Improvement of Artificial Bee Colony Algorithm Based on Self-Adaptive Random Optimization Strategy
Abstract
In order to overcome the disadvantages that traditional ABC algorithm is inclined to fall into local optima and it has a low searching speed either, an improved ABC algorithm based on SRABC was proposed. Firstly, the improved algorithm was derived from the self-adaptive method to update the new location of ABC so as to improve the correlation within the bee colony. Secondly, BRO mechanism was used to restrain the direction of searching for fitness function in order to improve local searching ability. On the other hand, PSO algorithm was introduced at the initial stage of the improved ABC algorithm to increase the convergence rate. Finally, the simulation results in three benchmark functions show that the proposed algorithm has obviously better performance in search ability and convergence rate.
Keywords
Swarm intelligence, Artificial Bee Colony (ABC), Bidirectional Random Optimization (BRO), Self-adaptive, Particle Swarm Optimization (PSO).
DOI
10.12783/dtcse/smce2017/12454
10.12783/dtcse/smce2017/12454
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