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Journal articles on the topic 'Competitive algorithms'

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Published: 25 May 2024

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1

Im, Sungjin, Janardhan Kulkarni, and Kamesh Munagala. "Competitive Algorithms from Competitive Equilibria." Journal of the ACM 65, no. 1 (2018): 1–33. http://dx.doi.org/10.1145/3136754.

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2

Bender, Michael A., Jeremy T. Fineman, Mahnush Movahedi, et al. "Resource-Competitive Algorithms." ACM SIGACT News 46, no. 3 (2015): 57–71. http://dx.doi.org/10.1145/2818936.2818949.

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3

Fiat, Amos, Richard M. Karp, Michael Luby, Lyle A. McGeoch, Daniel D. Sleator, and Neal E. Young. "Competitive paging algorithms." Journal of Algorithms 12, no. 4 (1991): 685–99. http://dx.doi.org/10.1016/0196-6774(91)90041-v.

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4

Budura, Georgeta, Corina Botoca, and Nicolae Miclău. "Competitive learning algorithms for data clustering." Facta universitatis - series: Electronics and Energetics 19, no. 2 (2006): 261–69. http://dx.doi.org/10.2298/fuee0602261b.

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This paper presents and discusses some competitive learning algorithms for data clustering. A new competitive learning algorithm, named the dynamically penalized rival competitive learning algorithm (DPRCL), is introduced and studied. It is a variant of the rival penalized competitive algorithm [1] and it performs appropriate clustering without knowing the clusters number, by automatically driving the extra seed points far away from the input data set. It does not have the 'dead units' problem. Simulations results, performed in different conditions, are presented showing that the performance of the new DPRCL algorithm is better comparative with other competitive algorithms.
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LIU, ZHI-QIANG, and YAJUN ZHANG. "COMPENSATION COMPETITIVE LEARNING." International Journal of Computational Intelligence and Applications 01, no. 03 (2001): 303–22. http://dx.doi.org/10.1142/s1469026801000263.

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In general, in competitive learning the requirement for the initial number of prototypes is a difficult task, as we do not usually know the number of clusters in the input data a priori. The behavior and performance of the competitive algorithms are very sensitive to the initial locations and number of the prototypes. In this paper after investigating several important competitive learning paradigms, we present compensation techniques for overcoming the problems in competitive learning. Our experimental results show that competition with compensation can improve the performance of the learning algorithm.
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Fiat, Amos, Yuval Rabani, and Yiftach Ravid. "Competitive k-server algorithms." Journal of Computer and System Sciences 48, no. 3 (1994): 410–28. http://dx.doi.org/10.1016/s0022-0000(05)80060-1.

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7

Majd, Amin, Golnaz Sahebi, Masoud Daneshtalab, Juha Plosila, Shahriar Lotfi, and Hannu Tenhunen. "Parallel imperialist competitive algorithms." Concurrency and Computation: Practice and Experience 30, no. 7 (2018): e4393. http://dx.doi.org/10.1002/cpe.4393.

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Wu, Yonghua, Guohun Zhu, Huaying Chen, and Jucun Qin. "WIN Algorithm for Discrete Online TSP." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1199–202. http://dx.doi.org/10.20965/jaciii.2011.p1199.

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Traveling Salesman Problem (TSP) which is proved as an NP-Complete problem is solved by many algorithms. In this paper, we propose online TSP which is based on general discrete metric space. A Waiting-If-Necessary (WIN) algorithm is proposed that involves with increasing cost caused by zealous algorithms and unnecessary waiting caused by cautious algorithms. We measure the performance of the WIN algorithm using competitive analysis and found that it is a 2-competitive algorithm. The competitive ratio of theWIN algorithm can be improved by setting parameterT0.
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Osman, Hossam, and Moustafa M. Fahmy. "Probabilistic Winner-Take-All Learning Algorithm for Radial-Basis-Function Neural Classifiers." Neural Computation 6, no. 5 (1994): 927–43. http://dx.doi.org/10.1162/neco.1994.6.5.927.

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This paper proposes a new adaptive competitive learning algorithm called “the probabilistic winner-take-all.” The algorithm is based on a learning scheme developed by Agrawala within the statistical pattern recognition literature (Agrawala 1970). Its name stems from the fact that for a given input pattern once each competitor computes the probability of being the one that generated this pattern, the computed probabilities are utilized to probabilistically choose a winner. Then, only this winner is permitted to learn. The learning rule of the algorithm is derived for three different cases. Its properties are discussed and compared to those of two other competitive learning algorithms, namely the standard winner-take-all and the maximum-likelihood soft competition. Experimental comparison is also given. When all three algorithms are used to train the hidden layer of radial-basis-function classifiers, experiments indicate that classifiers trained with the probabilistic winner-take-all outperform those trained with the other two algorithms.
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10

Mohapatra, Prabhujit, Kedar Nath Das, Santanu Roy, Ram Kumar, and Nilanjan Dey. "A Novel Multi-Objective Competitive Swarm Optimization Algorithm." International Journal of Applied Metaheuristic Computing 11, no. 4 (2020): 114–29. http://dx.doi.org/10.4018/ijamc.2020100106.

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In this article, a new algorithm, namely the multi-objective competitive swarm optimizer (MOCSO), is introduced to handle multi-objective problems. The algorithm has been principally motivated from the competitive swarm optimizer (CSO) and the NSGA-II algorithm. In MOCSO, a pair wise competitive scenario is presented to achieve the dominance relationship between two particles in the population. In each pair wise competition, the particle that dominates the other particle is considered the winner and the other is consigned as the loser. The loser particles learn from the respective winner particles in each individual competition. The inspired CSO algorithm does not use any memory to remember the global best or personal best particles, hence, MOCSO does not need any external archive to store elite particles. The experimental results and statistical tests confirm the superiority of MOCSO over several state-of-the-art multi-objective algorithms in solving benchmark problems.
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11

Huang, Jian, and Yijun Gu. "Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity." Applied Sciences 13, no. 18 (2023): 10496. http://dx.doi.org/10.3390/app131810496.

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Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks.
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12

Jung, Sung-Hoon. "Competitive Generation for Genetic Algorithms." Journal of Korean Institute of Intelligent Systems 17, no. 1 (2007): 86–93. http://dx.doi.org/10.5391/jkiis.2007.17.1.086.

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13

Lin, Jun-Lin, Yu-Hsiang Tsai, Chun-Ying Yu, and Meng-Shiou Li. "Interaction Enhanced Imperialist Competitive Algorithms." Algorithms 5, no. 4 (2012): 433–48. http://dx.doi.org/10.3390/a5040433.

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14

ZHANG, YONG, YUXIN WANG, FRANCIS Y. L. CHIN, and HING-FUNG TING. "COMPETITIVE ALGORITHMS FOR ONLINE PRICING." Discrete Mathematics, Algorithms and Applications 04, no. 02 (2012): 1250015. http://dx.doi.org/10.1142/s1793830912500152.

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Given a seller with m items, a sequence of users {u1, u2, …} come one by one, the seller must set the unit price and assign some items to each user on his/her arrival. Items can be sold fractionally. Each ui has his/her value function vi(⋅) such that vi(x) is the highest unit price ui is willing to pay for x items. The objective is to maximize the revenue by setting the price and number of items for each user. In this paper, we have the following contributions: if the highest value h among all vi(x) is known in advance, we first show the lower bound of the competitive ratio is ⌊ log h⌋/2, then give an online algorithm with competitive ratio 4⌊ log h⌋ + 6; if h is not known in advance, we give an online algorithm with competitive ratio 2⋅h log -1/2 h + 8⋅h3 log -1/2 h.
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15

Dimou, C. K., and V. K. Koumousis. "Genetic Algorithms in Competitive Environments." Journal of Computing in Civil Engineering 17, no. 3 (2003): 142–49. http://dx.doi.org/10.1061/(asce)0887-3801(2003)17:3(142).

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16

Manasse, Mark S., Lyle A. McGeoch, and Daniel D. Sleator. "Competitive algorithms for server problems." Journal of Algorithms 11, no. 2 (1990): 208–30. http://dx.doi.org/10.1016/0196-6774(90)90003-w.

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17

Kumar, Sandeep, and Deepak Garg. "Online Financial Algorithms: Competitive Analysis." International Journal of Computer Applications 40, no. 7 (2012): 8–14. http://dx.doi.org/10.5120/4974-7228.

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18

Westphal, Stephan, Sven O. Krumke, and Rob van Stee. "Competitive Algorithms for Cottage Rental." Electronic Notes in Discrete Mathematics 25 (August 2006): 187–88. http://dx.doi.org/10.1016/j.endm.2006.06.069.

