8 results
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2. An Optimized Discrete Dragonfly Algorithm Tackling the Low Exploitation Problem for Solving TSP.
- Author
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Emambocus, Bibi Aamirah Shafaa, Jasser, Muhammed Basheer, Amphawan, Angela, and Mohamed, Ali Wagdy
- Subjects
SWARM intelligence ,TRAVELING salesman problem ,ALGORITHMS ,METAHEURISTIC algorithms ,PROBLEM solving ,DETERMINISTIC algorithms - Abstract
Optimization problems are prevalent in almost all areas and hence optimization algorithms are crucial for a myriad of real-world applications. Deterministic optimization algorithms tend to be computationally costly and time-consuming. Hence, heuristic and metaheuristic algorithms are more favoured as they provide near-optimal solutions in an acceptable amount of time. Swarm intelligence algorithms are being increasingly used for optimization problems owing to their simplicity and good performance. The Dragonfly Algorithm (DA) is one which is inspired by the swarming behaviours of dragonflies, and it has been proven to have a superior performance than other algorithms in multiple applications. Hence, it is worth considering its application to the traveling salesman problem which is a predominant discrete optimization problem. The original DA is only suitable for solving continuous optimization problems and, although there is a binary version of the algorithm, it is not easily adapted for solving discrete optimization problems like TSP. We have previously proposed a discrete adapted DA algorithm suitable for TSP. However, it has low effectiveness, and it has not been used for large TSP problems. In this paper, we propose an optimized discrete adapted DA by using the steepest ascent hill climbing algorithm as a local search. The algorithm is applied to a TSP problem modelling a package delivery system in the Kuala Lumpur area and to benchmark TSP problems, and it is found to have a higher effectiveness than the discrete adapted DA and some other swarm intelligence algorithms. It also has a higher efficiency than the discrete adapted DA. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. A Hybrid Stochastic Deterministic Algorithm for Solving Unconstrained Optimization Problems.
- Author
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Alshamrani, Ahmad M., Alrasheedi, Adel Fahad, Alnowibet, Khalid Abdulaziz, Mahdi, Salem, and Mohamed, Ali Wagdy
- Subjects
CONJUGATE gradient methods ,DETERMINISTIC algorithms ,FINITE differences ,SEARCH algorithms ,CONVEX functions - Abstract
In this paper, a new deterministic method is proposed. This method depends on presenting (suggesting) some modifications to existing parameters of some conjugate gradient methods. The parameters of our suggested method contain a mix of deterministic and stochastic parameters. The proposed method is added to a line search algorithm to make it a globally convergent method. The convergence analysis of the method is established. The gradient vector is estimated by a finite difference approximation approach, and a new step-size h of this approach is generated randomly. In addition, a set of stochastic parameter formulas is constructed from which some solutions are generated randomly for an unconstrained problem. This stochastic technique is hybridized with the new deterministic method to obtain a new hybrid algorithm that finds an approximate solution for the global minimization problem. The performance of the suggested hybrid algorithm is tested in two sets of benchmark optimization test problems containing convex and non-convex functions. Comprehensive comparisons versus four other hybrid algorithms are listed in this study. The performance profiles are utilized to evaluate and compare the performance of the five hybrid algorithms. The numerical results show that our proposed hybrid algorithm is promising and competitive for finding the global optimum point. The comparison results between the performance of our suggested hybrid algorithm and the other four hybrid algorithms indicate that the proposed algorithm is competitive with, and in all cases superior to, the four algorithms in terms of the efficiency, reliability, and effectiveness for finding the global minimizers of non-convex functions. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. A Formulation for the Stochastic Multi-Mode Resource-Constrained Project Scheduling Problem Solved with a Multi-Start Iterated Local Search Metaheuristic.
- Author
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Ramos, Alfredo S., Miranda-Gonzalez, Pablo A., Nucamendi-Guillén, Samuel, and Olivares-Benitez, Elias
- Subjects
METAHEURISTIC algorithms ,PROBLEM solving ,NP-hard problems ,DETERMINISTIC algorithms ,SCHEDULING ,MATHEMATICAL models - Abstract
This research introduces a stochastic version of the multi-mode resource-constrained project scheduling problem (MRCPSP) and its mathematical model. In addition, an efficient multi-start iterated local search (MS-ILS) algorithm, capable of solving the deterministic MRCPSP, is adapted to deal with the proposed stochastic version of the problem. For its deterministic version, the MRCPSP is an NP-hard optimization problem that has been widely studied. The problem deals with a trade-off between the amount of resources that each project activity requires and its duration. In the case of the proposed stochastic formulation, the execution times of the activities are uncertain. Benchmark instances of projects with 10, 20, 30, and 50 activities from well-known public libraries were adapted to create test instances. The adapted algorithm proved to be capable and efficient for solving the proposed stochastic problem. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
5. Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning.
