A time-efficient method for metaheuristics: Using tabu search and tabu GA as a case

Chun Wei Tsai, Shih Pang Tseng, Ming Chao Chiang, Chu Sing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper presents an efficient algorithm for reducing the computation time of metaheuristics. The proposed algorithm is motivated by the observation that some of the sub-solutions of metaheuristics will eventually end up being part of the final solutions. As such, if they can be saved away as soon as they were found, then most, if not all, of the redundant computations can be eliminated to save the computation time of metaheuristics. To evaluate the performance of the proposed algorithm, we use it to cut the computation time of a single-solution-based algorithm called Tabu Search (TS) and a population-based algorithm called Tabu Genetic Algorithm (Tabu GA) in solving the traveling salesman problem (TSP). The test benchmarks for the TSP problem are from 198 up to 1,655 cities. Our experimental results indicate that the proposed algorithm can reduce the computation time from 65.72% up to about 94.25% compared to those of TS and Tabu GA alone.

Original languageEnglish
Title of host publicationProceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
Pages24-29
Number of pages6
DOIs
Publication statusPublished - 2009 Nov 27
Event2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009 - Shenyang, China
Duration: 2009 Aug 122009 Aug 14

Publication series

NameProceedings - 2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
Volume2

Other

Other2009 9th International Conference on Hybrid Intelligent Systems, HIS 2009
CountryChina
CityShenyang
Period09-08-1209-08-14

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Software

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