Reinforcement learning for solution updating in Artificial Bee Colony

Abstract

In the Artificial Bee Colony (ABC) algorithm, the employed bee and the onlooker bee phase involve updating the candidate solutions by changing a value in one dimension, dubbed one-dimension update process. For some problems which the number of dimensions is very high, the one-dimension update process can cause the solution quality and convergence speed drop. This paper proposes a new algorithm, using reinforcement learning for solution updating in ABC algorithm, called R-ABC. After updating a solution by an employed bee, the new solution results in positive or negative reinforcement applied to the solution dimensions in the onlooker bee phase. Positive reinforcement is given when the candidate solution from the employed bee phase provides a better fitness value. The more often a dimension provides a better fitness value when changed, the higher the value of update becomes in the onlooker bee phase. Conversely, negative reinforcement is given when the candidate solution does not provide a better fitness value. The performance of the proposed algorithm is assessed on eight basic numerical benchmark functions in four categories with 100, 500, 700, and 900 dimensions, seven CEC2005’s shifted functions with 100, 500, 700, and 900 dimensions, and six CEC2014’s hybrid functions with 100 dimensions. The results show that the proposed algorithm provides solutions which are significantly better than all other algorithms for all tested dimensions on basic benchmark functions. The number of solutions provided by the R-ABC algorithm which are significantly better than those of other algorithms increases when the number of dimensions increases on the CEC2005’s shifted functions. The R-ABC algorithm is at least comparable to the state-of-the-art ABC variants on the CEC2014’s hybrid functions.

Trim Loss Optimization in Paper Production Using Reinforcement Artificial Bee Colony

“…Hence, there is no existing benchmark problem in the literature. To evaluate the performance of the proposed method, we compare the quality of the solutions obtained from the R-ABC algorithm [29]- [30] with those from the ABC algorithm [35] and two state-of-the-art ABC algorithms, which are aABC [17] and TMABC [18], as well as the randomly generating solutions (referred as Random).…”

“…Specifically, universal sizes close to the dimensions that frequently give smaller trim loss have higher chances to be sampled through the reinforcement learning mechanism of the algorithm. This mechanism tends to perform well in high-dimensional problems [29]. Every newly generated solution must be checked for its feasibility.…”

“…Otherwise, the employed bee keeps the current solution, and TRIAL of the current solution is increased by 1. Consequently, a negative reinforcement is applied to the selected dimension using (29). The values of reward factor ߙ and penalty factor ߚ are calculated as shown in (30).…”

“…We apply Artificial Bee Colony algorithm with reinforcement solution updating (R-ABC) in [29] and [30] to solve this two-stage cutting stock problem with universal sizes. R-ABC is a swarm algorithm and is a variant of the Artificial Bee Colony (ABC) algorithm [31].…”

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

“…In the experiments, H-MOABC is used to solve the two scalable real-world RNP instances in the hierarchical decoupling manner. Fairee et al (2018) proposed a new ABC algorithm by using reinforcement learning in solution updating. According to whether the new solution produced by an employed bee is better or worse, a positive or negative reinforcement applied to the solution to be used by the onlooker bees.…”