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19

Hansen, Karsten T., Kanishka Misra, and Mallesh M. Pai. "Frontiers: Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms." Marketing Science 40, no. 1 (2021): 1–12. http://dx.doi.org/10.1287/mksc.2020.1276.

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We show that the long-run prices from independent machine learning algorithms depend on the informational value of price experiments. If low, the long-run prices are consistent with the static Nash equilibrium; however, if high, the long-run prices are supra-competitive.
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20

Banerjee, Prithu, Wei Chen, and Laks V. S. Lakshmanan. "Maximizing social welfare in a competitive diffusion model." Proceedings of the VLDB Endowment 14, no. 4 (2020): 613–25. http://dx.doi.org/10.14778/3436905.3436920.

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Influence maximization (IM) has garnered a lot of attention in the literature owing to applications such as viral marketing and infection containment. It aims to select a small number of seed users to adopt an item such that adoption propagates to a large number of users in the network. Competitive IM focuses on the propagation of competing items in the network. Existing works on competitive IM have several limitations. (1) They fail to incorporate economic incentives in users' decision making in item adoptions. (2) Majority of the works aim to maximize the adoption of one particular item, and ignore the collective role that different items play. (3) They focus mostly on one aspect of competition - pure competition. To address these concerns we study competitive IM under a utility-driven propagation model called UIC, and study social welfare maximization. The problem in general is not only NP-hard but also NP-hard to approximate within any constant factor. We, therefore, devise instant dependent efficient approximation algorithms for the general case as well as a (1 - 1/ e - ∈ )-approximation algorithm for a restricted setting. Our algorithms outperform different baselines on competitive IM, both in terms of solution quality and running time on large real networks under both synthetic and real utility configurations.
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21

Lee, Russell, Jessica Maghakian, Mohammad Hajiesmaili, Jian Li, Ramesh Sitaraman, and Zhenhua Liu. "Online peak-aware energy scheduling with untrusted advice." ACM SIGEnergy Energy Informatics Review 1, no. 1 (2021): 59–77. http://dx.doi.org/10.1145/3508467.3508473.

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This paper studies the online energy scheduling problem in a hybrid model where the cost of energy is proportional to both the volume and peak usage, and where energy can be either locally generated or drawn from the grid. Inspired by recent advances in online algorithms with Machine Learned (ML) advice, we develop parameterized deterministic and randomized algorithms for this problem such that the level of reliance on the advice can be adjusted by a trust parameter. We then analyze the performance of the proposed algorithms using two performance metrics: robustness that measures the competitive ratio as a function of the trust parameter when the advice is inaccurate, and consistency for competitive ratio when the advice is accurate. Since the competitive ratio is analyzed in two different regimes, we further investigate the Pareto optimality of the proposed algorithms. Our results show that the proposed deterministic algorithm is Pareto-optimal, in the sense that no other online deterministic algorithms can dominate the robustness and consistency of our algorithm. Furthermore, we show that the proposed randomized algorithm dominates the Pareto-optimal deterministic algorithm. Our large-scale empirical evaluations using real traces of energy demand, energy prices, and renewable energy generations highlight that the proposed algorithms outperform worst-case optimized algorithms and fully data-driven algorithms.
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22

Lohn, Jason D., Gregory S. Hornby, and Derek S. Linden. "Human-competitive evolved antennas." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22, no. 3 (2008): 235–47. http://dx.doi.org/10.1017/s0890060408000164.

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AbstractWe present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASA's Space Technology 5 mission between March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.
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23

Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization." Evolutionary Computation 18, no. 1 (2010): 127–56. http://dx.doi.org/10.1162/evco.2010.18.1.18105.

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This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.
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Miura, Takeshi, Kentaro Sano, Kenichi Suzuki, and Tadao Nakamura. "A Competitive Learning Algorithm with Controlling Maximum Distortion." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 2 (2005): 166–74. http://dx.doi.org/10.20965/jaciii.2005.p0166.

