Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a bal...Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a balance between objectives and constraints,existing constrained multi-objective evolutionary algorithms(CMOEAs)predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints,and the designed strategies usually are effective for the problems with simple constraints.However,these methods most ignore the relationship between decision variables and constraints.In fact,the essence of optimization is to find appropriate decision variables to meet various complex constraints.Therefore,it is hoped that the problem can be analyzed from the perspective of decision variables,so as to obtain more excellent results.Based on the above motivation,this paper proposes a decision variables classification approach,according to the relationship between decision variables and constraints,variables are divided into constraint-related(CR)variables and constraintindependent(CI)variables.Consequently,by optimizing these two types of variables independently,the population can sustain a favorable balance between feasibility and diversity.Furthermore,specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity.Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.展开更多
In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refe...In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.展开更多
Solving constrained multiobjective optimization problems(CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricat...Solving constrained multiobjective optimization problems(CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricate feasible region.Makes it difficult for the algorithm to search for the complete constraint PF. In addition, under the influence of multiple complex nonlinear constraints, the conventional calculation method of overall constraint violation is inefficient for assessing the quality of infeasible solutions, potentially misguiding the evolutionary direction of the population. In response to these challenges, this paper proposes the fuzzy constraint dominance strategy(FCDS).This novel approach facilitates nuanced comparisons of solutions to strike a better balance between objectives and constraints. The fuzzy constraint violation introduced in FCDS mitigates the misleading impact of complex nonlinear constraints. Moreover,FCDS divides the solution process of complex CMOP into multiple stages from easy to difficult, and uses adaptive methods to increase the difficulty level of the problem. Systematic experiments on four test suites and three real-world applications have conclusively demonstrated the superior competitiveness of FCDS against leading algorithms.展开更多
基金supported in part by the National Natural Science Foundation of China(U23A20340,62176238,62476254,62106230)the Key Research and Development Projects of the Ministry of Science and Technology of China(2022YFD2001200)+3 种基金the Natural Science Foundation Project of Henan Province(242300420277)the Key Research and Development Program of Henan(251111113900)the Frontier Exploration Projects of Longmen Laboratory(LMQYTSKT031)Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Open Fund Project(BDIC-2023-B-005).
文摘Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a balance between objectives and constraints,existing constrained multi-objective evolutionary algorithms(CMOEAs)predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints,and the designed strategies usually are effective for the problems with simple constraints.However,these methods most ignore the relationship between decision variables and constraints.In fact,the essence of optimization is to find appropriate decision variables to meet various complex constraints.Therefore,it is hoped that the problem can be analyzed from the perspective of decision variables,so as to obtain more excellent results.Based on the above motivation,this paper proposes a decision variables classification approach,according to the relationship between decision variables and constraints,variables are divided into constraint-related(CR)variables and constraintindependent(CI)variables.Consequently,by optimizing these two types of variables independently,the population can sustain a favorable balance between feasibility and diversity.Furthermore,specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity.Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.
基金supported in part by the National Natural Science Foundation of China (Grant No. 61901052)in part by the 111 project (Grant No. B17007)in part by the Director Funds of Beijing Key Laboratory of Network System Architecture and Convergence (Grant No. 2017BKL-NSACZJ-02)。
文摘In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.
基金supported by the Scientific Research Project of Xiang Jiang Lab(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28)+3 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72421002,62403477)the Science and Technology Innovation Program of Hunan Province(ZC23112101-10)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)
文摘Solving constrained multiobjective optimization problems(CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricate feasible region.Makes it difficult for the algorithm to search for the complete constraint PF. In addition, under the influence of multiple complex nonlinear constraints, the conventional calculation method of overall constraint violation is inefficient for assessing the quality of infeasible solutions, potentially misguiding the evolutionary direction of the population. In response to these challenges, this paper proposes the fuzzy constraint dominance strategy(FCDS).This novel approach facilitates nuanced comparisons of solutions to strike a better balance between objectives and constraints. The fuzzy constraint violation introduced in FCDS mitigates the misleading impact of complex nonlinear constraints. Moreover,FCDS divides the solution process of complex CMOP into multiple stages from easy to difficult, and uses adaptive methods to increase the difficulty level of the problem. Systematic experiments on four test suites and three real-world applications have conclusively demonstrated the superior competitiveness of FCDS against leading algorithms.