- Author
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Nicola, Marcel and Nicola, Claudiu-Ionel
- Subjects
REINFORCEMENT learning ,PERMANENT magnet motors ,PARTICLE swarm optimization ,SIMULATED annealing ,COMPUTATIONAL intelligence ,GENETIC algorithms ,DETERMINISTIC algorithms - Abstract
Starting from the nonlinear operating equations of the permanent magnet synchronous motor (PMSM) and from the global strategy of the field-oriented control (FOC), this article compares the linear and nonlinear control of a PMSM. It presents the linear quadratic regulator (LQR) algorithm as a linear control algorithm, in addition to that obtained through feedback linearization (FL). Naturally, the nonlinear approach through the Lyapunov and Hamiltonian functions leads to results that are superior to those of the linear algorithms. With the particle swarm optimization (PSO), simulated annealing (SA), genetic algorithm (GA), and gray wolf Optimization (GWO) computational intelligence (CI) algorithms, the performance of the PMSM–control system (CS) was optimized by obtaining parameter vectors from the control algorithms by optimizing specific performance indices. Superior performance of the PMSM–CS was also obtained by using reinforcement learning (RL) algorithms, which provided correction command signals (CCSs) after the training stages. Starting from the PMSM–CS performance that was obtained for a benchmark, there were four types of linear and nonlinear control algorithms for the control of a PMSM, together with the means of improving the PMSM–CS performance by using CI algorithms and RL–twin delayed deep deterministic policy gradient (TD3) agent algorithms. The article also presents experimental results that confirm the superiority of PMSM–CS–CI over classical PI-type controllers. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. On Coevaluation Behavior and Equivalence.
- Author
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Zúñiga, Angel and Bel-Enguix, Gemma
- Subjects
SEMANTICS ,FINITE, The ,DETERMINISTIC algorithms - Abstract
Coevaluation, the coinductive interpretation of standard big-step evaluation rules, is a concise form of semantics, with the same number of rules as in evaluation, which intends to simultaneously describe finite and infinite computations. However, it is known that it is only able to express an infinite computations subset, and, to date, it remains unknown exactly what this subset is. More precisely, coevaluation behavior has several unusual features: there are terms for which evaluation is infinite but that do not coevaluate, it is not deterministic in the sense that there are terms which coevaluate to any value v, and there are terms for which evaluation is infinite but that coevaluate to only one value. In this work, we describe the infinite computations subset which is able to be expressed by coevaluation. More importantly, we introduce a coevaluation extension that is well-behaved in the sense that the finite computations coevaluate exactly as in evaluation and the infinite computations coevaluate exactly as in divergence. Consequently, no unusual features are presented; in particular, this extension captures all infinite computations (not only a subset of them). In addition, as a consequence of thiswell-behavior, we present the expected equivalence between (extended) coevaluation and evaluation union divergence. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
7. Quantum Algorithms for Some Strings Problems Based on Quantum String Comparator †.
- Author
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Khadiev, Kamil, Ilikaev, Artem, and Vihrovs, Jevgenijs
- Subjects
ALGORITHMS ,COMPARATOR circuits ,QUANTUM communication ,DETERMINISTIC algorithms ,PROBLEM solving ,QUANTUM computing - Abstract
We study algorithms for solving three problems on strings. These are sorting of n strings of length k, "the Most Frequent String Search Problem", and "searching intersection of two sequences of strings". We construct quantum algorithms that are faster than classical (randomized or deterministic) counterparts for each of these problems. The quantum algorithms are based on the quantum procedure for comparing two strings of length k in O (k) queries. The first problem is sorting n strings of length k. We show that classical complexity of the problem is Θ (n k) for constant size alphabet, but our quantum algorithm has O ˜ (n k) complexity. The second one is searching the most frequent string among n strings of length k. We show that the classical complexity of the problem is Θ (n k) , but our quantum algorithm has O ˜ (n k) complexity. The third problem is searching for an intersection of two sequences of strings. All strings have the same length k. The size of the first set is n, and the size of the second set is m. We show that the classical complexity of the problem is Θ ((n + m) k) , but our quantum algorithm has O ˜ ((n + m) k) complexity. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
8. Twisted Edwards Elliptic Curves for Zero-Knowledge Circuits.
- Author
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Bellés-Muñoz, Marta, Whitehat, Barry, Baylina, Jordi, Daza, Vanesa, and Muñoz-Tapia, Jose Luis
- Subjects
ELLIPTIC curves ,DATA encryption ,DETERMINISTIC algorithms ,DIGITAL signatures ,PUBLIC key cryptography ,BLOCKCHAINS ,CRYPTOGRAPHY - Abstract
Circuit-based zero-knowledge proofs have arose as a solution to the implementation of privacy in blockchain applications, and to current scalability problems that blockchains suffer from. The most efficient circuit-based zero-knowledge proofs use a pairing-friendly elliptic curve to generate and validate proofs. In particular, the circuits are built connecting wires that carry elements from a large prime field, whose order is determined by the number of elements of the pairing-friendly elliptic curve. In this context, it is important to generate an inner curve using this field, because it allows to create circuits that can verify public-key cryptography primitives, such as digital signatures and encryption schemes. To this purpose, in this article, we present a deterministic algorithm for generating twisted Edwards elliptic curves defined over a given prime field. We also provide an algorithm for checking the resilience of this type of curve against most common security attacks. Additionally, we use our algorithms to generate Baby Jubjub, a curve that can be used to implement elliptic-curve cryptography in circuits that can be validated in the Ethereum blockchain. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
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