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Vector quantization with an optimal codebook is attractive for lossy data compression. So far, a number of codebook design algorithms have been proposed to minimize the mean square error, MSE. However, these algorithms have a problem that MSE minimization sometimes causes an unacceptable maximum-distortion, which is very important in several applications. This paper proposes a competitive learning algorithm with controlling maximum distortion that designs a codebook giving a maximum distortion within a given error bound. The proposed algorithm assigns a code vector to an input vector with a too large distortion. Experimental results showed that the algorithm has both maximum-distortion control and MSE minimization capability.
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Ashlagi, Itai, Brendan Lucier, and Moshe Tennenholtz. "Equilibria of Online Scheduling Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (2013): 67–73. http://dx.doi.org/10.1609/aaai.v27i1.8631.

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We describe a model for competitive online scheduling algorithms. Two servers, each with a single observable queue, compete for customers. Upon arrival, each customer strategically chooses the queue with minimal expected wait time. Each scheduler wishes to maximize its number of customers, and can strategically select which scheduling algorithm, such as First-Come-First-Served (FCFS), to use for its queue. This induces a game played by the servers and the customers. We consider a non-Bayesian setting, where servers and customers play to maximize worst-case payoffs. We show that there is a unique subgame perfect safety-level equilibrium and we describe the associated scheduling algorithm (which is not FCFS). The uniqueness result holds for both randomized and deterministic algorithms, with a different equilibrium algorithm in each case. When the goal of the servers is to minimize competitive ratio, we prove that it is an equilibrium for each server to apply FCFS: each server obtains the optimal competitive ratio of 2.
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26

Yang, Lin, Ali Zeynali, Mohammad H. Hajiesmaili, Ramesh K. Sitaraman, and Don Towsley. "Competitive Algorithms for Online Multidimensional Knapsack Problems." Proceedings of the ACM on Measurement and Analysis of Computing Systems 5, no. 3 (2021): 1–30. http://dx.doi.org/10.1145/3491042.

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In this paper, we study the online multidimensional knapsack problem (called OMdKP) in which there is a knapsack whose capacity is represented in m dimensions, each dimension could have a different capacity. Then, n items with different scalar profit values and m-dimensional weights arrive in an online manner and the goal is to admit or decline items upon their arrival such that the total profit obtained by admitted items is maximized and the capacity of knapsack across all dimensions is respected. This is a natural generalization of the classic single-dimension knapsack problem and finds several relevant applications such as in virtual machine allocation, job scheduling, and all-or-nothing flow maximization over a graph. We develop two algorithms for OMdKP that use linear and exponential reservation functions to make online admission decisions. Our competitive analysis shows that the linear and exponential algorithms achieve the competitive ratios of O(θα ) and O(łogł(θα)), respectively, where α is the ratio between the aggregate knapsack capacity and the minimum capacity over a single dimension and θ is the ratio between the maximum and minimum item unit values. We also characterize a lower bound for the competitive ratio of any online algorithm solving OMdKP and show that the competitive ratio of our algorithm with exponential reservation function matches the lower bound up to a constant factor.
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27

Jafarzadeh, H., N. Moradinasab, and M. Elyasi. "An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem." Engineering, Technology & Applied Science Research 7, no. 6 (2017): 2260–65. http://dx.doi.org/10.48084/etasr.1570.

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The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.
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28

Lykouris, Thodoris, and Sergei Vassilvitskii. "Competitive Caching with Machine Learned Advice." Journal of the ACM 68, no. 4 (2021): 1–25. http://dx.doi.org/10.1145/3447579.

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Traditional online algorithms encapsulate decision making under uncertainty, and give ways to hedge against all possible future events, while guaranteeing a nearly optimal solution, as compared to an offline optimum. On the other hand, machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future, and usually come with strong guarantees on the expected generalization error. In this work, we develop a framework for augmenting online algorithms with a machine learned predictor to achieve competitive ratios that provably improve upon unconditional worst-case lower bounds when the predictor has low error. Our approach treats the predictor as a complete black box and is not dependent on its inner workings or the exact distribution of its errors. We apply this framework to the traditional caching problem—creating an eviction strategy for a cache of size k . We demonstrate that naively following the oracle’s recommendations may lead to very poor performance, even when the average error is quite low. Instead, we show how to modify the Marker algorithm to take into account the predictions and prove that this combined approach achieves a competitive ratio that both (i) decreases as the predictor’s error decreases and (ii) is always capped by O (log k ), which can be achieved without any assistance from the predictor. We complement our results with an empirical evaluation of our algorithm on real-world datasets and show that it performs well empirically even when using simple off-the-shelf predictions.
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29

Mazinani, S. M., J. Chitizadeh, M. H. Yaghmaee, M. T. Honary, and F. Tashtarian. "NEW CLUSTERING SCHEMES FOR WIRELESS SENSOR NETWORKS." IIUM Engineering Journal 11, no. 1 (2010): 51–69. http://dx.doi.org/10.31436/iiumej.v11i1.39.

