An APPLICATION of A MODIFIED CAMEL TRAVELING BEHAVIOR ALGORITHM for TRAVELING SALESMAN PROBLEM

Abstract

Camel Traveling Behavior Algorithm (CA) is a fairly new algorithm developed in 2016 by Mohammed Khalid Ibrahim and Ramzy Salim Ali. Scientists have put forward a few publications on CA. CA was applied to continuous optimization problems and engineering problems in the literature. It has been shown that CA has comparable performance with Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Besides, a modified camel algorithm (MCA) has been implemented in the field of engineering and was showed that it has competitive performance with Cuckoo Search (CS), PSO, and CA. In this work, an application of MCA has been done in the traveling salesman problem. A set of classical datasets which have cities scale ranged from 51 to 150 was used in the application. The results show that the MCA is superior to Simulated Annealing (SA), Tabu Search (TS), GA, and CA for 60% of all datasets. Also, it was given that a detailed analysis presents the number of best, worst, average solutions, standard deviation, and the average CPU time concerning meta-heuristics. The metrics stress that MCA demonstrates a performance rate over 50% in finding optimal solutions. Finally, MCA solves the discrete problem in reasonable times in comparison to other algorithms for all datasets.

Keywords

• Modified Camel Algorithm
• Meta-heuristic Algorithms
• Traveling Salesman Problem

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