Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model base...Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.展开更多
Multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. Howev...Multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However, premature convergence is the major drawback of MODE, especially when there are numerous local Pareto optimal solutions. To overcome this problem, we propose a MODE with a diversity enhancement (MODE-DE) mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from http://www3.ntu.edu.sg/home/epnsugan/. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement.展开更多
This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation...This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.展开更多
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
文摘Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.
基金Project(No.0521010020)supported by the A*Star(Agency for Science,Technology and Research),Singapore
文摘Multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However, premature convergence is the major drawback of MODE, especially when there are numerous local Pareto optimal solutions. To overcome this problem, we propose a MODE with a diversity enhancement (MODE-DE) mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from http://www3.ntu.edu.sg/home/epnsugan/. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement.
文摘This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.
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.
