A hybrid genetic algorithm for the traveling salesman problem with pickup and delivery

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

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A Hybrid Genetic Algorithm for the Traveling Salesman Problem Using Generalized Partition Crossover

Parallel Problem Solving from Nature, PPSN XI ◽

10.1007/978-3-642-15844-5_57 ◽

2010 ◽

pp. 566-575 ◽

Cited By ~ 14

Author(s):

Darrell Whitley ◽

Doug Hains ◽

Adele Howe

Keyword(s):

Genetic Algorithm ◽

Traveling Salesman Problem ◽

Hybrid Genetic Algorithm ◽

Traveling Salesman ◽

The Traveling Salesman Problem ◽

Generalized Partition

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A Genetic Algorithm for the Traveling Salesman Problem with Pickup and Delivery Using Depot Removal and Insertion Moves

Applications of Evolutionary Computation - Lecture Notes in Computer Science ◽

10.1007/978-3-642-12242-2_44 ◽

2010 ◽

pp. 431-440

Author(s):

Volkan Çınar ◽

Temel Öncan ◽

Haldun Süral

Keyword(s):

Genetic Algorithm ◽

Traveling Salesman Problem ◽

Traveling Salesman ◽

Pickup And Delivery ◽

The Traveling Salesman Problem

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Hybrid Genetics Algorithms for Multiple Sequence Alignment

Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-9644-0.ch013 ◽

2016 ◽

pp. 346-366 ◽

Cited By ~ 1

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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