L-fuzzifying antimatroids: A fuzzy approach to the generalization of shelling precedence structures

2020 ◽  
Vol 39 (3) ◽  
pp. 4183-4196
Author(s):  
Fu-Ning Lin ◽  
Guang-Ji Yu ◽  
Guang-Ming Xue ◽  
Jiang-Feng Han
Keyword(s):  
Greedy Algorithm ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Optimal Solutions ◽  
Fuzzy Approach ◽  
Bijective Correspondence ◽  
Fundamental Properties ◽  
Feasible Sets ◽  
The Greedy Algorithm ◽  
Shed Light

 Crisp antimatroid is a combinatorial abstraction of convexity. It also can be incorporated into the greedy algorithm in order to seek the optimal solutions. Nevertheless, this kind of significant classical structure has inherent limitations in addressing fuzzy optimization problems and abstracting fuzzy convexities. This paper introduces the concept of L-fuzzifying antimatroid associated with an L-fuzzifying family of feasible sets. Several relevant fundamental properties are obtained. We also propose the concept of L-fuzzifying rank functions for L-fuzzifying antimatroids, and then investigate their axiomatic characterizations. Finally, we shed light upon the bijective correspondence between an L-fuzzifying antimatroid and its L-fuzzifying rank function.

scholarly journals Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1540
Author(s):  
Gyula Ábrahám ◽  
György Dósa ◽  
Tibor Dulai ◽  
Zsolt Tuza ◽  
Ágnes Werner-Stark
Keyword(s):  
Greedy Algorithm ◽  
Bin Packing ◽  
Industrial Applications ◽  
Benchmark Problems ◽  
Optimal Solutions ◽  
Research Areas ◽  
Efficiency Of Algorithms ◽  
Almost All ◽  
Short Time ◽  
The Greedy Algorithm

Bin Packing is one of the research areas of Operations Research with many industrial applications, as well as rich theoretical impact. In this article, the authors deal with Bin Packing on the practical side: they consider two Bin Packing Benchmark classes. These benchmark problems are often used to check the “usefulness”, efficiency of algorithms. The problem is well-known to be NP-hard. Instead of introducing some exact, heuristic, or approximation method (as usual), the problem is attacked here with some kind of greedy algorithm. These algorithms are very fast; on the other hand, they are not always able to provide optimal solutions. Nevertheless, they can be considered as pre-processing algorithms for solving the problem. It is shown that almost all problems in the considered two benchmark classes are, in fact, easy to solve. In case of the Schwerin class, where there are 200 instances, it is obtained that all instances are solved by the greedy algorithm, optimally, in a very short time. The Falkenauer U class is a little bit harder, but, here, still more than 91% of the instances are solved optimally very fast, with the help of another greedy algorithm. Based on the above facts, the main contribution of the paper is to show that pre-processing is very useful for solving such kinds of problems.

Implementasi Algoritma Dijkstra Pada Game Pacman

CCIT Journal ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 170-176
Author(s):  
Anggit Dwi Hartanto ◽  
Aji Surya Mandala ◽  
Dimas Rio P.L. ◽  
Sidiq Aminudin ◽  
Andika Yudirianto
Keyword(s):  
Artificial Intelligence ◽  
Greedy Algorithm ◽  
Dijkstra Algorithm ◽  
Testing Phase ◽  
A Value ◽  
Route Problem ◽  
Starting Point ◽  
The Greedy Algorithm

Pacman is one of the labyrinth-shaped games where this game has used artificial intelligence, artificial intelligence is composed of several algorithms that are inserted in the program and Implementation of the dijkstra algorithm as a method of solving problems that is a minimum route problem on ghost pacman, where ghost plays a role chase player. The dijkstra algorithm uses a principle similar to the greedy algorithm where it starts from the first point and the next point is connected to get to the destination, how to compare numbers starting from the starting point and then see the next node if connected then matches one path with the path). From the results of the testing phase, it was found that the dijkstra algorithm is quite good at solving the minimum route solution to pursue the player, namely by getting a value of 13 according to manual calculations

scholarly journals An Optimized Algorithm and Test Bed for Improvement of Efficiency of ESS and Energy Use

