Yazar "Mahi, Mostafa" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • Küçük Resim Yok
    Öğe
    A new approach based on particle swarm optimization algorithm for solving data allocation problem
    (ELSEVIER, 2018) Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, Halife
    The effectiveness distributed database systems highly depends on the state of site that its task is to allocate fragments. This allocation purpose is performed for obtaining the minimum execute time and transaction cost of queries. There are some NP-hard problems that Data Allocation Problem (DAP) is one of them and solving this problem by means of enumeration method can be computationally expensive. Recently heuristic algorithms have been used to achieve desirable solutions. Due to fewer control parameters, robustness, speed convergence characteristics and easy adaptation to the problem, this paper propose a novel method based on Particle Swarm Optimization (PSO) algorithm which is suitable to minimize the total transmission cost for both the each site - fragment dependency and the each inter - fragment dependency. The core of the study is to solve DAP by utilizing and adaptation PSO algorithm, PSO-DAP for short. Allocation of fragments to the site has been done with PSO algorithm and its performance has been evaluated on 20 different test problems and compared with the state-of-art algorithms. Experimental results and comparisons demonstrate that proposed method generates better quality solutions in terms of execution time and total cost than compared state-of-art algorithms. (C) 2017 Elsevier B.V. All rights reserved.
  • Küçük Resim Yok
    Öğe
    A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem
    (ELSEVIER SCIENCE BV, 2015) Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, Halife
    The Traveling Salesman Problem (TSP) is one of the standard test problems used in performance analysis of discrete optimization algorithms. The Ant Colony Optimization (ACO) algorithm appears among heuristic algorithms used for solving discrete optimization problems. In this study, a new hybrid method is proposed to optimize parameters that affect performance of the ACO algorithm using Particle Swarm Optimization (PSO). In addition, 3-Opt heuristic method is added to proposed method in order to improve local solutions. The PSO algorithm is used for detecting optimum values of parameters alpha and beta which are used for city selection operations in the ACO algorithm and determines significance of inter-city pheromone and distances. The 3-Opt algorithm is used for the purpose of improving city selection operations, which could not be improved due to falling in local minimums by the ACO algorithm. The performance of proposed hybrid method is investigated on ten different benchmark problems taken from literature and it is compared to the performance of some well-known algorithms. Experimental results show that the performance of proposed method by using fewer ants than the number of cities for the TSPs is better than the performance of compared methods in most cases in terms of solution quality and robustness. (C) 2015 Elsevier B.V. All rights reserved.
  • Küçük Resim Yok
    Öğe
    A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem
    (SPRINGER, 2018) Gulcu, Saban; Mahi, Mostafa; Baykan, Omer Kaan; Kodaz, Halife
    This article presented a parallel cooperative hybrid algorithm for solving traveling salesman problem. Although heuristic approaches and hybrid methods obtain good results in solving the TSP, they cannot successfully avoid getting stuck to local optima. Furthermore, their processing duration unluckily takes a long time. To overcome these deficiencies, we propose the parallel cooperative hybrid algorithm (PACO-3Opt) based on ant colony optimization. This method uses the 3-Opt algorithm to avoid local minima. PACO-3Opt has multiple colonies and a master-slave paradigm. Each colony runs ACO to generate the solutions. After a predefined number of iterations, each colony primarily runs 3-Opt to improve the solutions and then shares the best tour with other colonies. This process continues until the termination criterion meets. Thus, it can reach the global optimum. PACO-3Opt was compared with previous algorithms in the literature. The experimental results show that PACO-3Opt is more efficient and reliable than the other algorithms.