Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After criti...

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Main Authors:	Julius, Beneoluchi Odili, M. N. M., Kahar, Noraziah, Ahmad, M., Zarina, Riaz, Ul Haq
Format:	Article
Language:	English
Published:	Taylor & Francis 2017
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/18345/ http://umpir.ump.edu.my/id/eprint/18345/ http://umpir.ump.edu.my/id/eprint/18345/ http://umpir.ump.edu.my/id/eprint/18345/1/Performance%20Analyses%20of%20Nature-inspired%20Algorithms1.pdf

id	ump-18345
recordtype	eprints
spelling	ump-183452018-09-12T07:56:52Z http://umpir.ump.edu.my/id/eprint/18345/ Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management Julius, Beneoluchi Odili M. N. M., Kahar Noraziah, Ahmad M., Zarina Riaz, Ul Haq QA76 Computer software This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. The study reveals that the African Buffalo Optimization and the Ant Colony Optimization are the best in solving the symmetric TSP, which is similar to intelligence gathering channel in the strategic management of big organizations, while the Randomized Insertion Algorithm holds the best promise in asymmetric TSP instances akin to strategic information exchange channels in strategic management. Taylor & Francis 2017-06 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/18345/1/Performance%20Analyses%20of%20Nature-inspired%20Algorithms1.pdf Julius, Beneoluchi Odili and M. N. M., Kahar and Noraziah, Ahmad and M., Zarina and Riaz, Ul Haq (2017) Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management. Intelligent Automation & Soft Computing. pp. 1-11. ISSN 1079-8587 (In Press) http://www.tandfonline.com/doi/abs/10.1080/10798587.2017.1334370 DOI: 10.1080/10798587.2017.1334370
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Julius, Beneoluchi Odili M. N. M., Kahar Noraziah, Ahmad M., Zarina Riaz, Ul Haq Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management
description	This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. The study reveals that the African Buffalo Optimization and the Ant Colony Optimization are the best in solving the symmetric TSP, which is similar to intelligence gathering channel in the strategic management of big organizations, while the Randomized Insertion Algorithm holds the best promise in asymmetric TSP instances akin to strategic information exchange channels in strategic management.
format	Article
author	Julius, Beneoluchi Odili M. N. M., Kahar Noraziah, Ahmad M., Zarina Riaz, Ul Haq
author_facet	Julius, Beneoluchi Odili M. N. M., Kahar Noraziah, Ahmad M., Zarina Riaz, Ul Haq
author_sort	Julius, Beneoluchi Odili
title	Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management
title_short	Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management
title_full	Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management
title_fullStr	Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management
title_full_unstemmed	Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management
title_sort	performance analyses of nature-inspired algorithms on the traveling salesman’s problems for strategic management
publisher	Taylor & Francis
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18345/ http://umpir.ump.edu.my/id/eprint/18345/ http://umpir.ump.edu.my/id/eprint/18345/ http://umpir.ump.edu.my/id/eprint/18345/1/Performance%20Analyses%20of%20Nature-inspired%20Algorithms1.pdf
first_indexed	2023-09-18T22:25:56Z
last_indexed	2023-09-18T22:25:56Z
_version_	1777415966517362688

Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

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