# A Modified Artificial Bee Colony Algorithm Based on the Self-Learning Mechanism
## 1 Introduction  
In recent years, swarm intelligence algorithms have received a wide spread attention. The artificial bee colony (ABC) algorithm is a relatively new approach that was proposed by [PERSON] [1; 2], motivated by the collective foraging behavior of honey bees. In the process of foraging, the bees need to find the place of food source with the highest nectar amount. In ABC system, artificial bees search in the given search space and the food sources represent possible solutions for the optimisation problems. The bees update the candidate solutions by means of solution search equation (SSE) and if the new solution is better than the previous one in their memory, they memorize the new position and forget the previous one. Due to its simplicity and ease of implementation, the ABC algorithm has captured much attention and has been applied successfully to a variety of fields, such as classification and function approximation [3], feature selection [4], inverse modelling of a solar collector [5], electric power system optimization [6], multi-objective optimisation [7], complex network optimization [8], transportation energy demand [9], large-scale service composition for cloud manufacturing [10], job-shop scheduling problem with no-wait constraint [11], respiratory disease detection from medical images [12]. Although the ABC algorithm has been widely used in different fields, some researchers have also pointed out that the ABC algorithm suffers from low solution accuracy and poor convergenceperformance. To solve the optimization problem, the intelligent optimization algorithm should combine global search methods, used to locate the potential optimal regions, with local search methods, used to fine-tune the candidate solutions, to balance exploration and exploitation process. However, exploration strategies and exploitation strategies contradict each other and to achieve good performance, they should be well balanced. While the SSE of ABC, which is used to generate new candidate solutions based on the information of the present solutions, does well in exploration but poorly in exploitation, which results in the poor convergence rate. Thus, many related and improved ABC algorithms have been proposed [13; 14; 15; 16]. Inspired by a operator of global best (gbest) solution in particle swarm optimization (PSO) algorithm [17], [PERSON] and [PERSON] proposed a modified ABC algorithm called gbest-guided ABC (GABC) algorithm to improve the exploitation [18]; the gbest term in the modified SSE can drive the new candidate solution towards the global best solution. Although the GABC algorithm accelerated the convergence rate, the exploration performance decreased. Therefore, how to balance exploration and exploitation has become the main goal in the ABC research. Inspired by differential evolution [19], [PERSON] and [PERSON] introduced a new initialization approach and proposed an improved SSE which is based on that the bee searches only around the best solution of the previous iteration to improve the exploitation; by hybridizing the original SSE and the improved SSE with the fixed selective probability, the new search mechanism obtains better performance [20]. After that, based on the two SSEs, [PERSON] and [PERSON] proposed the modified ABC (MABC) algorithm which excludes the onlooker and scout bee stage. In MABC, a selective probability was introduced to balance exploration of the original SSE and exploitation of the improved SSE [21]; if the new candidate solution obtained using the improved SSE is worse than the original one, the bee uses the original SSE to generate a new candidate solution with a certain probability. To well balance exploration and exploitation, [PERSON] and [PERSON] constructed an adaptive scaling factor (SF) which regulates the range of parameter in SSE by using [PERSON]'s 1/5 mutation rule; a smaller SF makes the candidate solution fine-tuned with a small steps while causing slow convergence rate and the bigger SF speeds up the search, but it reduces the exploitation performance [22].