A novel predictive method based on key points for dynamic multi-objective optimization

Wang, Chunfeng; Yen, Gary G.; Zou, Fei

doi:10.1016/j.eswa.2021.116127

Cited by 13 publications

(3 citation statements)

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“…The procedure for solving the polar point is as described below. In this section, we choose TOPSIS method [63,64] to obtain the polar point. First, TOPOSIS calculates the weighted decision matrix and determines the positive ideal solution and negative ideal solution.…”

Section: Determine Key Pointsmentioning

confidence: 99%

“…All the problems are run 30 times independently on benchmark problems, and the environmental changes are set to 20 times in each run. For a fair comparison, the specific parameters for PPS, GM-DMOP, MMTL-DMOEA, IT-DMOEA, KT-DMOEA, and KDMOP are set according to their original publications [34,37,64,70,71,75]. For KPTHP, in the transfer stage, most of the parameters in the TrAdaBoost are set by default [76].…”

Section: Comparison Algorithms and Parameter Settingsmentioning

confidence: 99%

“…It integrates a small number of high-quality individuals with the imbalance transfer learning technique seamlessly, greatly improving the performance of dynamic optimization. (6) KDMOP[64]: KDMOP was proposed recently by Yen et al, and it introduces a wellregarded multi-attribute decision-making strategy called TOPSIS. TOPSIS is utilized to obtain the initial population in a new environment, and it is used to select good individuals in mating selection and environmental selection.…”

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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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Section: Determine Key Pointsmentioning

confidence: 99%