Artificial bee colony algorithm with directed scout

Abstract

As a relatively new model, the Artificial Bee Colony Algorithm (ABC) has shown impressive success in solving optimization problems. Nevertheless, its efficiency is still not satisfactory for some complex optimization problems. This paper has modified ABC and its other recent variants to improve its performance by modify the scout phase. This modification enhances its exploitation ability by intensifying the regions in the search space, which probably includes reasonable solutions. The experiments were performed on the CEC2014 benchmark suite, CEC2015 benchmark functions, and three real-life problems: pressure vessel design problem, tension and compression spring design problem, and Frequency-Modulated (FM) problem. And the proposed modification was applied to basic ABC, Gbest-Guided ABC, Depth First Search ABC, and Teaching Learning Based ABC, and they were compared with their modified counterparts. The results have shown that our modification can successfully increase the performance of the original versions. Moreover, the proposed modified algorithm was compared with the stateof-the-art optimization algorithms, and it produced competitive results.

Artificial Bee Colony Algorithm Based on Dimensional Memory Mechanism and Adaptive Elite Population for Training Artificial Neural Networks

“…Liang et al devised a search mechanism using the information of the global best solution with two adaptive parameters to balance exploration and exploitation at different phases (I-ABC) [25]. Saleh et al constructed a new update equation that guarantees updating towards the global best solution via absolute value (ABCds) [26]. Rahnema et al combined ABC with WOA and proposed equations for updating towards the global best solution for both employed bees and onlooker bees, respectively (ABCWOA) [27].…”

Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms

“…The algorithm begins with a randomly distributed initial population generation and evaluation [11]. Then, repeated search cycles are executed to update the optimal solution.…”

“…In our research, a swarm intelligent algorithm named the artificial bee colony algorithm (ABC), which possesses a strong global optimization ability and fewer parameters [11][12][13][14], is firstly proposed to solve the problem of assigning the entrances and exits as well as autonomous ETVs for several outbound and inbound tasks simultaneously. Actually, as an artificial intelligence algorithm, the ABC has been well adapted for various complex optimization and scheduling problems [15][16][17][18][19][20][21]; however, it often suffers from the problem of a slow convergence rate because of its single-dimensional random search strategy in the bee updating phases.…”