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 388 ◽  
Author(s):  
Seung-Mo Je ◽  
Jun-Ho Huh
Keyword(s):  
Greedy Algorithm ◽  
Energy Use ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Test Bed ◽  
C Language ◽  
Improvement Plan ◽  
The Republic ◽  
The Greedy Algorithm

The Republic of Korea (ROK) has four distinct seasons. Such an environment provides many benefits, but also brings some major problems when using new and renewable energies. The rainy season or typhoons in summer become the main causes of inconsistent production rates of these energies, and this would become a fatal weakness in supplying stable power to the industries running continuously, such as the aquaculture industry. This study proposed an improvement plan for the efficiency of Energy Storage System (ESS) and energy use. Use of sodium-ion batteries is suggested to overcome the disadvantages of lithium-ion batteries, which are dominant in the current market; a greedy algorithm and the Floyd–Warshall algorithm were also proposed as a method of scheduling energy use considering the elements that could affect communication output and energy use. Some significant correlations between communication output and energy efficiency have been identified through the OPNET-based simulations. The simulation results showed that the greedy algorithm was more efficient. This algorithm was then implemented with C-language to apply it to the Test Bed developed in the previous study. The results of the Test Bed experiment supported the proposals.

scholarly journals On quantitative stability in infinite-dimensional optimization under uncertainty

2021 ◽  
Author(s):  
M. Hoffhues ◽  
W. Römisch ◽  
T. M. Surowiec
Keyword(s):  
Stochastic Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Optimization Under Uncertainty ◽  
Optimal Solutions ◽  
Probability Metrics ◽  
Pde Constrained Optimization ◽  
Quantitative Stability ◽  
Infinite Dimensional ◽  
Optimal Value

AbstractThe vast majority of stochastic optimization problems require the approximation of the underlying probability measure, e.g., by sampling or using observations. It is therefore crucial to understand the dependence of the optimal value and optimal solutions on these approximations as the sample size increases or more data becomes available. Due to the weak convergence properties of sequences of probability measures, there is no guarantee that these quantities will exhibit favorable asymptotic properties. We consider a class of infinite-dimensional stochastic optimization problems inspired by recent work on PDE-constrained optimization as well as functional data analysis. For this class of problems, we provide both qualitative and quantitative stability results on the optimal value and optimal solutions. In both cases, we make use of the method of probability metrics. The optimal values are shown to be Lipschitz continuous with respect to a minimal information metric and consequently, under further regularity assumptions, with respect to certain Fortet-Mourier and Wasserstein metrics. We prove that even in the most favorable setting, the solutions are at best Hölder continuous with respect to changes in the underlying measure. The theoretical results are tested in the context of Monte Carlo approximation for a numerical example involving PDE-constrained optimization under uncertainty.

Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems

2021 ◽  
Vol 12 (4) ◽  
pp. 81-100
Author(s):  
Yao Peng ◽  
Zepeng Shen ◽  
Shiqi Wang
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Algorithm ◽  
Peak Detection ◽  
Multimodal Optimization ◽  
Optimal Solutions ◽  
Grouping Strategy ◽  
Different Dimensions ◽  
Global Optimal ◽  
Localization Ability

Multimodal optimization problem exists in multiple global and many local optimal solutions. The difficulty of solving these problems is finding as many local optimal peaks as possible on the premise of ensuring global optimal precision. This article presents adaptive grouping brainstorm optimization (AGBSO) for solving these problems. In this article, adaptive grouping strategy is proposed for achieving adaptive grouping without providing any prior knowledge by users. For enhancing the diversity and accuracy of the optimal algorithm, elite reservation strategy is proposed to put central particles into an elite pool, and peak detection strategy is proposed to delete particles far from optimal peaks in the elite pool. Finally, this article uses testing functions with different dimensions to compare the convergence, accuracy, and diversity of AGBSO with BSO. Experiments verify that AGBSO has great localization ability for local optimal solutions while ensuring the accuracy of the global optimal solutions.

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