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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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 http://www.aropub.org/wp-content/uploads/2015/08/Computer-NCON-PGR-E01-001-CR.pdf |
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QA76 Computer software Odili, Julius Beneoluchi M. N. M., Kahar African Buffalo Optimization (ABO): A New Metaheuristic Algorithm |
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. |
format |
Article |
author |
Odili, Julius Beneoluchi M. N. M., Kahar |
author_facet |
Odili, Julius Beneoluchi M. N. M., Kahar |
author_sort |
Odili, Julius Beneoluchi |
title |
African Buffalo Optimization (ABO): A New Metaheuristic
Algorithm |
title_short |
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_sort |
african buffalo optimization (abo): a new metaheuristic
algorithm |
publisher |
Academic Research Online Publisher |
publishDate |
2015 |
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 |
first_indexed |
2023-09-18T22:18:16Z |
last_indexed |
2023-09-18T22:18:16Z |
_version_ |
1777415484179742720 |