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An urban pluvial flood is a devastating, costly natural disaster requiring effective rescue path planning to mitigate the loss of lives and property. The inherent uncertainty and complexity of the risks associated with urban flooding limit the ability to plan optimal rescue paths that prioritize both timeliness and safety. This study addresses the challenge by proposing an innovative assessment methodology to output risk values and probability representing safety and timeliness in each passable area while simulating real-world flood scenarios. Furthermore, the paper develops a pioneering path-planning algorithm based on deep reinforcement learning, incorporating improved stochastic reward exploitation and heterogeneous reward exploration mechanisms to function in a simulation rescue path-planning scenario with uncertainty and complexity. According to the findings, the proposed algorithm outperforms current state-of-the-art algorithms in converging to the optimal path, fully sampling, and running efficiency. The study contributes to theoretical progress on urban pluvial flood rescue, deep reinforcement learning, risk assessment, and decision intelligence while offering practical implications for smart cities, emergency management, and optimizing real-world problems by employing artificial intelligence. • Urban pluvial flood rescue path planning using deep reinforcement learning. • Probability-based risk assessment methodology for urban pluvial flood rescue. • Environmental simulation based on real-world rescue scenarios. • Improving the exploitation and exploration to stochastic and heterogeneous reward. • Expanding deep reinforcement learning to solve real-world problems. [ABSTRACT FROM AUTHOR]

In addition to dominance-based and decomposition-based algorithms, performance indicator-based algorithms have been widely used and investigated in the field of evolutionary multi-objective optimisation. This study proposes a multi-objective artificial bee colony optimisation method called ε -MOABC based on performance indicators to solve multi-objective and many-objective problems. The proposed algorithm develops an external archive on the basis of both Pareto dominance and preference indicators to save the non-dominated solutions produced in each generation. The population of the presented algorithm includes employed bees, onlooker bees, and scout bees. Employed bees adjust their trajectories according to the information provided by other employed bees. Motivated by employed bees, onlooker bees select food sources to update their positions according to a power law probability, with which the food sources with high quality have a high probability to be selected for exploration. The quality of food sources is calculated on the basis of the quality indicator I ε + . Scout bees dispose of food sources with poor quality. The proposed algorithm proves to be competitive in dealing with multi-objective and many-objective optimisation problems in comparison with other state-of-the-art algorithms for CEC09, LZ09, and DTLZ test instances. [ABSTRACT FROM AUTHOR]

The design of integrated intelligent computing solver with Morlet wavelet neural networks (MW-NNs) is presented for solving the mathematical predator–prey model by exploiting the strength of MW-NNs modeling, optimization ability of global search with genetic algorithms (GAs) and rapid local search eminence of sequential quadratic programming (SQP), i.e., MW-NNs-GA-SQP. The proposed MW-NNs-GA-SQP scheme is used to analyze the predator–prey dynamics for six different variables coefficient values. The validation, correctness and reliability of the presented MW-NNs-GA-SQP technique is attained through the consistent matched outcomes with the reference Adams numerical results. Moreover, statistics investigations have been accomplished to verify the precision and accuracy of the outcomes with proposed MW-NNs-GA-SQP solver via the performances of Theil's inequality coefficient, Nash Sutcliffe efficiency and mean absolute error. • The design of integrated intelligent computing solver with Morlet wavelet neural network (MW-NN) is presented for solving the mathematical predator–prey model. • We use the strength of MN-NN modeling, optimization ability of global search with genetic algorithms (GAs) and rapid local search eminence of sequential quadratic programming (SQP), i.e., MW-NN-GA-SQP. • The proposed MW-NN-GA-SQP scheme is used to analyze the predator–prey dynamics for six different variables coefficient values. • The validation, correctness and reliability of the presented MWNN-GA-SQP technique is attained through the consistent matched outcomes with the reference Adams numerical results. • Statistics investigations have been accomplished to verify the precision and accuracy of the outcomes with proposed MW-NN-GA-SQP solver via the performances of Theil's inequality coefficient, Nash Sutcliffe efficiency and mean absolute error. [ABSTRACT FROM AUTHOR]