The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms(MOEAs)lies a certain distance away from the decision makers’preference information region.Therefore,we prop...The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms(MOEAs)lies a certain distance away from the decision makers’preference information region.Therefore,we propose a multi-ob jective optimization algorithm,referred to as the double-grid interactive preference based MOEA(DIPMOEA),which explicitly takes the preferences of decision makers(DMs)into account.First,according to the optimization ob jective of the practical multi-ob jective optimization problems and the preferences of DMs,the membership functions are mapped to generate a decision preference grid and a preference error grid.Then,we put forward two dominant modes of population,preference degree dominance and preference error dominance,and use this advantageous scheme to update the population in these two grids.Finally,the populations in these two grids are combined with the DMs’preference interaction information,and the preference multi-ob jective optimization interaction is performed.To verify the performance of DIP-MOEA,we test it on two kinds of problems,i.e.,the basic DTLZ series functions and the multi-ob jective knapsack problems,and compare it with several different popular preference-based MOEAs.Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs,quickly provides the test results,and has better performance in the distribution of the Pareto front solution set.展开更多
ACCORDING to the United Nations, half of the world’s population already lives in cities, with that proportion estimated to increase to 66 percent by 2050. If we are to progress, we must manage our cities both efficie...ACCORDING to the United Nations, half of the world’s population already lives in cities, with that proportion estimated to increase to 66 percent by 2050. If we are to progress, we must manage our cities both efficiently and humanely. Smart cities are defined by the use of data captured by cameras, sensors, and other devices, and then analyzed by artificial intelligence to provide real time information to decision makers and citizens.展开更多
The purpose of this research is to reduce requirements for decision maker (DM)to supply preference information during the interactive multiojective decisionmaking process. By introducing a concept of displaced toleran...The purpose of this research is to reduce requirements for decision maker (DM)to supply preference information during the interactive multiojective decisionmaking process. By introducing a concept of displaced tolerant value, a newalsorithm (called IDTV) is presented. It allows the DM to choose convenientways and contents of supplying necessary information according to differentconditions. A detailed dexseription of the algorithm is provided and a numericalexample is also given to illustrate the procedure described.展开更多
基金supported by the National Natural Science Foundation of China(No.72101266)the Military Postgraduate Funding Project+2 种基金China(No.JY2021B042)the Hunan Provincial Postgraduate Scientific Research Innovation ProjectChina(No.CX20200029)。
文摘The final solution set given by almost all existing preference-based multi-objective evolutionary algorithms(MOEAs)lies a certain distance away from the decision makers’preference information region.Therefore,we propose a multi-ob jective optimization algorithm,referred to as the double-grid interactive preference based MOEA(DIPMOEA),which explicitly takes the preferences of decision makers(DMs)into account.First,according to the optimization ob jective of the practical multi-ob jective optimization problems and the preferences of DMs,the membership functions are mapped to generate a decision preference grid and a preference error grid.Then,we put forward two dominant modes of population,preference degree dominance and preference error dominance,and use this advantageous scheme to update the population in these two grids.Finally,the populations in these two grids are combined with the DMs’preference interaction information,and the preference multi-ob jective optimization interaction is performed.To verify the performance of DIP-MOEA,we test it on two kinds of problems,i.e.,the basic DTLZ series functions and the multi-ob jective knapsack problems,and compare it with several different popular preference-based MOEAs.Experimental results show that DIP-MOEA expresses the preference information of DMs well and provides a solution set that meets the preferences of DMs,quickly provides the test results,and has better performance in the distribution of the Pareto front solution set.
文摘ACCORDING to the United Nations, half of the world’s population already lives in cities, with that proportion estimated to increase to 66 percent by 2050. If we are to progress, we must manage our cities both efficiently and humanely. Smart cities are defined by the use of data captured by cameras, sensors, and other devices, and then analyzed by artificial intelligence to provide real time information to decision makers and citizens.
文摘The purpose of this research is to reduce requirements for decision maker (DM)to supply preference information during the interactive multiojective decisionmaking process. By introducing a concept of displaced tolerant value, a newalsorithm (called IDTV) is presented. It allows the DM to choose convenientways and contents of supplying necessary information according to differentconditions. A detailed dexseription of the algorithm is provided and a numericalexample is also given to illustrate the procedure described.
