7,522 research outputs found
A Hybrid Firefly Algorithm - Ant Colony Optimization for Traveling Salesman Problem
Abstract. In this paper, we develop a novel method hybrid firefly algorithm-ant colony optimization for solving traveling salesman problem. The ACO has distributed computation to avoid premature convergence and the FA has a very great ability to search solutions with a fast speed to converge. To improve the result and convergence time, we used hybrid method. The hybrid approach involves local search by the FA and global search by the ACO. Local solution of FA is normalized and is used to initialize the pheromone for the global solution search using the ACO. The outcome are compared with FA and ACO itself. The experiment showed that the proposed method can find the solution much better without trapped into local optimum with shorter computation time.Keywords: Traveling Salesman Problem, Firefly Algorithm, Ant Colony Optimization, hybrid method.Abstrak. Pada penelitian ini dikembangkan suatu metode baru yaitu hybrid firefly algorithm-ant colony optimization (hybrid FA-ACO) untuk menyelesaikan masalah traveling salesman problem (TSP). ACO memiliki komputasi terdistribusi sehingga dapat mencegah konvergensi dini dan FA memiliki kemampuan konvergensi yang cepat dalam pencarian solusi. Untuk memperbaiki solusi dan mempercepat waktu konvergensi, digunakan metode kombinasi. Pendekatan kombinasi ini meliputi pencarian solusi dengan FA dan pencarian global dengan ACO. Solusi lokal dari FA dinormalisasi dan digunakan untuk menginisialisasi feromon untuk pencarian global ACO. Hasil dari hybrid FA-ACO dibandingkan dengan FA dan ACO. Hasil penelitian menunjukkan bahwa metode yang diusulkan dapat menemukan solusi yang lebih baik tanpa terjebak lokal optimum dengan waktu komputasi lebih pendek.Kata kunci: Traveling Salesman Problem, Firefly Algorithm, Ant Colony Optimization, metode hybrid
A Hybrid Firefly Algorithm – Ant Colony Optimization for Traveling Salesman Problem
. In this paper, we develop a novel method hybrid firefly algorithm-ant colony optimization for solving traveling salesman problem. The ACO has distributed computation to avoid premature convergence and the FA has a very great ability to search solutions with a fast speed to converge. To improve the result and convergence time, we used hybrid method. The hybrid approach involves local search by the FA and global search by the ACO. Local solution of FA is normalized and is used to initialize the pheromone for the global solution search using the ACO. The outcome are compared with FA and ACO itself. The experiment showed that the proposed method can find the solution much better without trapped into local optimum with shorter computation time
AN IMPROVED GENETIC ALGORITHM WITH A LOCAL OPTIMIZATION STRATEGY AND AN EXTRA MUTATION LEVEL FOR SOLVING TRAVELING SALESMAN PROBLEM
The Traveling salesman problem (TSP) is proved to be NP-complete in most cases. The genetic algorithm (GA) is one of the most useful algorithms for solving this problem. In this paper a conventional GA is compared with an improved hybrid GA in solving TSP. The improved or hybrid GA consist of conventional GA and two local optimization strategies. The first strategy is extracting all sequential groups including four cities of samples and changing the two central cities with each other. The second local optimization strategy is similar to an extra mutation process. In this step with a low probability a sample is selected. In this sample two random cities are defined and the path between these cities is reversed. The computation results show that the proposed method also finds better paths than the conventional GA within an acceptable computation time
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
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
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
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