Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

Zhiming Lv; Linqing Wang; Zhongyang Han; Jun Zhao; Wei Wang

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Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

机译：具有帕累托主动学习的替代辅助粒子群优化算法用于昂贵的多目标优化

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For multi-objective optimization problems,particle swarm optimization (PSO) algorithm generally needs a large number of fitness evaluations to obtain the Parcto optimal solutions.However,it will become substantially time-consuming when handling computationally expensive fitness functions.In order to save the computational cost,a surrogate-assisted PSO with Pareto active learning is proposed.In real physical space (the objective functions are computationally expensive),PSO is used as an optimizer,and its optimization results are used to construct the surrogate models.In virtual space,objective functions are replaced by the cheaper surrogate models,PSO is viewed as a sampler to produce the candidate solutions.To enhance the quality of candidate solutions,a hybrid mutation sampling method based on the simulated evolution is proposed,which combines the advantage of fast convergence of PSO and implements mutation to increase diversity.Furthermore,ε-Pareto active learning (ε-PAL) method is employed to pre-select candidate solutions to guide PSO in the real physical space.However,little work has considered the method of determining parameter ε.Therefore,a greedy search method is presented to determine the value of ε where the number of active sampling is employed as the evaluation criteria of classification cost.Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines (MLSSVM) are given,in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization (MOPSO) algorithms.

机译：对于多目标优化问题，粒子群优化（PSO）算法通常需要进行大量适应度评估才能获得Parcto最优解。但是，在处理计算量大的适应度函数时，它将变得非常耗时。计算成本方面，提出了具有Pareto主动学习的代理辅助PSO。在实际物理空间（目标函数在计算上比较昂贵）中，PSO被用作优化器，其优化结果用于构建代理模型。在虚拟空间中目标函数被廉价的替代模型所取代，PSO被视为生成候选解的采样器。为了提高候选解的质量，提出了一种基于模拟演化的混合突变采样方法，该方法结合了快速的优点PSO收敛并实现变异以增加多样性。此外，ε-帕累托主动学习（ε-PAL）方法是试图预先选择候选解以在实际物理空间中引导PSO。但是，很少的工作考虑了确定参数ε的方法。因此，提出了一种贪婪搜索方法来确定ε的值，其中有效采样数为进行了涉及多个基准测试问题的应用和多输入多输出最小二乘支持向量机（MLSSVM）的参数确定的实验研究，结果表明该算法具有良好的性能。提出的算法与其他代表性的多目标粒子群优化（MOPSO）算法相比。

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来源
《自动化学报（英文版）》 |2019年第3期|838-849|共12页
作者
Zhiming Lv; Linqing Wang; Zhongyang Han; Jun Zhao; Wei Wang;
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Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;

Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;

Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;

Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;

Faculty of Electronics Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China;

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Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization

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