Benefits of sparse population sampling in multi-objective evolutionary computing for large-Scale sparse optimization problems

Kropp, Ian; Nejadhashemi, A. Pouyan; Deb, Kalyanmoy

doi:10.1016/j.swevo.2021.101025

Cited by 20 publications

(3 citation statements)

References 18 publications

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“…In addition, since the objective is to minimize the total length of the power supply circuits, many decision variables should be set to 0. Therefore, the MNSDP is a typical large-scale sparse optimization problem [46]. As a result, to accelerate the searching process, it's quite necessary to generate high-quality solutions through heuristic methods.…”

Section: B General Initialization Methods For Bmopsmentioning

confidence: 99%

Large-scale matrix optimization based multi microgrid topology design with a constrained differential evolution algorithm

Li¹,

Huang²,

Zhang³

et al. 2022

Preprint

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Binary matrix optimization commonly arise in the real world, e.g., multi-microgrid network structure design problem (MGNSDP), which is to minimize the total length of the power supply line under certain constraints. Finding the global optimal solution for these problems faces a great challenge since such problems could be large-scale, sparse and multimodal. Traditional linear programming is time-consuming and cannot solve nonlinear problems. To address this issue, a novel improved feasibility rule based differential evolution algorithm, termed LBMDE, is proposed. To be specific, a general heuristic solution initialization method is first proposed to generate high-quality solutions. Then, a binary-matrix-based DE operator is introduced to produce offspring. To deal with the constraints, we proposed an improved feasibility rule based environmental selection strategy. The performance and searching behaviors of LBMDE are examined by a set of benchmark problems.

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Section: B General Initialization Methods For Bmopsmentioning

confidence: 99%

Large-scale matrix optimization based multi microgrid topology design with a constrained differential evolution algorithm

Li¹,

Huang²,

Zhang³

et al. 2022

Preprint

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show abstract

“…In [9,66,67], the author generate a UE/UD problem for a target algorithm, using statistical tests to determine a significant difference between the target algorithm and other algorithms. In [68], the author presents a new approach to improve large-scale sparse muldti-objective algorithms using sparse overall sampling (SPS) methods. hypothesis testing is applied to determine if existing algorithms using SPS are effective enough to be used as a stand-alone sparse optimization algorithm.…”

Section: Hypothesis Testingmentioning

confidence: 99%

CNN-HT: A Two-Stage Algorithm Selection Framework

Liu

et al. 2023

Preprint

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The No Free Lunch theorem tells us that no algorithm can beat other algorithms on all types of problems. The algorithm selection structure is proposed to select the most suitable algorithm from a set of algorithms for an unknown optimization problem. In this paper, an innovative algorithm selection approach called the CNN-HT two-stage algorithm selection framework is introduced. In the first stage, a Convolutional Neural Network (CNN) is employed to classify problems. In the second stage, the Hypothesis Testing (HT) technique is used to suggest the best-performing algorithm based on the statistical analysis of the performance metric of algorithms that address various problem categories. To provide a more general structure for the classification model, we adopt Exploratory Landscape Analysis (ELA) features of the problem as the input and utilize feature selection techniques to reduce the redundant ones. In problem classification, our proposed algorithm selection framework achieves an average accuracy of 96% for the classification of unknown black-box problems, and it improves to 98.8% after feature selection. This shows that our proposed classification method can accurately classify BBOB problems into 24 classes of problems and thus can correctly recommend algorithms accordingly. The performance of CNN-HT is compared with a single optimization algorithm on a continuous black-box optimization problem set, and the average ranking of CNN-HT is superior, demonstrating its effectiveness.

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“…Li et al formulated the power flow optimization with uncertain wind and solar energy into multiobjective problems, and introduced constraint handle technique to resolve it . Kropp et al proposed a sparse population sampling approach to initialized population for largescale sparse multi-objective algorithms (Kropp et al, 2022). Liu et al employed a feedforward neural network to learn a gradient-descent-like direction to reproduce offspring solutions for efficiently tackling LSMOPs (Liu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Coarse-to-fine evolutionary search for large-scale multi-objective optimization: An application to ratio error estimation of voltage transformers

Zhang²,

Xing³

2022

Front. Energy Res.

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Multi-objective optimization problems (MOPs) are commonly confronted in various fields, such as condition monitoring for renewable energy systems, and ratio error estimation of voltage transformers. With the increase in decision variables of MOPs, their exponentially growing search spaces are challenging for existing evolutionary algorithms. To handle this challenge, this paper suggests a coarse-to-fine large-scale evolutionary multi-objective search, called CF-LEMO. In the coarse search phase, CF-LEMO performs evolutionary search on both the original and transformed large-scale MOPs alternately, accelerating the population to approach the Pareto-optimal fronts. In addition, to alleviate the issue of diversity loss, we design a diversity preservation mechanism to preserve a well-distributed archive to support subsequent fine search. In the fine search stage, CF-LEMO conducts local search on the current population to mine high-quality solutions, which are used to update the population and archive. Then, based on the archive, the multi-objective optimization based on decomposition is employed to evolve all decision variables, so as to obtain a population with good convergence and diversity near the Pareto-optimal fronts. To assess the effectiveness of the proposed CF-LEMO, we compare its performance against four representative baseline algorithms on a benchmarks suite LSMOP1-LSMOP9 with 2 and 3 objectives. The empirical results confirm its super performance by significanlty outperforming all the four competitors on 12 out of 18 benchmarks. Moreover, the experiments demonstrate the superior performance of CF-LEMO in sovling multi-objective ratio error estimation problems with up to 6,000 decision variables.

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Benefits of sparse population sampling in multi-objective evolutionary computing for large-Scale sparse optimization problems

Cited by 20 publications

References 18 publications

Large-scale matrix optimization based multi microgrid topology design with a constrained differential evolution algorithm

Large-scale matrix optimization based multi microgrid topology design with a constrained differential evolution algorithm

CNN-HT: A Two-Stage Algorithm Selection Framework

Coarse-to-fine evolutionary search for large-scale multi-objective optimization: An application to ratio error estimation of voltage transformers

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