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In this paper, two clustering algorithms are proposed. In the first one, we investigate a clustering protocol for single hop wireless sensor networks that employs a competitive scheme for cluster head selection. The proposed algorithm is named EECS-M that is a modified version to the well known protocol EECS where some of the nodes become volunteers to be cluster heads with an equal probability. In the competition phase in contrast to EECS using a fixed competition range for any volunteer node, we assign a variable competition range to it that is related to its distance to base station. The volunteer nodes compete in their competition ranges and every one with more residual energy would become cluster head. In the second one, we develop a clustering protocol for single hop wireless sensor networks. In the proposed algorithm some of the nodes become volunteers to be cluster heads. We develop a time based competitive clustering algorithm that the advertising time is based on the volunteer node’s residual energy. We assign to every volunteer node a competition range that may be fixed or variable as a function of distance to BS. The volunteer nodes compete in their competition ranges and every one with more energy would become cluster head. In both proposed algorithms, our objective is to balance the energy consumption of the cluster heads all over the network. Simulation results show the more balanced energy consumption and longer lifetime.
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30

Bauer, V. P., and V. V. Smirnov. "Institutional Features of the Development of Competitive Cryptocurrency." Finance: Theory and Practice 24, no. 5 (2020): 84–99. http://dx.doi.org/10.26794/2587-5671-2020-24-5-84-99.

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The aim of the article is to clarify the basics of the digitalization strategy of the competitive businesses and identify features of the institutional environment that ensure the development of cryptocurrency as a new asset (IT product) of the modern economy, analyze the methods of implementing the cryptocurrency business models. The relevance of the research paper is determined by the need to develop a competitive Russian cryptocurrency (including the cryptoruble) with the growing private, state and cross-national cryptocurrencies. The scientific novelty of the study implies clarifying the informal and formal rules of the institutional environment and related methods ensuring the development of a competitive cryptocurrency. The authors consider the following methods to implement the institutional features of the cryptocurrencies business model development: logic and blockchain algorithm that establish trust and collaboration between cryptocurrency developers; logic and blockchain consensus algorithm ensuring that all the parties of the blockchain network come to a common agreement (consensus); logic and blockchain algorithms that form cryptocurrency transactions and control its turnover by generating blocks of cryptocurrencies, by forming the structure of blocks and transactions of cryptocurrencies, by storing cryptocurrencies’ keys and providing security, by mining (forging) cryptocurrency, etс.The results of the study provide a basis for identifying the institutional features and the corresponding methods providing a competitive cryptocurrency development with a detailed analysis of the blockchain consensus algorithms that ensure the competitiveness of the cryptocurrency. The conclusions show that the most promising are the hybrid consensus algorithms which may include both the logic of two or more known algorithms and the original logic of a new algorithm. The authors recommend defining the logic of the blockchain consensus algorithm as a priority when developing a cryptocurrency to ensure reliability of the transactions in the blockchain network, thus increasing the competitiveness of the cryptocurrency.
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31

Ma, Hang. "A Competitive Analysis of Online Multi-Agent Path Finding." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 234–42. http://dx.doi.org/10.1609/icaps.v31i1.15967.

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We study online Multi-Agent Path Finding (MAPF), where new agents are constantly revealed over time and all agents must find collision-free paths to their given goal locations. We generalize existing complexity results of (offline) MAPF to online MAPF. We classify online MAPF algorithms into different categories based on (1) controllability (the set of agents that they can plan paths for at each time) and (2) rationality (the quality of paths they plan) and study the relationships between them. We perform a competitive analysis for each category of online MAPF algorithms with respect to commonly-used objective functions. We show that a naive algorithm that routes newly-revealed agents one at a time in sequence achieves a competitive ratio that is asymptotically bounded from both below and above by the number of agents with respect to flowtime and makespan. We then show a counter-intuitive result that, if rerouting of previously-revealed agents is not allowed, any rational online MAPF algorithms, including ones that plan optimal paths for all newly-revealed agents, have the same asymptotic competitive ratio as the naive algorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on the competitive ratio of any rational online MAPF algorithms that allow rerouting. The results thus provide theoretical insights into the effectiveness of using MAPF algorithms in an online setting for the first time.
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32

Chen, Lin, Deshi Ye, and Guochuan Zhang. "Approximating the Optimal Algorithm for Online Scheduling Problems via Dynamic Programming." Asia-Pacific Journal of Operational Research 32, no. 01 (2015): 1540011. http://dx.doi.org/10.1142/s0217595915400114.

