Cartesian trajectory planning of space robots using a multi-objective optimization

Jin, Rongyu; Rocco, Paolo; Geng, Yunhai

doi:10.1016/j.ast.2020.106360

Cited by 60 publications

(17 citation statements)

References 23 publications

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“…In total, we performed experimental comparisons on five pairs of parameters of n t and τ t , including (5,5), (5,10), (5,15), (5,20), and (10,5). Table 5 shows the DMIGD values for each algorithm on all tested functions.…”

Section: Results On Dmigd Metricmentioning

confidence: 99%

“…Table 6. Mean and SD of MIGD indicator obtained by seven algorithms for (n t , τ t ) = (5, 10), (5,15), and (5,20). From Table 9, we can obviously notice that the performance of KPTHP is significantly better than the other two versions, which suggests that each component of KPTHP has an indispensable influence.…”

Section: Analysis Of the Different Components Of Kpthpmentioning

confidence: 96%

“…In the following, we present a visualization of the excellent performance of the algorithm, including the IGD evolution curves of the sixteen functions at different moments and the POFs of FDA1, dMOP2, and DF2 obtained by the seven algorithms. The change frequency τ t and change severity n t of all the algorithms are fixed to (5,20).…”

Section: Analyzing the Evolutionary Processes Of The Compared Algorithmsmentioning

confidence: 99%

“…Multi-objective optimization problems (MOPs) [1,2] are dedicated to optimizing multiple objectives simultaneously to obtain an approximate optimal solution. They have a wide range of applications, including environmental applications [3], biology [4], space robots [5], and energy allocation [6,7]. Multi-objective evolutionary algorithms (MOEAs) [8][9][10][11][12] are considered an efficient choice to deal with MOPs by finding the most plausible individuals in the objective space to compose the POS.…”

Section: Introductionmentioning

confidence: 99%

“…Mean and SD of HVD indicator obtained by seven algorithms for (n t , τ t ) = (5,10),(5,15), and(5,20).…”

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confidence: 99%

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Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization

Wang

2022

Mathematics

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Dynamic multi-objective optimization problems (DMOPs) have been of interest to many researchers. These are problems in which the environment changes during the evolutionary process, such as the Pareto-optimal set (POS) or the Pareto-optimal front (POF). This kind of problem imposes more challenges and difficulties for evolutionary algorithms, mainly because it demands population to track the changing POF efficiently and accurately. In this paper, we propose a new approach combining key-points-based transfer learning and hybrid prediction strategies (KPTHP). In particular, the transfer process combines predictive strategy with obtaining anticipated key points depending on the previous moments to acquire the optimal individuals at the new instance during the evolution. Additionally, center-point-based prediction is used to complement transfer learning to comprehensively generate initial populations. KPTHP and six state-of-the-art algorithms are tested on various test functions for MIGD, DMIGD, MMS, and HVD metrics. KPTHP obtains superior results on most of the tested functions, which shows that our algorithm performs excellently in both convergence and diversity, with more competitiveness in addressing dynamic problems.

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