Julius Beneoluchi Odilia, Mohd Nizam Mohmad Kahara, A. Noraziaha,b, M. Zarinac, Riaz Ul Haqa
Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 759-769, 2018, DOI:10.1080/10798587.2017.1334370
Abstract 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… More >
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