Solving the Traveling Salesman's Problem Using the African Buffalo Optimization.

Julius Beneoluchi Odili,Mohd Nizam Mohmad Kahar

doi:10.1155/2016/1510256

Julius Beneoluchi Odili, Mohd Nizam Mohmad Kahar

Open Access

https://doi.org/10.1155/2016/1510256

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Abstract

This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays uncommon intelligence, strategic organizational skills, and exceptional navigational ingenuity in its traversal of the African landscape in search for food. The African Buffalo Optimization builds a mathematical model from the behavior of this animal and uses the model to solve 33 benchmark symmetric Traveling Salesman's Problem and six difficult asymmetric instances from the TSPLIB. This study shows that buffalos are able to ensure excellent exploration and exploitation of the search space through regular communication, cooperation, and good memory of its previous personal exploits as well as tapping from the herd's collective exploits. The results obtained by using the ABO to solve these TSP cases were benchmarked against the results obtained by using other popular algorithms. The results obtained using the African Buffalo Optimization algorithm are very competitive.

Highlights

The growing need for profit maximization and cost minimization has never been greater in human history than what we have today
The first experiment was concerned with the comparison of the performance of African Buffalo Optimization (ABO) in Traveling Salesman’s Problem (TSP) instances with the results obtained from a recent study [26] involving Berlin52, St70, Eil76, Pr76, KroA100, Eil101, Ch150, and Tsp225
The performance obtained from using the ABO is weighed against the performance of some popular algorithms such as the Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABO), Min-Max Ant System (MMAS), and Randomized Insertion Algorithm (RAI); some hybrid methods such as Model Induced Max-Min Ant Colony Optimization (MIMM-ACO), Hybrid Algorithm (HA), and Cooperative Genetic Ant Systems (CGAS); and some popular Neural Networks-based optimization methods

Summary

Introduction

The growing need for profit maximization and cost minimization has never been greater in human history than what we have today. This need has made optimization a very favoured area of scientific investigations. This development has led to the design of a number of optimization algorithms. Some of the most popular algorithms are the Particle Swarm Optimization [1], Ant Colony Optimization [2], Genetic Algorithm [3], Artificial Bee Colony [4], and many others. An attempt to proffer solutions to some of the weaknesses of these algorithms is the motivation for the development of the African Buffalo Optimization (ABO)

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