Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing – tabu search algorithm to solve the symmetrical traveling salesman problem

2016 ◽  
Vol 49 ◽  
pp. 937-952 ◽  
Author(s):  
Yu Lin ◽  
Zheyong Bian ◽  
Xiang Liu
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Traveling Salesman ◽  
Tabu Search Algorithm ◽  
Neighborhood Structure ◽  
Hybrid Simulated Annealing

Microcanonical Optimization Applied to the Traveling Salesman Problem

1998 ◽  
Vol 09 (01) ◽  
pp. 133-146 ◽  
Author(s):  
Alexandre Linhares ◽  
José R. A. Torreão
Keyword(s):  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
New Physics ◽  
Traveling Salesman ◽  
Tabu Search Algorithm ◽  
Combinatorial Optimization Problems ◽  
Microcanonical Annealing ◽  
The Traveling Salesman Problem

Optimization strategies based on simulated annealing and its variants have been extensively applied to the traveling salesman problem (TSP). Recently, there has appeared a new physics-based metaheuristic, called the microcanonical optimization algorithm (μO), which does not resort to annealing, and which has proven a superior alternative to the annealing procedures in various applications. Here we present the first performance evaluation of μO as applied to the TSP. When compared to three annealing strategies (simulated annealing, microcanonical annealing and Tsallis annealing), and to a tabu search algorithm, the microcanonical optimization has yielded the best overall results for several instances of the euclidean TSP. This confirms μO as a competitive approach for the solution of general combinatorial optimization problems.

Study on Tabu Search Algorithm for Multi-Cargo Loading Problem

2012 ◽  
Vol 178-181 ◽  
pp. 1802-1805
Author(s):  
Chun Yu Ren
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Initial Solution ◽  
Neighborhood Search ◽  
Tabu Search Algorithm ◽  
Search Efficiency ◽  
Speed Of Convergence ◽  
Neighborhood Structure ◽  
Loading Problem

The paper is focused on the Multi-cargo Loading Problem (MCLP). Tabu search algorithm is an algorithm based on neighborhood search. According to the features of the problem, the essay centered the construct initial solution to construct neighborhood structure. For the operation, 1-move and 2-opt were applied, it can also fasten the speed of convergence, and boost the search efficiency. Finally, the good performance of this algorithm can be proved by experiment calculation and concrete examples.

A cutting sequence optimization method based on tabu search algorithm for complex parts machining

2018 ◽  
Vol 233 (3) ◽  
pp. 745-755 ◽  
Author(s):  
Chuanwei Zhang ◽  
Feiyan Han ◽  
Wu Zhang
Keyword(s):  
Tabu Search ◽  
Tool Path ◽  
Search Algorithm ◽  
Optimization Method ◽  
Traveling Salesman ◽  
Tabu Search Algorithm ◽  
Rough Machining ◽  
Sequence Optimization ◽  
Cutting Sequence ◽  
Complex Parts

Defining the cutting sequence of each cutter scientifically in the process of removing the allowance has an important influence on the machining efficiency for complex parts, which have multiple machining features. In order to satisfy the needs of high efficiency for rough machining, after determining the tool path of the machining region, a cutting sequence optimization method based on the tabu search algorithm is presented to define the cutting order in rough machining of complex parts. First, a cutting sequence optimization mathematical model is established, which relates to the shortest total length of the tool path. Second, through the problem analysis, the cutting sequence optimization model is converted into an open and constrained traveling salesman problem. And then, the optimization model is solved by dealing with an open and constrained traveling salesman problem using the tabu search algorithm. Finally, the optimal cutting sequence of machining a casing part is calculated, and a simulation and experiment are carried out. The result shows that the optimization approach presented in this article can optimize the cutting sequence and cutter position of advance and retract. Compared with the non-optimized cutting sequence method, the total length of tool path is reduced by 16.7%, the cutter lifting times are reduced to 26, and the efficiency is increased by 21.62%.

Study on Mechanical Bearing Capacity with Tabu Search Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

2013 ◽  
Vol 345 ◽  
pp. 3-6
Author(s):  
Chun Yu Ren
Keyword(s):  
Tabu Search ◽  
Bearing Capacity ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Tabu Search Algorithm ◽  
Search Efficiency ◽  
Neighborhood Structure ◽  
Loading Problem ◽  
Annealing Algorithm

This paper studies multi-vehicle and multi-cargo loading problem under the limited mechanical bearing capacity. Tabu search algorithm is an algorithm based on neighborhood search. According to the features of the problem, the essay centered the construct initial solution to construct neighborhood structure. Firstly, for the operation, 1-move and 2-opt were applied. Secondly, through utilizing Boltzmann mechanism of simulated annealing algorithm, it can also fasten the speed of convergence, and boost the search efficiency. Finally, the good performance of this algorithm can be proved by experiment calculation and the mechanical engineering examples.

scholarly journals Optimal Network Destruction Strategy with Heterogeneous Cost under Cascading Failure Model

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Fang Yang ◽  
Tao Ma ◽  
Tao Wu ◽  
Hong Shan ◽  
Chunsheng Liu
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Network Performance ◽  
Search Algorithm ◽  
Cascading Failure ◽  
Tabu Search Algorithm ◽  
Failure Model ◽  
The Cost ◽  
Node Removal ◽  
The Impact

By studying an attacker’s strategy, defenders can better understand their own weaknesses and prepare a response to potential threats in advance. Recent studies on complex networks using the cascading failure model have revealed that removing critical nodes in the network will seriously threaten network security due to the cascading effect. The conventional strategy is to maximize the declining network performance by removing as few nodes as possible, but this ignores the difference in node removal costs and the impact of the removal order on network performance. Having considered all factors, including the cost heterogeneity and removal order of nodes, this paper proposes a destruction strategy that maximizes the declining network performance under a constraint based on the removal costs. First, we propose a heterogeneous cost model to describe the removal cost of each node. A hybrid directed simulated annealing and tabu search algorithm is then devised to determine the optimal sequence of nodes for removal. To speed up the search efficiency of the simulated annealing algorithm, this paper proposes an innovative directed disturbance strategy based on the average cost. After each annealing iteration, the tabu search algorithm is used to adjust the order of node removal. Finally, the effectiveness and convergence of the proposed algorithm are evaluated through extensive experiments on simulated and real networks. As the cost heterogeneity increases, we find that the impact of low-cost nodes on network security becomes larger.

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