Adaptive hybrid response mechanism for dynamic multi-objective optimization and its application in multi-robot task allocation

The dynamic changes in task requirements and real-time fluctuations in robot states in multi-robot task allocation (MRTA) increase the complexity of algorithm design. This paper presents an Adaptive Multi-Objective Evolutionary Algorithm with Hybrid Response Mechanism (AMOEAD-HRM) for dynamic multi-objective MRTA, addressing environmental uncertainty through innovative mechanisms. AMOEAD-HRM proposes a GNG-based prediction response mechanism, leveraging Growing Neural Gas (GNG) networks to model the time-varying nature of tasks and robot states. Unlike fixed-architecture predictors, GNG captures data topological structures to construct adaptive predictive models, dynamically adjusting to fluctuations and uncertainties by iteratively optimizing network topology. This enables effective characterization of complex temporal patterns without prior distribution assumptions, providing a robust foundation for predicting dynamic changes. To enhance responsiveness, the algorithm integrates a memory-based response mechanism and a Gaussian polynomial mixture mutation strategy. A dynamic adaptive weight adjustment strategy selects optimal response mechanisms according to environmental variation degrees, balancing prediction accuracy and real-time adaptability to improve system robustness and flexibility. Experimental validation on 19 benchmark problems shows AMOEAD-HRM’s superiority. In dynamic scenarios, it responds 46.1% faster than DNSGA-II. Under high dynamics, its solution sets have 3.4% higher MHV than DNSGA-II. With moderate changes, MHV is 0.34% higher than SGEA.