Meta-heuristic algorithms

Xem 1-20 trên 22 kết quả Meta-heuristic algorithms
  • The Multi-stripe Travelling Salesman Problem (Ms-TSP) is an extension of the Travelling Salesman Problem (TSP). In the q-stripe TSP with q ≥ 1, the objective function sums the costs for traveling from one vertex to each of the next q vertices along the tour. To solve medium to large-sized instances, a metaheuristic approach is proposed. The proposed method has two main components, which are construction and improvement phases. The construction phase generates an initial solution using the Greedy Randomized Adaptive Search Procedure (GRASP).

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  • This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries.

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  • The proposed approach is based on a modified genetic algorithm, which generates an initial population with heuristic solutions obtained from the well-known (LKH) heuristic algorithm for the TSP together with the solution of a mathematical model for the shortest path problem. In addition, two recombination methods and a mutation operator are considered. Computational experiments on benchmark instances show that the proposed algorithm can obtain high-quality solutions within short computing times.

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  • In this paper electromagnetism (EM) metaheuristic is used for solving the NPhard strong minimum energy topology problem (SMETP). Objective function is adapted to the problem so that it effectively prevents infeasible solutions. Proposed EM algorithm uses efficient local search to speed up overall running time. This approach is tested on two sets of randomly generated symmetric and asymmetric instances.

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  • This paper presents an efficient design of non-uniform cosine modulated filter banks (CMFB) using canonic signed digit (CSD) coefficients. CMFB has got an easy and efficient design approach. Non-uniform decomposition can be easily obtained by merging the appropriate filters of a uniform filter bank. Only the prototype filter needs to be designed and optimized. In this paper, the prototype filter is designed using window method, weighted Chebyshev approximation and weighted constrained least square approximation. The coefficients are quantized into CSD, using a look-up-table.

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  • In this study, after presenting a complete definition of the uncapacitated multiple allocation p-hub center problem (UMApHCP) two well-known metaheuristic algorithms are proposed to solve the problem for small scale and large scale standard data sets.

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  • This paper develops a hybrid metaheuristic algorithm based on the Genetic Algorithm. In the developed algorithm, (1) a heuristic, (2) a local search, and (3) a restart phase is proposed.

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  • This paper aims to develop a mathematical modeling for Home Health Care Routing and Scheduling Problem and to solve it by means of Simulated Annealing (SA) algorithm considering real condition (staff vehicle traveling, conditions of patients and so forth).

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  • In our work, we propose a metaheuristic algorithm which is mainly based on the principles of Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) to solve the problem. The GRASP is used to build an initial solution which is good enough in a construction phase.

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  • Metaheuristics are general frameworks used to build heuristic algorithms for hard optimization problems. In this paper, an overview of promising and widely used metaheuristic methods in solving different variants of Berth Allocation Problem is presented.

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  • Network intrusion detection based on anomaly detection techniques has a significant role in protecting networks and systems against harmful activities. Different metaheuristic techniques have been used for anomaly detector generation. Yet, reported literature has not studied the use of the multi-start metaheuristic method for detector generation. This paper proposes a hybrid approach for anomaly detection in large scale datasets using detectors generated based on multi-start metaheuristic method and genetic algorithms.

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  • This study aims at developing a metaheuristic based image thresholding method via the employments of the HistoryBased Adaptive Differential Evolution with Linear population size reduction algorithm (LSHADE) and the Otsu objective function. The LSHADE algorithm is used to determine an appropriate set of threshold levels that maximizes the separation between clusters of pixels. Experimental results with four applications demonstrate the usefulness of the newly developed tool.

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  • In this article, two new metaheuristic optimization techniques, namely, crow search algorithm and re y algorithm are applied to the well placement optimization problem and their applications to maximize the net pro t value are studied. To study the performance of the re y and crow search algorithm, Eclipse and MATLAB environment are used . The proposed techniques are compared to popular established methods for optimizing well placement. Results show that the re y algorithm is proved to be e cient and e ective compared to other established techniques.

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  • Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems....

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  • Optimization is important in all branches of engineering due to limited resources available. Through optimization, maximum usage of the resource can be achieved. However, global optimization can be difficult due to the requirement of the knowledge of the system behavior under analysis and the possible large solution space. Without this knowledge, the optimization thus obtained may only be a local optimization. Metaheuristic algorithms, on the other hand, are effective in exploring the solution space....

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  • Giải thuật tham lam (tiếng Anh: Greedy algorithm) là một thuật toán giải quyết một bài toán theo kiểu metaheuristic để tìm kiếm lựa chọn tối ưu địa phương ở mỗi bước đi với hy vọng tìm được tối ưu toàn cục. Mời các bạn cùng tham khảo.

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  • Research in the field of vehicle routing is often focused on finding new ideas and concepts in the development of fast and efficient algorithms for an improved solution process. In this first part of the survey, we present an overview of recent literature dealing with adaptive or guided search techniques for problems in vehicle routing.

    pdf29p vinguyentuongdanh 19-12-2018 26 0   Download

  • In this paper the Bandwidth Multicoloring Problem (BMCP) and the Bandwidth Coloring Problem (BCP) are considered. The problems are solved by two genetic algorithms (GAs) which use the integer encoding and standard genetic operators adapted to the problems. In both proposed implementations, all individuals are feasible by default, so search is directed into the promising regions. The first proposed method named GA1 is a constructive metaheuristic that construct solution, while the second named GA2 is an improving metaheuristic used to improve an existing solution.

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  • The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.

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  • In this paper, Resource-Constrained Deliveryman Problem (RCDMP) is introduced. The RCDMP problem deals with finding a tour with minimum waiting time sum so that it consumes not more than

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