A special point-based transfer component analysis for dynamic multi-objective optimization

Abstract

To solve dynamic multi-objective optimization problems better, the key is to adapt quickly to environmental changes and track the possible changing optimal solutions in time. In this paper, we propose a special point-based transfer component analysis for dynamic multi-objective optimization algorithm (SPTr-RM-MEDA). To be specific, when a change occurs, the neighbors of some special points are selected from the optimal set at previous time, and the transfer component analysis makes the use of minimizing the distance between the mapped previous optima and the mapped current optima. Accordingly, the purpose is to predict a part of next initial population from the neighborhoods of special points by transfer component analysis. To adapt to the change well, SPTr-RM-MEDA also reevaluates the previous optimal set. In addition, an adaptive diversity introduction strategy is adopted to maintain the population size. SPTr-RM-MEDA is performed on 12 test problems under 8 kinds of environmental changes, and experimental results show that it is superior to other five state-of-the-art algorithms on most of test problems.

An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management

“…The inherent nature of the prediction algorithm may not always result in the same prediction value based on the input data [36]. In traffic applications, the fundamental LSTM algorithm can be enhanced in conjunction with other methods, such as optimization-based decision-making, and may offer several advantages [37,38]. These combinations may improve efficiency by finding optimal solutions and optimizing decision-making processes.…”