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Very recently Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] initiate a new systematic way of studying online problems by introducing the competitive ratio approximation scheme (simplified as competitive schemes in this paper), which is a class of algorithms {Aϵ|ϵ > 0} with a competitive ratio at most ρ*(1 + ϵ), where ρ* is the best possible competitive ratio over all online algorithms. Along this line, Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] provide competitive schemes for several online over time scheduling problems like Qm|rj, (pmtn)|∑wjcj, while the running times are polynomial if the number of machines is a constant. In this paper, we consider the classical online scheduling problems, where jobs arrive in a list. We present competitive schemes for Rm‖C max and [Formula: see text], where the running times are polynomial if the number of machines is a constant. Specifically, we are able to derive a competitive scheme for P‖C max which runs in polynomial time even if the number of machines is an input. Our method is novel and more efficient than that of Günther et al. [E. Günther, O. Maurer, N. Megow and A. Wiese (2013). A new approach to online scheduling: Approximating the optimal competitive ratio. In Proc. 24th Annual ACM-SIAM Symp. Discrete Algorithms (SODA).] Indeed, by utilizing the standard rounding technique for the off-line scheduling problems, we reformulate the online scheduling problem into a game on an infinite graph, through which we arrive at the following key point: Assuming that the best competitive ratio is ρ*, for any online algorithm there exists a list of a polynomial number of jobs showing that its competitive ratio is at least ρ* - O(ϵ). Interestingly such a result is achieved via a dynamic programming algorithm. Our framework is also applicable to other online problems.
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33

Bejerano, Y., I. Cidon, and J. Naor. "Efficient handoff rerouting algorithms: a competitive on-line algorithmic approach." IEEE/ACM Transactions on Networking 10, no. 6 (2002): 749–60. http://dx.doi.org/10.1109/tnet.2002.804833.

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Ben-Aroya, Avraham, and Sivan Toledo. "Competitive analysis of flash memory algorithms." ACM Transactions on Algorithms 7, no. 2 (2011): 1–37. http://dx.doi.org/10.1145/1921659.1921669.

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35

Bansal, Nikhil, Niv Buchbinder, and Joseph (Seffi) Naor. "Randomized Competitive Algorithms for Generalized Caching." SIAM Journal on Computing 41, no. 2 (2012): 391–414. http://dx.doi.org/10.1137/090779000.

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36

Fiat, Amos, Dean P. Foster, Howard Karloff, Yuval Rabani, Yiftach Ravid, and Sundar Vishwanathan. "Competitive Algorithms for Layered Graph Traversal." SIAM Journal on Computing 28, no. 2 (1998): 447–62. http://dx.doi.org/10.1137/s0097539795279943.

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Achlioptas, Dimitris, Marek Chrobak, and John Noga. "Competitive analysis of randomized paging algorithms." Theoretical Computer Science 234, no. 1-2 (2000): 203–18. http://dx.doi.org/10.1016/s0304-3975(98)00116-9.

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38

Ahalt, Stanley C., Ashok K. Krishnamurthy, Prakoon Chen, and Douglas E. Melton. "Competitive learning algorithms for vector quantization." Neural Networks 3, no. 3 (1990): 277–90. http://dx.doi.org/10.1016/0893-6080(90)90071-r.

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39

Hopf, Michael, Clemens Thielen, and Oliver Wendt. "Competitive algorithms for multistage online scheduling." European Journal of Operational Research 260, no. 2 (2017): 468–81. http://dx.doi.org/10.1016/j.ejor.2016.12.047.

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40

Bansal, Nikhil, Ho-Leung Chan, and Kirk Pruhs. "Competitive Algorithms for Due Date Scheduling." Algorithmica 59, no. 4 (2009): 569–82. http://dx.doi.org/10.1007/s00453-009-9321-4.

