Search

This study focuses on the load balancing of the transactions in the blockchain. The problem is how to assign these transactions to the blocks. The objective is to guarantee a load balancing of the workload in the time of blocks. The proposed problem is an NP-hard one. To face the hardness of the studied problem, the challenge is to develop algorithms that solve the problem approximately. Finding an approximate solution is a real challenge. In this paper, nine algorithms are proposed. These algorithms are based on the dispatching-rules method, randomization approach, clustering algorithms, and iterative method. The proposed algorithms return approximate solutions in a remarkable time. In addition, in this paper, a novel architecture composed of blocks is proposed. This architecture adds the component "Balancer". This component is responsible to call the best-proposed algorithm and solve the scheduling problem in a polynomial time. In addition, the proposed work helps users to solve the problem of big data concurrency. These algorithms are coded and compared. The performance of these algorithms is tested over three classes of instances. These classes are generated based on uniform distribution. The total number of instances tested is 1350. The average gap, execution time, and the percentage of the best-reached value are used as metrics to measure the performance of the proposed algorithms. Experimental results show the performance of these algorithms and a comparison between them is discussed. The experimental results show that the best algorithm is best-mi-transactions iterative multi-choice with 93.9% in an average running time of 0.003 s. [ABSTRACT FROM AUTHOR]

Team Formation (TF) is considered one of the most significant problems in computer science and optimization. TF is defined as forming the best team of experts in a social network to complete a task with least cost. Many real-world problems, such as task assignment, vehicle routing, nurse scheduling, resource allocation, and airline crew scheduling, are based on the TF problem. TF has been shown to be a Nondeterministic Polynomial time (NP) problem, and high-dimensional problem with several local optima that can be solved using efficient approximation algorithms. This paper proposes two improved swarm-based algorithms for solving team formation problem. The first algorithm, entitled Hybrid Heap-Based Optimizer with Simulated Annealing Algorithm (HBOSA), uses a single crossover operator to improve the performance of a standard heap-based optimizer (HBO) algorithm. It also employs the simulated annealing (SA) approach to improve model convergence and avoid local minima trapping. The second algorithm is the Chaotic Heap-based Optimizer Algorithm (CHBO). CHBO aids in the discovery of new solutions in the search space by directing particles to different regions of the search space. During HBO's optimization process, a logistic chaotic map is used. The performance of the two proposed algorithms (HBOSA) and (CHBO) is evaluated using thirteen benchmark functions and tested in solving the TF problem with varying number of experts and skills. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the Heap-Based Optimizer (HBO), Developed Simulated Annealing (DSA), Particle SwarmOptimization (PSO), GreyWolfOptimization (GWO), and Genetic Algorithm (GA). Finally, the proposed algorithms were applied to a real-world benchmark dataset known as the Internet Movie Database (IMDB). The simulation results revealed that the proposed algorithms outperformed the compared algorithms in terms of efficiency and performance, with fast convergence to the global minimum. [ABSTRACT FROM AUTHOR]

Two-level priority based assignment problem (2PBAP) is an important optimization problem due to the timely delivery requirement in project management. Only four methods with deficiency are available in the literature to solve 2PBAP. In this paper, 2PBAP is reduced to a series of finding the feasible flow in a network with lower and upper arc capacities, and consequently two iterative algorithms are developed to solve 2PBAP. It is proved that both iterative algorithms find the optimal solution to 2PBAP in a strongly polynomial time. Owing to having fully utilized the network flow structural characteristic inherent to 2PBAP, both iterative algorithms have the advantages such as easy implementation on computer, no memory overflow in implementation on computer for large scale instances, high computational efficiency, and easy extension to the priority based assignment problem with priority level more than two, and successfully overcome the deficiency of existing approaches. Computational experiments validate that in terms of computational time, one of our proposed iterative algorithms has higher efficiency than the other, and rivals the existing best approach. [ABSTRACT FROM AUTHOR]

