Abstract
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In dynamic constrained multi-objective optimization problems (DCMOPs), both the constraints and objectives vary over time, potentially leading to changes in the constrained Pareto-optimal front. Feasibility prediction in the search region is crucial for responding to environmental changes, as it allows the algorithms to adopt the most appropriate response strategy based on shifts in feasibility. To this end, a dynamic constrained evolutionary algorithm with multi-tribe feasibility prediction based knowledge transfer (DCEA-MFPKT) is proposed to solve DCMOPs. In DCEA-MFPKT, a tribal region knee points-based classification strategy is first introduced. This strategy classifies the objective space into feasible and infeasible tribal regions based on the tribal region knee points. Then, a feasibility prediction strategy is proposed to capture the temporal behavior of the feasibility of tribal regions, where a long short term memory model is trained on sequences of historical data. Therefore, the feasibility of each tribal region can be predicted in the new environment. Based on the predicted feasibility status, a multi-tribe hybrid knowledge transfer strategy is proposed to select appropriate knowledge transfer operators adaptively for each tribal region to leverage historical solution archives. This helps construct a high-quality and environment-aware initial population in the new environment. Experimental results on a set of benchmarks and the operational optimization of the fluid catalytic cracking demonstrate the superiority of the proposed method over five state-of-the-art dynamic constrained multi-objective optimization methods.
Keywords:
Dynamic constrained multi-objective optimization
feasibility prediction
knowledge transfer
evolutionary algorithm
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