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41

Card, H. C., G. K. Rosendahl, D. K. McNeill, and R. D. McLeod. "Competitive learning algorithms and neurocomputer architecture." IEEE Transactions on Computers 47, no. 8 (1998): 847–58. http://dx.doi.org/10.1109/12.707586.

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42

Bartal, Y., A. Fiat, and Y. Rabani. "Competitive Algorithms for Distributed Data Management." Journal of Computer and System Sciences 51, no. 3 (1995): 341–58. http://dx.doi.org/10.1006/jcss.1995.1073.

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43

Karlin, A. R., M. S. Manasse, L. A. McGeoch, and S. Owicki. "Competitive randomized algorithms for nonuniform problems." Algorithmica 11, no. 6 (1994): 542–71. http://dx.doi.org/10.1007/bf01189993.

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44

Brown, Zach Y., and Alexander MacKay. "Competition in Pricing Algorithms." American Economic Journal: Microeconomics 15, no. 2 (2023): 109–56. http://dx.doi.org/10.1257/mic.20210158.

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We document new facts about pricing technology using high-frequency data, and we examine the implications for competition. Some online retailers employ technology that allows for more frequent price changes and automated responses to price changes by rivals. Motivated by these facts, we consider a model in which firms can differ in pricing frequency and choose pricing algorithms that are a function of rivals’ prices. In competitive (Markov perfect) equilibrium, the introduction of simple pricing algorithms can increase price levels, generate price dispersion, and exacerbate the price effects of mergers. (JEL D21, D22, D43, G34, L13, L81)
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45

Shirali, Nooshin, and Marjan Abdeyazdan. "An Imperialist Competitive Algorithm for Persian Text Segmentation." Ciência e Natura 37 (December 19, 2015): 247. http://dx.doi.org/10.5902/2179460x20780.

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Segmentation has been used in different natural language processing tasks, such as information retrieval and text summarization. In this paper a novel Persian text segmentation algorithm is proposed. Our proposed algorithm applies the imperialist competitive algorithm (ICA) to find the optimal topic boundaries. It is the first time that an evolutionary algorithm applies in Persian text segmentation. The experimental results show that proposed algorithm is more accurate than other Persian text segmentation algorithms.
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46

Siuly, Yan Li, and Peng Wen. "COMPARISONS BETWEEN MOTOR AREA EEG AND ALL-CHANNELS EEG FOR TWO ALGORITHMS IN MOTOR IMAGERY TASK CLASSIFICATION." Biomedical Engineering: Applications, Basis and Communications 26, no. 03 (2014): 1450040. http://dx.doi.org/10.4015/s1016237214500409.

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This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the brain–computer interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classification. The CC-LS-SVM algorithm combines the cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the CC technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG to investigate how well they perform and also to test which area EEG is better for the MI classification. These two algorithms are also compared with some existing methods which reveal their competitive performance during classification. Results on both datasets, IVa and IVb from BCI Competition III, show that the CC-LS-SVM algorithm performs better than the CC-LR algorithm on both the motor area EEG and the all-channels EEG. The results also demonstrate that the CC-LS-SVM algorithm performs much better for the all-channels EEG than for the motor area EEG. Furthermore, the LS-SVM-based approach can correctly identify the discriminative MI tasks, demonstrating the algorithm's superiority in classification performance over some existing methods.
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Xu, Chenyang, and Benjamin Moseley. "Learning-Augmented Algorithms for Online Steiner Tree." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8744–52. http://dx.doi.org/10.1609/aaai.v36i8.20854.

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This paper considers the recently popular beyond-worst-case algorithm analysis model which integrates machine-learned predictions with online algorithm design. We consider the online Steiner tree problem in this model for both directed and undirected graphs. Steiner tree is known to have strong lower bounds in the online setting and any algorithm’s worst-case guarantee is far from desirable. This paper considers algorithms that predict which terminal arrives online. The predictions may be incorrect and the algorithms’ performance is parameterized by the number of incorrectly predicted terminals. These guarantees ensure that algorithms break through the online lower bounds with good predictions and the competitive ratio gracefully degrades as the prediction error grows. We then observe that the theory is predictive of what will occur empirically. We show on graphs where terminals are drawn from a distribution, the new online algorithms have strong performance even with modestly correct predictions.
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Zheng, Rong, Abdelazim G. Hussien, He-Ming Jia, Laith Abualigah, Shuang Wang, and Di Wu. "An Improved Wild Horse Optimizer for Solving Optimization Problems." Mathematics 10, no. 8 (2022): 1311. http://dx.doi.org/10.3390/math10081311.

