A hybrid heuristic solving the traveling salesman problem

This paper presents a new hybrid heuristic for solving the Traveling Salesman Problem, The algorithm is designed on the frame of a general optimization procedure which acts upon two steps, iteratively. In first step of the global search, a feasible tour is constructed based on insertion approach. In the second step the feasible tour found at the first step, is improved by a local search optimization procedure. The second part of the paper presents the performances of the proposed heuristic algorithm, on several test instances. The statistical analysis shows the effectiveness of the local search optimization procedure, in the graphical representation.peer-reviewe

Traveling Salesman Problem with Optional Bonus Collection, Pickup Time and Passengers

This study introduces a variant of the Traveling Salesman Problem, named Traveling Salesman Problem with Optional Bonus Collection, Pickup Time and Passengers (PCVP-BoTc). It is a variant that incorporates elements of the Prize Collecting Traveling Salesman Problem and Ridesharing into the PCV. The objective is to optimize the revenue of the driver, which selectively defines which delivery or collection tasks to perform along the route. The economic effect of the collection is modeled by a bonus. The model can be applied to the solution of hybrid routing systems with route tasks and solidary transport. The driver, while performing the selected tasks, can give rides to persons who share route costs with him. Passengers are protected by restrictions concerning the maximum value they agree to pay for a ride and maximum travel duration. The activity of collecting the bonus in each locality demands a specific amount of time, affects the route duration, and is interconnected with the embarkment of passengers. Two mathematical formulations are presented for the problem and validated by a computational experiment using a solver. We propose four heuristic algorithms; three of them are hybrid metaheuristics. We tested the mathematical formulation implementations for 24 instances and the heuristic algorithms for 48

An Efficient Hybrid Ant Colony System for the Generalized Traveling Salesman Problem

The Generalized Traveling Salesman Problem (GTSP) is an extension of the well-known Traveling Salesman Problem (TSP), where the node set is partitioned into clusters, and the objective is to find the shortest cycle visiting each cluster exactly once. In this paper, we present a new hybrid Ant Colony System (ACS) algorithm for the symmetric GTSP. The proposed algorithm is a modification of a simple ACS for the TSP improved by an efficient GTSP-specific local search procedure. Our extensive computational experiments show that the use of the local search procedure dramatically improves the performance of the ACS algorithm, making it one of the most successful GTSP metaheuristics to date.Comment: 7 page

Metaheuristics and combinatorial optimization problems

This thesis will use the traveling salesman problem (TSP) as a tool to help present and investigate several new techniques that improve the overall performance of genetic algorithms (GA). Improvements include a new parent selection algorithm, harem select, that outperforms all other parent selection algorithms tested, some by up to 600%. Other techniques investigated include population seeding, random restart, heuristic crossovers, and hybrid genetic algorithms, all of which posted improvements in the range of 1% up to 1100%. Also studied will be a new algorithm, GRASP, that is just starting to enjoy a lot of interest in the research community and will also been applied to the traveling salesman problem (TSP). Given very little time to run, relative to other popular metaheuristic algorithms, GRASP was able to come within 5% of optimal on several of the TSPLIB maps used for testing. Both the GA and the GRASP algorithms will be compared with commonly used metaheuristic algorithms such as simulated annealing (SA) and reactive tabu search (RTS) as well as a simple neighborhood search - greedy search