Dynamic multi-objective optimization with evolutionary algorithms

A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

“…In this paper, we chose four commonly used strategies for comparison: (1) random initialization strategy (RIS), which will randomly initialize the population when the environment changes, (2) feed-forward prediction strategy (FPS) [36], (3) predictive multiobjective genetic algorithm (PMGA) [39], and (4) population prediction strategy (PPS) [37]. RM-MEDA [51] was used as the optimization algorithm.…”

A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

“…In this paper, we chose four commonly used strategies for comparison: (1) random initialization strategy (RIS), which will randomly initialize the population when the environment changes, (2) feed-forward prediction strategy (FPS) [36], (3) predictive multiobjective genetic algorithm (PMGA) [39], and (4) population prediction strategy (PPS) [37]. RM-MEDA [51] was used as the optimization algorithm.…”

Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

“…The literature cited present different methodologies for forecasting compared with our methods. Vantuch & Zelinka [11], Ervural et al [13] and Hatzakis & Wallace [6] used GAs with the ARIMA model to improve results compared with use of the ARIMA model alone. These authors used GAs to obtain a randomization of the ARIMA parameters, which is not always sufficient because ARIMA parameter estimation should be based on the data and research problem to achieve better forecasting.…”

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

“…The question that arises in dynamic cases is: how to track and predict the changes of POS [26]? This is especially important when the changing frequency is too high for MOEAs to search from scratch.…”

A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

“…In this paper, we chose four commonly used strategies for comparison: (1) random initialization strategy (RIS), which will randomly initialize the population when the environment changes, (2) feed-forward prediction strategy (FPS) [36], (3) predictive multiobjective genetic algorithm (PMGA) [39], and (4) population prediction strategy (PPS) [37]. RM-MEDA [51] was used as the optimization algorithm.…”

Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

“…The literature cited present different methodologies for forecasting compared with our methods. Vantuch & Zelinka [11], Ervural et al [13] and Hatzakis & Wallace [6] used GAs with the ARIMA model to improve results compared with use of the ARIMA model alone. These authors used GAs to obtain a randomization of the ARIMA parameters, which is not always sufficient because ARIMA parameter estimation should be based on the data and research problem to achieve better forecasting.…”

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

“…The question that arises in dynamic cases is: how to track and predict the changes of POS [26]? This is especially important when the changing frequency is too high for MOEAs to search from scratch.…”

A Hybrid Predictive Strategy Carried through Simultaneously from Decision Space and Objective Space for Evolutionary Dynamic Multiobjective Optimization

“…In this paper, we chose four commonly used strategies for comparison: (1) random initialization strategy (RIS), which will randomly initialize the population when the environment changes, (2) feed-forward prediction strategy (FPS) [36], (3) predictive multiobjective genetic algorithm (PMGA) [39], and (4) population prediction strategy (PPS) [37]. RM-MEDA [51] was used as the optimization algorithm.…”

Time Series Forecasting Using a Two-Level Multi-Objective Genetic Algorithm: A Case Study of Maintenance Cost Data for Tunnel Fans

“…The literature cited present different methodologies for forecasting compared with our methods. Vantuch & Zelinka [11], Ervural et al [13] and Hatzakis & Wallace [6] used GAs with the ARIMA model to improve results compared with use of the ARIMA model alone. These authors used GAs to obtain a randomization of the ARIMA parameters, which is not always sufficient because ARIMA parameter estimation should be based on the data and research problem to achieve better forecasting.…”

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization

“…The question that arises in dynamic cases is: how to track and predict the changes of POS [26]? This is especially important when the changing frequency is too high for MOEAs to search from scratch.…”