A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes

Zhang, Peng; Li, Jinlong; Li, Tengfei; Chen, Huanhuan

doi:10.1109/tevc.2020.3035825

Cited by 37 publications

(14 citation statements)

References 41 publications

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“…Furthermore, the performance of GENERAL is more stable with larger instances. For example, in the cases of (40,5,150) and (40,5,200), the minimum values of GENERAL are much larger than the maximum values produced by ACO-SA and GA.…”

Section: A Experiments Onmentioning

confidence: 92%

“…In [16] and [39], the SA algorithm was introduced to optimally allocate tasks and solve the nonlinear problem, respectively. In [40], a kernel matrix and probability model was introduced to balance the relationship between diversity and convergence during the high-dimensional optimization process. In [17] and [41], the evolutionary optimization method was applied to solve resource allocation problem in the presence of various uncertainties.…”

Section: Literature Reviewmentioning

confidence: 99%

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Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Liu

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

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Unmanned Aerial Vehicles (UAVs) play an essential role in information collection where routing and time scheduling are two critical factors and are considered sequently. Given the paths of UAVs, metaheuristic algorithms are explored to allocate searching time of UAVs, which are hard to guarantee the optimal solution for time allocation. In this paper, a novel searchinG timE allocatioN undEr coopeRative pAth pLaning (GENERAL) is proposed to solve the optimal solution of the time allocation given paths of UAVs. GENERAL adopts a semi-greedy construction and a repair procedure to initialize and amend the routing solutions during iterations. Motivated by the Newton's method from convex optimization domains, we introduce a new Perturbed Parametric Nonlinear Complementarity Problem function (PPNCP-function), which reformulates the time allocation problem as a smoothing system of equations according to Karush-Kuhn-Tucker (KKT) theorem. Then a smoothing Newton method is introduced to obtain the optimal time allocation solution with superlinear convergence. Experimental results empirically indicate the GENERAL's effectiveness compared to Ant Colony Optimization with Simulated Annealing (ACO-SA) and Genetic Algorithm (GA). Besides, some numerical results indicate that the smoothing Newton method with PPNCP function is promising. The emerging aspects of this paper include: 1) it integrates the path planning with time allocation problem for maximizing the information collection reward; 2) a new PPNCP-function and a smoothing Newton method have been proposed to solve the optimal time allocation under path planning of UAVs; 3) the theoretical convergence analysis of the smoothing Newton method has been provided in this paper.

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Section: A Experiments Onmentioning

confidence: 92%

Section: Literature Reviewmentioning

confidence: 99%

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Liu

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

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“…This optimization method innovatively designed a reference point update method. To maintain the balance between convergence and diversity in the solution process of many-objective optimization method, Zhang et al [20] studied a many-objective evolution method based on the point of determinacy. In order to optimize the high-dimensional many-objective FJSSP, Li et al [21] designed a new imperialist competition algorithm, which added a variable neighborhood search algorithm.…”

Section: Related Workmentioning

confidence: 99%

Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration

Sang

Tan

2022

Energies

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With the increasingly customized product requirements of customers, the manufactured products have the characteristics of multi-variety and small-batch production. A high-quality production scheduling scheme can reduce energy consumption, improve production capacity and processing quality of the enterprise. The high-dimensional many-objective green flexible job shop scheduling problem (Ma-OFJSSP) urgently needs to be solved. However, the existing optimization method are difficult to effectively optimize the Ma-OFJSSP. This study proposes a many-objective flexible job shop scheduling model. An optimization method SV-MA is designed to effectively optimize the Ma-OFJSSP model. The SV-MA memetic algorithm combines an improved strength Pareto evolution method (SPEA2) and the variable neighborhood search method. To effectively distinguish the better solutions and increase the selection pressure of the non-dominated solutions, the fitness calculation method based on the shift-based density estimation strategy is adopted. The SV-MA algorithm designs the variable neighborhood strategy which combines with scheduling knowledge. Finally, in the workshop scheduling benchmarks and the machining workshop engineering case, the feasibility and effectiveness of the proposed model and SV-MA algorithm are verified by comparison with other methods. The production scheduling scheme obtained by the proposed model and SV-MA optimization algorithm can improve production efficiency and reduce energy consumption in the production process.

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“…To verify the proposed 2D&R, the following five different types of algorithms are selected to incorporate 2D&R: 1) MaOEADPP [47], a novel approach using a repulsive point process to identify high-quality solutions based on the decomposition of the kernel matrix; 2) MOEA/D-DU [48], a decomposition-based algorithm that update the K nearest parent solutions based on the perpendicular distance from a solution to the weight vector for a better tradeoff between convergence and diversity; 3) NSGA-II [49], a classical Pareto dominance-based approach with elite mechanism and maintain diversity through crowding distance sorting; 4) NSGA-III [50], a reference point-based algorithm; 5) 1by1EA [51], a niche-based MOEA which selects the offspring individuals one-by-one based on the convergence indicator.…”

Section: B Algorithm Selectionmentioning

confidence: 99%

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

Xiong

Zheng

2022

IEEE Access

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Most existing evolutionary approaches to satellite range scheduling seek optimal solution in terms of the request satisfaction. The scheduling demand of managing ground station resource is seldom considered, which restricts their real-world applications. To effectively generate a set of more rational satellite range schedules, this paper establishes the multi-objective satellite range scheduling mathematical model. Unlike existing approaches, we propose a general population generation approach to solve the problem without relying on any specific kind of evolutionary algorithm, so different types of evolutionary algorithms can be extended to satellite range scheduling without modifying the original framework and search strategy. The idea is to utilize the request satisfaction and resource utilization knowledge learnt from parent schedules to guide the generation and updating of new solutions. Furthermore, an iterative rewriting operator is designed to guide a biased faster convergence towards the low request failure region in objective space. The proposed approach has been applied to five different types of classical and state-ofthe-art evolutionary algorithms and examined on benchmark problems. Experimental results illustrate the search efficiency enhancement and good adaptability to different evolutionary algorithms, which show the broad application prospect for satellite range scheduling.

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A New Many-Objective Evolutionary Algorithm Based on Determinantal Point Processes

Cited by 37 publications

References 41 publications

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Many-Objective Flexible Job Shop Scheduling Problem with Green Consideration

Evolutionary Multiobjective Satellite Range Scheduling With Learning-Guided Population Generation

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