title: A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.
abstract: There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.
authors.affiliation: School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China.
authors.forename: Weixing
authors.initials: W
authors.lastname: Su
authors.affiliation: School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China.
authors.forename: Hanning
authors.initials: H
authors.lastname: Chen
authors.affiliation: School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China.
authors.forename: Fang
authors.initials: F
authors.lastname: Liu
authors.affiliation: Beijing Shenzhou Aerospace Software Technology Co. Ltd., Beijing 110000, China.
authors.forename: Na
authors.initials: N
authors.lastname: Lin
authors.affiliation: Beijing Shenzhou Aerospace Software Technology Co. Ltd., Beijing 110000, China; School of Mechanical Engineering, Beijing Institute of Technology, Beijing 110081, China.
authors.forename: Shikai
authors.initials: S
authors.lastname: Jing
authors.affiliation: School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, China.
authors.forename: Xiaodan
authors.initials: X
authors.lastname: Liang
authors.affiliation: College of Information and Technology, Jilin Normal University, Siping 136000, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
keywords: Artificial bee colony; Crossover operation; Dynamic optimization; Life-cycle; Powell’s search