SIMULATED ANNEALING AND TABU SEARCH ALGORITHMS FOR MULTIWAY GRAPH PARTITION

1992 ◽  
Vol 02 (02) ◽  
pp. 159-185 ◽  
Author(s):  
L. TAO ◽  
Y.C. ZHAO ◽  
K. THULASIRAMAN ◽  
M.N.S. SWAMY
Keyword(s):  
Experimental Study ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Time Frame ◽  
Search Algorithms ◽  
Graph Partition ◽  
Solution Quality ◽  
Running Time ◽  
New Algorithms

For a given graph G with vertex and edge weights, we partition the vertices into subsets to minimize the total weights for edges crossing the subsets (weighted cut size) under the constraint that the vertex weights are evenly distributed among the subsets. We propose two new effective graph partition algorithms based on simulated annealing and tabu search, and compare their performance with that of the LPK algorithm reported in Ref. 12. Extensive experimental study shows that both of our new algorithms produce significantly better solutions than the LPK algorithm (maximal and minimal improvements on average weighted cut size are roughly 51.8% and 10.5% respectively) with longer running time, and this advantage in solution quality would not change even if we run the LPK algorithm repeatedly with random initial solutions in the same time frame as required by our algorithms.

Concept of Neighborhood Degree and its Application to Switching Plural Optimization Methods in Scheduling

2010 ◽  
Vol 14 (1) ◽  
pp. 21-27
Author(s):  
Fangyan Dong ◽  
◽  
Kewei Chen ◽  
Kaoru Hirota ◽  
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Vehicle Routing ◽  
Optimization Methods ◽  
Optimization Process ◽  
Scheduling Problems ◽  
Solution Quality ◽  
Practical Applications ◽  
Benchmark Data

A concept of neighborhood degree is proposed to evaluate the quality of solutions to scheduling problems such as vehicle routing, scheduling, and dispatching problems. It is possible to apply it to the optimization process of scheduling problems in order to switch between various optimization methods by considering convergence speed and solution quality. In the experiments on TSP benchmark data, two optimization methods, i.e., tabu search and simulated annealing, are switched effectively by observing the variation of the neighborhood degree. Directions for Practical applications are also mentioned.

APPLYING METAHEURISTICS TO FEEDER BUS NETWORK DESIGN PROBLEM

2004 ◽  
Vol 21 (04) ◽  
pp. 543-560 ◽  
Author(s):  
S. N. KUAN ◽  
H. L. ONG ◽  
K. M. NG
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Network Design ◽  
Computational Efficiency ◽  
Design Problem ◽  
Network Design Problem ◽  
Test Problems ◽  
Solution Quality ◽  
Feeder Bus ◽  
Bus Network

This paper proposes the design and analysis of two metaheuristics, simulated annealing (SA) and tabu search (TS), for solving the feeder bus network design problem. The results are compared to those published in the literature. A comparative study is also carried out on several test problems generated at random to evaluate the performance of these heuristics in terms of their computational efficiency and solution quality. Computational experiments have shown that TS is a more effective metaheuristic in solving the problem than SA.

Does Complex Metaheuristic Out-Perform Simple Hill-Climbing for Optimization Problems? A Simulation Evaluation

2013 ◽  
Vol 748 ◽  
pp. 666-669 ◽  
Author(s):  
Xing Wen Zhang
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Random Noise ◽  
Computation Time ◽  
Hill Climbing ◽  
Supply Chain Optimization ◽  
Solution Quality ◽  
Metaheuristic Methods

In this paper we compare the performance of metaheuristic methods, namely simulated annealing and Tabu Search, against simple hill climbing heuristic on a supply chain optimization problem. The benchmark problem we consider is the retailer replenishment optimization problem for a retailer selling multiple products. Computation and simulation results demonstrate that simulated annealing and Tabu search improve solution quality. However, the performance improvement is less in simulations with random noise. Lastly, simulated annealing appears to be more robust than Tabu search, and the results justify its extra implementation effort and computation time when compared against hill climbing.

A FAST ALGORITHM FOR BANDWIDTH MINIMIZATION

2007 ◽  
Vol 16 (03) ◽  
pp. 537-544 ◽  
Author(s):  
ANDREW LIM ◽  
BRIAN RODRIGUES ◽  
FEI XIAO
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Minimization Problem ◽  
Fast Algorithm ◽  
Hill Climbing ◽  
Solution Quality ◽  
Complete Problem ◽  
Np Complete ◽  
Bandwidth Minimization ◽  
Matrix Bandwidth

We propose a simple and direct node shifting method with hill climbing for the well-known matrix bandwidth minimization problem. Many heuristics have been developed for this NP-complete problem including the Cuthill-McKee (CM) and the Gibbs, Poole and Stockmeyer (GPS) algorithms. Recently, heuristics such as Simulated Annealing, Tabu Search and GRASP have been used, where Tabu Search and the GRASP with Path Relinking achieved significantly better solution quality than the CM and GPS algorithms. Experimentation shows that our method achieves the best solution quality when compared with these while being much faster than newly-developed algorithms.

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