Pareto simulated annealing—a metaheuristic technique for multiple‐objective combinatorial optimization

1998 ◽  
Vol 7 (1) ◽  
pp. 34-47 ◽  
Author(s):  
Piotr Czyzżak ◽  
Adrezej Jaszkiewicz
Keyword(s):  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Multiple Objective ◽  
Pareto Simulated Annealing

Matched Field Processing Based on the Simulated Annealing

2013 ◽  
Vol 651 ◽  
pp. 879-884
Author(s):  
Qi Wang ◽  
Ying Min Wang ◽  
Yan Ni Gou
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Mediterranean Sea ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Combinatorial Optimization Problem ◽  
Matched Field Processing ◽  
Combinatorial Optimization Problems ◽  
Passive Localization

The matched field processing (MFP) for localization usually needs to match all the replica fields in the observation sea with the received fields, and then find the maximum peaks in the matched results, so how to find the maximum in the results effectively and quickly is a problem. As known the classical simulated annealing (CSA) which has the global optimization capability is used widely for combinatorial optimization problems. For passive localization the position of the source can be recognized as a combinatorial optimization problem about range and depth, so a new matched field processing based on CSA is proposed. In order to evaluate the performance of this method, the normal mode was used to calculate the replica field. Finally the algorithm was evaluated by the dataset in the Mediterranean Sea in 1994. Comparing to the conventional matched field passive localization (CMFP), it can be conclude that the new one can localize optimum peak successfully where the output power of CMFP is maximum, meanwhile it is faster than CMFP.

Parallel Distributed Approaches to Combinatorial Optimization: Benchmark Studies on Traveling Salesman Problem

1990 ◽  
Vol 2 (3) ◽  
pp. 261-269 ◽  
Author(s):  
Carsten Peterson
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Information Processing ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Traveling Salesman ◽  
Neural Information ◽  
Neural Information Processing

We present and summarize the results from 50-, 100-, and 200-city TSP benchmarks presented at the 1989 Neural Information Processing Systems (NIPS) postconference workshop using neural network, elastic net, genetic algorithm, and simulated annealing approaches. These results are also compared with a state-of-the-art hybrid approach consisting of greedy solutions, exhaustive search, and simulated annealing.

A Multilevel Hypergraph Partitioning Algorithm Based on Simulated Annealing

2013 ◽  
Vol 753-755 ◽  
pp. 2908-2911
Author(s):  
Yao Yuan Zeng ◽  
Wen Tao Zhao ◽  
Zheng Hua Wang
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Unstructured Meshes ◽  
Running Time ◽  
Hypergraph Partitioning ◽  
Benchmark Suite ◽  
Partitioning Algorithm ◽  
Better Than

Multilevel hypergraph partitioning is a significant and extensively researched problem in combinatorial optimization. In this paper, we present a multilevel hypergraph partitioning algorithm based on simulated annealing approach for global optimization. Experiments on the benchmark suite of several unstructured meshes show that, for 2-, 4-, 8-, 16-and 32-way partitioning, although more running time was demanded, the quality of partition produced by our algorithm are on the average 14% and the maximum 22% better than those produced by partitioning software hMETIS in term of the SOED metric.

SIMULATED ANNEALING SCHEMES IN TRANSIENTLY CHAOTIC NEURAL NETWORK MODEL

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2579-2584 ◽  
Author(s):  
Y. C. FENG ◽  
X. CAI
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Neural Network Model ◽  
Approximation Method ◽  
Optimization Problems ◽  
Chaotic Neural Network ◽  
Combinatorial Optimization Problems ◽  
Search Speed ◽  
Evolution Function

A transiently chaotic neural network (TCNN) is an approximation method for combinatorial optimization problems. The evolution function of self-back connect weight, called annealing function, influences the accurate and search speed of TCNN model. This paper analyzes two common annealing schemes. Furthermore we proposed a new subsection exponential annealing function. Finally, we compared these annealing schemes in TSP problem.

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