Combining Kernelized Autoencoding and Centroid Prediction for Dynamic
Multi-objective Optimization
- URL: http://arxiv.org/abs/2312.00978v1
- Date: Sat, 2 Dec 2023 00:24:22 GMT
- Title: Combining Kernelized Autoencoding and Centroid Prediction for Dynamic
Multi-objective Optimization
- Authors: Zhanglu Hou, Juan Zou, Gan Ruan, Yuan Liu, Yizhang Xia
- Abstract summary: This paper proposes a unified paradigm, which combines the kernelized autoncoding evolutionary search and the centriod-based prediction.
The proposed method is compared with five state-of-the-art algorithms on a number of complex benchmark problems.
- Score: 3.431120541553662
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Evolutionary algorithms face significant challenges when dealing with dynamic
multi-objective optimization because Pareto optimal solutions and/or Pareto
optimal fronts change. This paper proposes a unified paradigm, which combines
the kernelized autoncoding evolutionary search and the centriod-based
prediction (denoted by KAEP), for solving dynamic multi-objective optimization
problems (DMOPs). Specifically, whenever a change is detected, KAEP reacts
effectively to it by generating two subpopulations. The first subpoulation is
generated by a simple centriod-based prediction strategy. For the second
initial subpopulation, the kernel autoencoder is derived to predict the moving
of the Pareto-optimal solutions based on the historical elite solutions. In
this way, an initial population is predicted by the proposed combination
strategies with good convergence and diversity, which can be effective for
solving DMOPs. The performance of our proposed method is compared with five
state-of-the-art algorithms on a number of complex benchmark problems.
Empirical results fully demonstrate the superiority of our proposed method on
most test instances.
Related papers
- Dynamic Incremental Optimization for Best Subset Selection [15.8362578568708]
Best subset selection is considered the gold standard for many learning problems.
An efficient subset-dual algorithm is developed based on the primal and dual problem structures.
arXiv Detail & Related papers (2024-02-04T02:26:40Z) - Analyzing and Enhancing the Backward-Pass Convergence of Unrolled
Optimization [50.38518771642365]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form.
This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method.
A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
arXiv Detail & Related papers (2023-12-28T23:15:18Z) - Bidirectional Looking with A Novel Double Exponential Moving Average to
Adaptive and Non-adaptive Momentum Optimizers [109.52244418498974]
We propose a novel textscAdmeta (textbfADouble exponential textbfMov averagtextbfE textbfAdaptive and non-adaptive momentum) framework.
We provide two implementations, textscAdmetaR and textscAdmetaS, the former based on RAdam and the latter based on SGDM.
arXiv Detail & Related papers (2023-07-02T18:16:06Z) - Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks.
One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver.
This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
arXiv Detail & Related papers (2023-01-28T01:50:42Z) - A novel multiobjective evolutionary algorithm based on decomposition and
multi-reference points strategy [14.102326122777475]
Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been regarded as a significantly promising approach for solving multiobjective optimization problems (MOPs)
We propose an improved MOEA/D algorithm by virtue of the well-known Pascoletti-Serafini scalarization method and a new strategy of multi-reference points.
arXiv Detail & Related papers (2021-10-27T02:07:08Z) - Batched Data-Driven Evolutionary Multi-Objective Optimization Based on
Manifold Interpolation [6.560512252982714]
We propose a framework for implementing batched data-driven evolutionary multi-objective optimization.
It is so general that any off-the-shelf evolutionary multi-objective optimization algorithms can be applied in a plug-in manner.
Our proposed framework is featured with a faster convergence and a stronger resilience to various PF shapes.
arXiv Detail & Related papers (2021-09-12T23:54:26Z) - Solving Large-Scale Multi-Objective Optimization via Probabilistic
Prediction Model [10.916384208006157]
An efficient LSMOP algorithm should have the ability to escape the local optimal solution from the huge search space.
Maintaining the diversity of the population is one of the effective ways to improve search efficiency.
We propose a probabilistic prediction model based on trend prediction model and generating-filtering strategy, called LT-PPM, to tackle the LSMOP.
arXiv Detail & Related papers (2021-07-16T09:43:35Z) - Momentum Accelerates the Convergence of Stochastic AUPRC Maximization [80.8226518642952]
We study optimization of areas under precision-recall curves (AUPRC), which is widely used for imbalanced tasks.
We develop novel momentum methods with a better iteration of $O (1/epsilon4)$ for finding an $epsilon$stationary solution.
We also design a novel family of adaptive methods with the same complexity of $O (1/epsilon4)$, which enjoy faster convergence in practice.
arXiv Detail & Related papers (2021-07-02T16:21:52Z) - An Online Prediction Approach Based on Incremental Support Vector
Machine for Dynamic Multiobjective Optimization [19.336520152294213]
We propose a novel prediction algorithm based on incremental support vector machine (ISVM)
We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process.
The proposed algorithm can effectively tackle dynamic multiobjective optimization problems.
arXiv Detail & Related papers (2021-02-24T08:51:23Z) - Bilevel Optimization: Convergence Analysis and Enhanced Design [63.64636047748605]
Bilevel optimization is a tool for many machine learning problems.
We propose a novel stoc-efficientgradient estimator named stoc-BiO.
arXiv Detail & Related papers (2020-10-15T18:09:48Z) - EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm
for Constrained Global Optimization [68.8204255655161]
EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables.
It implements a number of improvements to the well-known Differential Evolution (DE) algorithm.
Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
arXiv Detail & Related papers (2020-07-09T10:19:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.