3,531 research outputs found
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
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
How to make a greedy heuristic for the asymmetric traveling salesman problem competitive
It is widely confirmed by many computational experiments that a greedy type heuristics for the Traveling Salesman Problem (TSP) produces rather poor solutions except for the Euclidean TSP. The selection of arcs to be included by a greedy heuristic is usually done on the base of cost values. We propose to use upper tolerances of an optimal solution to one of the relaxed Asymmetric TSP (ATSP) to guide the selection of an arc to be included in the final greedy solution. Even though it needs time to calculate tolerances, our computational experiments for the wide range of ATSP instances show that tolerance based greedy heuristics is much more accurate an faster than previously reported greedy type algorithms
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āļāļāļāļąāļāļĒāđāļ āļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāđāļāđāļāļāļēāļĢāļāļāļāđāļāļāđāļŠāđāļāļāļēāļāđāļāļīāļāļĢāļāļāļāļŠāđāļāđāļāļĢāļ·āđāļāļāļŠāļģāļāļēāļāđāļāļ·āđāļāđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļēāļĢāđāļāļīāļāļāļēāļāđāļāļĒāļĄāļĩāļ§āļąāļāļāļļāļāļĢāļ°āļŠāļāļāđāđāļŦāđāļĢāļ°āļĒāļ°āļāļēāļāļāļĩāđāđāļāđāđāļāļāļēāļĢāļāļāļŠāđāļāļāđāļģāļāļĩāđāļŠāļļāļ āļāļąāļāļŦāļēāđāļŠāđāļāļāļēāļāļāļāļŠāđāļāđāļāļĢāļ·āđāļāļāļŠāļģāļāļēāļāļāļāļāļāļĢāļīāļĐāļąāļāļāļĢāļāļĩāļĻāļķāļāļĐāļē āļĄāļĩāļāļļāļāļāļĢāļ°āļāļēāļĒāļŠāļīāļāļāđāļēāđāļāļĩāļĒāļāđāļŦāđāļāđāļāļĩāļĒāļ§ āđāļāļ·āđāļāļŠāđāļāđāļāļĢāļ·āđāļāļāļŠāļģāļāļēāļāđāļāļĒāļąāļāļĢāđāļēāļāļāļąāļ§āđāļāļāļāļģāļŦāļāđāļēāļĒ 20 āļĢāđāļēāļ āđāļāđāļāļāļāļĢāļļāļāđāļāļāļŊ āđāļĨāļ°āļāļĢāļīāļĄāļāļāļĨ āļāļēāļāļ§āļīāļāļąāļĒāļāļĩāđāļāļķāļāđāļāđāļāļģāđāļŠāļāļāđāļāļ§āļāļēāļāļāļĢāļąāļāļāļĢāļļāļāđāļĨāļ°āļāļāļāđāļāļāđāļŠāđāļāļāļēāļāļāļēāļĢāļāļāļŠāđāļāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄāđāļĨāļ°āļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāđāļāļĒāļāļēāļĢāļāļĢāļ°āļĒāļļāļāļāđāļāļēāļĢāđāļāđāļāļąāļāļŦāļēāļāļēāļĢāļāļąāļāđāļŠāđāļāļāļēāļāđāļāļīāļāļĢāļāļŠāļģāļŦāļĢāļąāļāļāļēāļĢāđāļāđāļāļąāļāļŦāļēāļāļēāļĢāđāļāļīāļāļāļēāļāļāļāļāļāļāļąāļāļāļēāļāļāļēāļĒāļāļĩāđāļĄāļĩāļĢāļ°āļĒāļ°āļāļēāļāđāļāđāļĨāļ°āļāļĨāļąāļāđāļāđāļēāļāļąāļ(Symmetric traveling salesman problem) āđāļāļĒāđāļāđāļ§āļīāļāļĩāļāļēāļĢāļāļģāļĨāļāļāļāļēāļĢāļāļāđāļŦāļāļĩāļĒāļ§āđāļāļ·āđāļāļāļĢāļąāļāļāļĢāļļāļāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļāļāļāļēāļĢāļāļąāļāļāļēāļĢāđāļŠāđāļāļāļēāļāļāļēāļĢāđāļāļīāļāļĢāļ āđāļĨāļ°āđāļāđāđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļ§āļīāļāļĩāļāļĩāđāđāļāđāđāļāļāļąāļāļāļļāļāļąāļāļāļ·āļāļ§āļīāļāļĩāļāļēāļĢāļŦāļēāļāļģāļāļāļāļāļĩāđāđāļāļĨāđāđāļāļĩāļĒāļāļāļĩāđāļŠāļļāļ (Nearest neighbor heuristic) āđāļĨāļ°āļ§āļīāļāļĩāļāļēāļĢāļāļģāļĨāļāļāļāļēāļĢāļāļāđāļŦāļāļĩāļĒāļ§ (Simulated annealing algorithm) āļāļąāđāļāļāļĩāđ āļāļēāļāļāļēāļĢāļ§āļīāļāļąāļĒāļāļāļ§āđāļē āļ§āļīāļāļĩāļāļēāļĢāļāļģāļĨāļāļāļāļēāļĢāļāļāđāļŦāļāļĩāļĒāļ§āļŠāļēāļĄāļēāļĢāļāļĨāļāļĢāļ°āļĒāļ°āļāļēāļāļāļēāļĢāđāļāļīāļāļĢāļāļāļēāļāļ§āļīāļāļĩāļāļĩāđāđāļāđāđāļāļāļąāļāļāļļāļāļąāļāđāļāđ 7.81 % āļāļģāļŠāļģāļāļąāļ: āļāļąāļāļŦāļēāļāļēāļĢāđāļāļīāļāļāļēāļāļāļāļāļāļāļąāļāļāļēāļāļāļēāļĒ, āļāļēāļĢāļŦāļēāļāļģāļāļāļāļāļĩāđāđāļāļĨāđāđāļāļĩāļĒāļāļāļĩāđāļŠāļļāļ, āļ§āļīāļāļĩāļāļēāļĢāļāļģāļĨāļāļāļāļēāļĢāļāļāđāļŦāļāļĩāļĒāļ§, āđāļĄāļāļēāļ§āļīāļāļĩāļŪāļīāļ§āļĢāļīāļŠāļāļīāļ Abstract This research was concerned with designing the vehicle routing for cosmetic products. The objective was to minimize the total transportation distance. In addition, there was a single depot of the transportation routing problem in the cosmetic company case study in order to distribute products through 20 cosmetic dealers in Bangkok and nearby places. We proposed the effective transportation route to solve the symmetric traveling salesman problem by using the simulated annealing algorithm to enhance the efficiency of the vehicle routing. Accordingly, two algorithms, the nearest neighbor heuristic and the simulated annealing algorithm, are compared. As in the results, the simulated annealing algorithm outperforms the current method approximately 7.81% Keywords: Travelling salesman problem, nearest neighbor heuristic, simulated annealing, metaheuristic
A review of the Tabu Search Literature on Traveling Salesman Problems
The Traveling Salesman Problem (TSP) is one of the most widely studied problems inrncombinatorial optimization. It has long been known to be NP-hard and hence research onrndeveloping algorithms for the TSP has focused on approximate methods in addition to exactrnmethods. Tabu search is one of the most widely applied metaheuristic for solving the TSP. Inrnthis paper, we review the tabu search literature on the TSP, point out trends in it, and bringrnout some interesting research gaps in this literature.
Parallel ACO with a Ring Neighborhood for Dynamic TSP
The current paper introduces a new parallel computing technique based on ant
colony optimization for a dynamic routing problem. In the dynamic traveling
salesman problem the distances between cities as travel times are no longer
fixed. The new technique uses a parallel model for a problem variant that
allows a slight movement of nodes within their Neighborhoods. The algorithm is
tested with success on several large data sets.Comment: 8 pages, 1 figure; accepted J. Information Technology Researc
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