A memetic level-based learning swarm optimizer for large-scale water distribution network optimization

Jia, Ya-Hui; Mei, Yi; Zhang, Mengjie

doi:10.1145/3377930.3389828

Cited by 8 publications

(2 citation statements)

References 28 publications

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“…If a function is nonadditively separable, i.e., every pair of its subfunctions is also non-additively separable, DG-based decomposition strategies may report false linkage. Let us consider the minimization of the following function: fc,1 : [−5, 5] 2 → [0, 100] defined as fc,1 (x) = (|x 1 | + |x 2 |) 2 . A greedy optimizer that is deterministic (e.g., with a fixed value of the random seed) applied to optimize x 1 , for any value of x 2 , will converge to the same value and vice versa.…”

Section: B Decomposition Inaccuracies: False and Missing Linkagementioning

confidence: 99%

Incremental Recursive Ranking Grouping for Large-Scale Global Optimization

Komarnicki

Przewoźniczek

Kwaśnicka

et al. 2023

IEEE Trans. Evol. Computat.

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Real-world optimization problems may have a different underlying structure. In black-box optimization, the dependencies between decision variables remain unknown. However, some techniques can discover such interactions accurately. In Large Scale Global Optimization (LSGO), problems are high-dimensional. It was shown effective to decompose LSGO problems into subproblems and optimize them separately. The effectiveness of such approaches may be highly dependent on the accuracy of problem decomposition. Many state-of-the-art decomposition strategies are derived from Differential Grouping (DG). However, if a given problem consists of non-additively separable subproblems, DG-based strategies may discover many non-existing interactions. On the other hand, monotonicity checking strategies proposed so far do not report non-existing interactions for any separable subproblems but may miss discovering many of the existing ones. Therefore, we propose Incremental Recursive Ranking Grouping (IRRG) that suffers from none of these flaws. IRRG consumes more fitness function evaluations than the recent DG-based propositions, e.g., Recursive DG 3 (RDG3). Nevertheless, the effectiveness of the considered Cooperative Co-evolution frameworks after embedding IRRG or RDG3 was similar for problems with additively separable subproblems that are suitable for RDG3. After replacing the additive separability with non-additive, embedding IRRG leads to results of significantly higher quality.

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Section: B Decomposition Inaccuracies: False and Missing Linkagementioning

confidence: 99%

Incremental Recursive Ranking Grouping for Large-Scale Global Optimization

Komarnicki

Przewoźniczek

Kwaśnicka

et al. 2023

IEEE Trans. Evol. Computat.

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show abstract

“…Recently, evolutionary algorithms (EAs) have been applied to many combinatorial optimization problems and proven to be very successful in real-world applications [7,8,3]. Differential evolution (DE) is an efficient heuristic optimization algorithm that facilitates a population-based search in continuous multidimensional spaces [15,18].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems

Xie

Neumann

2021

Preprint

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The Stockpile blending problem is an important component of mine production scheduling, where stockpiles are used to store and blend raw material. The goal of blending material from stockpiles is to create parcels of concentrate which contain optimal metal grades based on the material available. The volume of material that each stockpile provides to a given parcel is dependent on a set of mine schedule conditions and customer demands. Therefore, the problem can be formulated as a continuous optimization problem. In the real-world application, there are several constraints required to guarantee parcels that meet the demand of downstream customers. It is a challenge in solving the stockpile blending problems since its scale can be very large. We introduce two repaired operators for the problems to convert the infeasible solutions into the solutions without violating the two tight constraints. Besides, we introduce a multi-component fitness function for solving the large-scale stockpile blending problem which can maximize the volume of metal over the plan and maintain the balance between stockpiles according to the usage of metal. Furthermore, we investigate the well-known approach in this paper, which is used to solve optimization problems over continuous space, namely the differential evolution (DE) algorithm. The experimental results show that the DE algorithm combined with two proposed duration repair methods is significantly better in terms of the values of results than the results on real-world instances for both one-month problems and large-scale problems.

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Application of Self-adaptive Vision-Correction Algorithm for Water-Distribution Problem

Lee

2021

KSCE J Civ Eng

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A memetic level-based learning swarm optimizer for large-scale water distribution network optimization

Cited by 8 publications

References 28 publications

Incremental Recursive Ranking Grouping for Large-Scale Global Optimization

Incremental Recursive Ranking Grouping for Large-Scale Global Optimization

Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems

Application of Self-adaptive Vision-Correction Algorithm for Water-Distribution Problem

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