African Buffalo Optimization (ABO): A New Metaheuristic Algorithm

This paper proposes a new meta-heuristic approach to solving numerical and graph-based problems. The African buffalo algorithm evolved from an understanding of the animal's survival instincts and the search techniques they utilize in the African forests and savannahs; the search for the optimal...

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Main Authors: Odili, Julius Beneoluchi, M. N. M., Kahar
Format: Article
Language:English
Published: Academic Research Online Publisher 2015
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/14478/
http://umpir.ump.edu.my/id/eprint/14478/1/30001.pdf
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author Odili, Julius Beneoluchi
M. N. M., Kahar
author_facet Odili, Julius Beneoluchi
M. N. M., Kahar
author_sort Odili, Julius Beneoluchi
building UMP Institutional Repository
collection Online Access
description This paper proposes a new meta-heuristic approach to solving numerical and graph-based problems. The African buffalo algorithm evolved from an understanding of the animal's survival instincts and the search techniques they utilize in the African forests and savannahs; the search for the optimal path to pasture is aligned to their cooperative, intelligent, and social nature. The African Buffalo Optimization (A.B.0) algorithm simulates the African buffalos' behaviour by encapsulation in a mathematical model; which solves a number of discrete optimization problems using graph-based route planning, job scheduling and it extends Swarm Intelligence paradigms. When compared to the Ant Colony Optimization algorithm, Simulated Annealing and Genetic Algorithm, the results obtained from African Buffalo Optimization show that the algorithm works well and can be extended to solving problems like: path planning, scheduling, vehicle routing in addition to other constraint-driven problems.
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publishDate 2015
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spelling ump-144782018-07-27T02:03:06Z http://umpir.ump.edu.my/id/eprint/14478/ African Buffalo Optimization (ABO): A New Metaheuristic Algorithm Odili, Julius Beneoluchi M. N. M., Kahar QA76 Computer software This paper proposes a new meta-heuristic approach to solving numerical and graph-based problems. The African buffalo algorithm evolved from an understanding of the animal's survival instincts and the search techniques they utilize in the African forests and savannahs; the search for the optimal path to pasture is aligned to their cooperative, intelligent, and social nature. The African Buffalo Optimization (A.B.0) algorithm simulates the African buffalos' behaviour by encapsulation in a mathematical model; which solves a number of discrete optimization problems using graph-based route planning, job scheduling and it extends Swarm Intelligence paradigms. When compared to the Ant Colony Optimization algorithm, Simulated Annealing and Genetic Algorithm, the results obtained from African Buffalo Optimization show that the algorithm works well and can be extended to solving problems like: path planning, scheduling, vehicle routing in addition to other constraint-driven problems. Academic Research Online Publisher 2015 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14478/1/30001.pdf Odili, Julius Beneoluchi and M. N. M., Kahar (2015) African Buffalo Optimization (ABO): A New Metaheuristic Algorithm. Journal of Advanced & Applied Sciences (JAAS), 03 (03). pp. 101-106. ISSN 2289-6260. (Published) http://www.aropub.org/wp-content/uploads/2015/08/Computer-NCON-PGR-E01-001-CR.pdf
spellingShingle QA76 Computer software
Odili, Julius Beneoluchi
M. N. M., Kahar
African Buffalo Optimization (ABO): A New Metaheuristic Algorithm
title African Buffalo Optimization (ABO): A New Metaheuristic Algorithm
title_full African Buffalo Optimization (ABO): A New Metaheuristic Algorithm
title_fullStr African Buffalo Optimization (ABO): A New Metaheuristic Algorithm
title_full_unstemmed African Buffalo Optimization (ABO): A New Metaheuristic Algorithm
title_short African Buffalo Optimization (ABO): A New Metaheuristic Algorithm
title_sort african buffalo optimization (abo): a new metaheuristic algorithm
topic QA76 Computer software
url http://umpir.ump.edu.my/id/eprint/14478/
http://umpir.ump.edu.my/id/eprint/14478/
http://umpir.ump.edu.my/id/eprint/14478/1/30001.pdf