Paper Info
Title
Autonomous Bee Colony Optimization for multi-objective function
Abstract
An Autonomous Bee Colony Optimization (A-BCO) algorithm for solving multi-objective numerical problems is proposed. In contrast with previous Bee Colony algorithms, A-BCO utilizes a diversity-based performance metric to dynamically assess the archive set. This assessment is employed to adapt the bee colony structures and flying patterns. This self-adaptation feature is introduced to optimize the balance between exploration and exploitation during the search process. Moreover, the total number of search iterations is also determined/optimized by A-BCO, according to user pre-specified conditions, during the search process. We evaluate A-BCO upon numerical benchmark problems and the experimental results demonstrate the effectiveness and robustness of the proposed algorithm when compared with the Non-dominated Sorting Genetic Algorithm II and the latest Multi-objective Bee Colony Algorithm proposed to date.
Year
DOI
Venue
2010
10.1109/CEC.2010.5586057
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
multiobjective function,optimisation,search iterations,autonomous bee colony optimization,diversity-based performance metric,iterative methods,benchmark testing,measurement,classification algorithms,optimization,objective function,convergence
Artificial bee colony algorithm,Mathematical optimization,Computer science,Performance metric,Robustness (computer science),Sorting,Artificial intelligence,Statistical classification,Genetic algorithm,Machine learning,Benchmark (computing),Metaheuristic
Conference
ISBN
Citations 
PageRank 
978-1-4244-6909-3
5
0.45
References 
Authors
11
7
Name
Order
Citations
PageRank
Fanchao Zeng1132.59
James Decraene25010.17
Malcolm Yoke Hean Low369452.19
Philip Hingston470062.33
Wentong Cai51928197.81
Suiping Zhou653046.88
Mahinthan Chandramohan722211.67