The Exact Satisfiability problem asks if we can find a satisfying assignment to each clause such that exactly one literal in each clause is assigned 1, while the rest are all assigned 0. We can generalise this problem further by defining that a C j clause is solved iff exactly j of the literals in the clause are 1 and all others are 0. We now introduce the family of Generalised Exact Satisfiability problems called G i XSAT as the problem to check whether a given instance consisting of C j clauses with j ∈ { 0 , 1 , ... , i } for each clause has a satisfying assignment. In this paper, we present faster exact polynomial space algorithms, using a nonstandard measure, to solve G i XSAT, for i ∈ { 2 , 3 , 4 } , in O (1.3674 n) time, O (1.5687 n) time and O (1.6545 n) time, respectively, using polynomial space, where n is the number of variables. This improves the current state of the art for polynomial space algorithms from O (1.4203 n) time for G2XSAT by Zhou, Jiang and Yin and from O (1.6202 n) time for G3XSAT by Dahllöf and from O (1.6844 n) time for G4XSAT which was by Dahllöf as well. In addition, we present faster exact algorithms solving G2XSAT, G3XSAT and G4XSAT in O (1.3188 n) time, O (1.3407 n) time and O (1.3536 n) time respectively at the expense of using exponential space. [ABSTRACT FROM AUTHOR]

We revisit the maximum satisfiability problem (Max-SAT) in the data stream model. In this problem, the stream consists of m clauses that are disjunctions of literals drawn from n Boolean variables. The objective is to find an assignment to the variables that maximizes the number of satisfied clauses. Chou et al. (FOCS 2020) showed that Ω (n) space is necessary to yield a 2 / 2 + ε approximation of the optimum value; they also presented an algorithm that yields a 2 / 2 − ε approximation of the optimum value using O (ε − 2 log ⁡ n) space. In this paper, we not only focus on approximating the optimum value, but also on obtaining the corresponding Boolean assignment using sublinear o (m n) space. We present randomized single-pass algorithms that w.h.p.1 yield: • A 1 − ε approximation using O ˜ (n / ε 3) space and exponential post-processing time • A 3 / 4 − ε approximation using O ˜ (n / ε) space and polynomial post-processing time. Our ideas also extend to dynamic streams. However, we show that the streaming k -SAT problem, which asks whether one can satisfy all size- k input clauses, must use Ω (n k) space. We also consider the related minimum satisfiability problem (Min-SAT), introduced by Kohli et al. (SIAM J. Discrete Math. 1994), that asks to find an assignment that minimizes the number of satisfied clauses. For this problem, we give a O ˜ (n 2 / ε 2) space algorithm, which is sublinear when m = ω (n) , that yields an α + ε approximation where α is the approximation guarantee of the offline algorithm. If each variable appears in at most f clauses, we show that a 2 f n approximation using O ˜ (n) space is possible. Finally, for the Max-AND-SAT problem where clauses are conjunctions of literals, we show that any single-pass algorithm that approximates the optimal value up to a factor better than 1/2 with success probability at least 2/3 must use Ω (m n) space. [ABSTRACT FROM AUTHOR]

When multiple heterogeneous unmanned aerial vehicles (UAVs) provide service for multiple users in sensor networks, users' diverse priorities and corresponding priority-related satisfaction are rarely concerned in traditional task assignment algorithms. A priority-driven user satisfaction model is proposed, in which a piecewise function considering soft time window and users' different priority levels is designed to describe the relationship between user priority and user satisfaction. On this basis, the multi-UAV task assignment problem is formulated as a combinatorial optimization problem with multiple constraints, where the objective is maximizing the priority-weighted satisfaction of users while minimizing the total energy consumption of UAVs. A multipopulation-based cooperation genetic algorithm (MPCGA) by adapting the idea of "exploration-exploitation" into traditional genetic algorithms (GAs) is proposed, which can solve the task assignment problem in polynomial time. Simulation results show that compared with the algorithm without considering users' priority-based satisfaction, users' weighted satisfaction can be improved by about 47% based on our algorithm in situations where users' information acquisition is tight time-window constraints. In comparison, UAVs' energy consumption only increased by about 6%. Besides, compared with traditional GA, our proposed algorithm can also improve users' weighted satisfaction by about 5% with almost the same energy consumption of UAVs. [ABSTRACT FROM AUTHOR]