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Wild horse optimizer (WHO) is a recently proposed metaheuristic algorithm that simulates the social behavior of wild horses in nature. Although WHO shows competitive performance compared to some algorithms, it suffers from low exploitation capability and stagnation in local optima. This paper presents an improved wild horse optimizer (IWHO), which incorporates three improvements to enhance optimizing capability. The main innovation of this paper is to put forward the random running strategy (RRS) and the competition for waterhole mechanism (CWHM). The random running strategy is employed to balance exploration and exploitation, and the competition for waterhole mechanism is proposed to boost exploitation behavior. Moreover, the dynamic inertia weight strategy (DIWS) is utilized to optimize the global solution. The proposed IWHO is evaluated using twenty-three classical benchmark functions, ten CEC 2021 test functions, and five real-world optimization problems. High-dimensional cases (D = 200, 500, 1000) are also tested. Comparing nine well-known algorithms, the experimental results of test functions demonstrate that the IWHO is very competitive in terms of convergence speed, precision, accuracy, and stability. Further, the practical capability of the proposed method is verified by the results of engineering design problems.
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Gouleakis, Themistoklis, Konstantinos Lakis, and Golnoosh Shahkarami. "Learning-Augmented Algorithms for Online TSP on the Line." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11989–96. http://dx.doi.org/10.1609/aaai.v37i10.26414.

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We study the online Traveling Salesman Problem (TSP) on the line augmented with machine-learned predictions. In the classical problem, there is a stream of requests released over time along the real line. The goal is to minimize the makespan of the algorithm. We distinguish between the open variant and the closed one, in which we additionally require the algorithm to return to the origin after serving all requests. The state of the art is a 1.64-competitive algorithm and a 2.04-competitive algorithm for the closed and open variants, respectively. In both cases, a tight lower bound is known. In both variants, our primary prediction model involves predicted positions of the requests. We introduce algorithms that (i) obtain a tight 1.5 competitive ratio for the closed variant and a 1.66 competitive ratio for the open variant in the case of perfect predictions, (ii) are robust against unbounded prediction error, and (iii) are smooth, i.e., their performance degrades gracefully as the prediction error increases. Moreover, we further investigate the learning-augmented setting in the open variant by additionally considering a prediction for the last request served by the optimal offline algorithm. Our algorithm for this enhanced setting obtains a 1.33 competitive ratio with perfect predictions while also being smooth and robust, beating the lower bound of 1.44 we show for our original prediction setting for the open variant. Also, we provide a lower bound of 1.25 for this enhanced setting.
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50

Spiridonova, A., and E. Juchnevicius. "Price Algorithms as a Threat to Competition Under the Conditions of Digital Economy: Approaches to Antimonopoly Legislation of BRICS Countries." BRICS Law Journal 7, no. 2 (2020): 94–117. http://dx.doi.org/10.21684/2412-2343-2020-7-2-94-117.

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The authors examine certain legal problems of antitrust regulation in the digital economy facing the international community, including BRICS member countries. This article focuses on the problems associated with the use of price algorithms by enterprises as a threat factor to competition. The concept of “price algorithm” and the goals of its use by enterprises are analyzed; it is concluded that the use of price algorithms is just one of the tools for conducting economic activity. At the same time, enterprises can pose a threat to competition by using price algorithms as an element of concluding anti-competitive agreements (concerted actions) between enterprises and illegal coordination of their activities. Restriction of competition through the use of price algorithms can harm consumers of goods, works, and services and should be controlled by antitrust authorities. Based on the analysis of the antitrust laws of the BRICS member countries, it is concluded that currently the concept of a “pricing algorithm” is not enshrined in the laws of any of the BRICS member states, however, there are prohibitions on anticompetitive agreements of enterprises and illegal coordination of economic activity. We refute the need to legally enshrine the concept of “price algorithm” in antitrust law. At the same time, it proves that enterprises should be held accountable for the use of the price algorithm as atool to limit competition. The paper proves that within the framework of interstate cooperation of the BRICS countries in the field of competition law, it is necessary to develop common approaches to antitrust regulation in the digital economy, including to ensure auniform approach to regulating and controlling the use of price algorithms by enterprises in the framework of economic activity.
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