Well known optimization algorithms

The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes).

13p vilarryellison 29-10-2021 12 2   Download

The proposed algorithm is tested on 76 unconstrained benchmark functions with different characteristics and the performance of the algorithm is compared with that of other well known optimization algorithms. A statistical test is also performed to investigate the results obtained using different algorithms. The results have proved the effectiveness of the proposed elitist TLBO algorithm.

22p toritori 11-05-2020 13 1   Download

The results obtained showed that the proposed algorithm was capable of finding the optimal solution in most cases when considering a time limit of 500 seconds. The methodology is also applied to solve a real-life instance that arises in the transportation system in Colombia (2 depots and 719 services), resulting in a decrease of the required fleet size and a balanced allocation of services, thus reducing deadhead trips.

14p toritori 11-05-2020 15 0   Download

The purpose of this paper is to present a new and an alternative differential evolution (ADE) algorithm for solving unconstrained global optimization problems. In the new algorithm, a new directed mutation rule is introduced based on the weighted difference vector between the best and the worst individuals of a particular generation. The mutation rule is combined with the basic mutation strategy through a linear decreasing probability rule. This modification is shown to enhance the local search ability of the basic DE and to increase the convergence rate.

17p kethamoi1 17-11-2019 20 1   Download

The optimization of a novel programmable data-flow crypto processor dedicated to security applications is considered. An architecture based on assigning basic functional units to four synchronous regions was proposed in a previous work. In this paper, the problem of selecting the number of synchronous regions and the distribution of functional units among these regions is formulated as a combinatorial multi-objective optimization problem. The objective functions are chosen as: the implementation area, the execution delay, and the consumed energy when running the well-known AES algorithm.

12p trinhthamhodang1 14-11-2019 25 2   Download

This paper focuses on designing an application of service discovery on the basis of demand analysis, object-oriented service oriented routing. With the evolving variety of Web services today, it is essential to take advantages of these services to perform new complex tasks. Based on the researches of some of the world's researchers by the well-known articles [2], the author of this paper proposes a technique that uses graph theory combined with ACO optimization algorithm, to create the optimal route to support the service discovery problem.

13p visumika2711 17-07-2019 17 2   Download

In this paper, we propose a new genetic encoding for well known Quadratic Assignment Problem (QAP). The new encoding schemes are implemented with appropriate objective function and modified genetic operators. The numerical experiments were carried out on the standard QAPLIB data sets known from the literature. The presented results show that in all cases proposed genetic algorithm reached known optimal solutions in reasonable time.

14p vinguyentuongdanh 19-12-2018 38 1   Download

In this paper we consider the well known p-median problem. We introduce a new large neighborhood based on ideas of S.Lin and B.W. Kernighan for the graph partition problem. We study the behavior of the local improvement and Ant Colony algorithms with new neighborhood. Computational experiments show that the local improvement algorithm with the neighborhood is fast and finds feasible solutions with small relative error. The Ant Colony algorithm with new neighborhood as a rule finds an optimal solution for computationally difficult test instances.

11p vinguyentuongdanh 20-12-2018 31 2   Download

This paper discusses a decision-tree approach to the problem of assigning probabilities to words following a given text. In contrast with previous decision-tree language model attempts, an algorithm for selecting nearly optimal questions is considered. The model is to be tested on a standard task, The Wall Street Journal, allowing a fair comparison with the well-known trigram model.

4p bunrieu_1 18-04-2013 57 4   Download

This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics community: Maximum Entropy (ME) estimation with L2 regularization, the Averaged Perceptron (AP), and Boosting. We also investigate ME estimation with L1 regularization using a novel optimization algorithm, and BLasso, which is a version of Boosting with Lasso (L1) regularization. We first investigate all of our estimators on two re-ranking tasks: a parse selection task and a language model (LM) adaptation task. ...

8p hongvang_1 16-04-2013 53 3   Download

In this paper we consider a combinatorial optimization problem that is similar to the bottleneck traveling salesman problem. We show that an optimal tour for this problem is pyramidal tour (1, 3, 5,…, n,…, 6, 4, 2) or consists of some pyramidal subtours. The above 7methods can be extended to complete bipartite graphs. 1. Problem statement It is well-known that the traveling salesman problem (TSP) is strongly NP-hard (cf. [1], p. 353). But for some special cases of the TSP can be solvable in polynomial time. ...

5p tuanlocmuido 19-12-2012 62 3   Download

This book presents state of the art contributes to Simulated Annealing (SA) that is a well-known probabilistic meta-heuristic. It is used to solve discrete and continuous optimization problems. The significant advantage of SA over other solution methods has made it a practical solution method for solving complex optimization problems. Book is consisted of 13 chapters, classified in single and multiple objectives applications and it provides the reader with the knowledge of SA and several applications.

0p cucdai_1 19-10-2012 45 6   Download