An Angle-Based Crossover Tabu Search for the Traveling Salesman Problem

2007 ◽  
Author(s):  
Ning Yang ◽  
Ping Li ◽  
Baisha Mei
Keyword(s):  
Tabu Search ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem

scholarly journals Which Local Search Operator Works Best for the Open-Loop TSP?

2019 ◽  
Vol 9 (19) ◽  
pp. 3985
Author(s):  
Lahari Sengupta ◽  
Radu Mariescu-Istodor ◽  
Pasi Fränti
Keyword(s):  
Simulated Annealing ◽  
Tabu Search ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Processing Time ◽  
Closed Loop ◽  
Open Loop ◽  
Traveling Salesman ◽  
The Traveling Salesman Problem ◽  
Local Search Operator

The traveling salesman problem (TSP) has been widely studied for the classical closed-loop variant. However, very little attention has been paid to the open-loop variant. Most of the existing studies also focus merely on presenting the overall optimization results (gap) or focus on processing time, but do not reveal much about which operators are more efficient to achieve the result. In this paper, we present two new operators (link swap and 3–permute) and study their efficiency against existing operators, both analytically and experimentally. Results show that while 2-opt and relocate contribute equally in the closed-loop case, the situation changes dramatically in the open-loop case where the new operator, link swap, dominates the search; it contributes by 50% to all improvements, while 2-opt and relocate have a 25% share each. The results are also generalized to tabu search and simulated annealing.

Hybrid Genetics Algorithms for Multiple Sequence Alignment

2016 ◽  
pp. 346-366 ◽  
Author(s):  
John Tsiligaridis
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Traveling Salesman Problem ◽  
Sequence Alignment ◽  
Multiple Sequence Alignment ◽  
Hybrid Genetic Algorithm ◽  
Traveling Salesman ◽  
Divide And Conquer ◽  
Multiple Sequence ◽  
The Traveling Salesman Problem

The purpose of this chapter is to present a set of algorithms and their efficiency for the consistency based Multiple Sequence Alignment (MSA) problem. Based on the strength and adaptability of the Genetic Algorithm (GA) two approaches are developed depending on the MSA type. The first approach, for the non related sequences (no consistency), involves a Hybrid Genetic Algorithm (GA_TS) considering also Tabu Search (TS). The Traveling Salesman Problem (TSP) is also applied determining MSA orders. The second approach, for sequences with consistency, deals with a hybrid GA based on the Divide and Conquer principle (DCP) and it can save space. A consistent dot matrices (CDM) algorithm discovers consistency and creates MSA. The proposed GA (GA_TS_VS) also uses TS but it works with partitions. In conclusion, GAs are stochastic approaches that are proved very beneficial for MSA in terms of their performance.

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