Abstract
Genetic algorithm (GA) is a part of evolutionary computing that simulates the theory of evolution and natural selection, where this technique depends on a heuristic random search. This algorithm reflects the operation of natural selection, where the fittest individuals are chosen for reproduction so that they produce offspring of the next generation. This paper proposes a method to improve GA using artificial bee colony (GABC). This proposed algorithm was applied to random number generation (RNG), and travelling salesman problem (TSP). The proposed method used to generate initial populations for GA rather than the random generation that used in traditional GA. The results of testing on RNG show that the proposed GABC was better than traditional GA in the mean iteration and the execution time. The results of testing TSP show the superiority of GABC on the traditional GA. The superiority of the GABC is clear in terms of the percentage of error rate, the average length route, and obtaining the shortest route. The programming language Python3 was used in programming the proposed methods.
Highlights
Artificial intelligence (AI) has been shown great performance in several tasks [1,2,3,4]
Algorithm 5: Genetic algorithm (GA) stage for travelling salesman problem (TSP) INPUT: Initial population (NR) OUTPUT: New solutions(NS) Process: Step1: Sort the new routes (NR) according to their fitness value. /* ascending order */ Step2: Select from NR the best solutions depending on their fitness value (BS). /* Using roulette wheel selection method*/ Step3: Perform crossover operation between BS to produce new solutions (NS). /* ordered Crossover is used, with a crossover rate of 0.85 */ Step4: If there is a mutation rate, perform the mutation operation/*swap mutation, with a mutation rate of 0.01*/ Step5: Return NS
In this paper, GA was improved using Artificial bee colony (ABC) (GABC) by using ABC to generate an initial population rather than randomly generation that used in traditional GA
Summary
Artificial intelligence (AI) has been shown great performance in several tasks [1,2,3,4]. Artificial bee colony (ABC) algorithm was proposed by Karaboga in 2005, which is simple in concept and requires very few initialization parameters to adjust. It has properties of fast convergence speed, good quality of solutions and good robustness, etc., but it has the disadvantages of early convergence and ease of falling into the local optimum [8]. TSP is one of the problems known as NP-hard and is a wellknown problem in the field of computer science and mathematics.
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