Performance assessment of foraging algorithms vs. evolutionary algorithms

作     者：El-Abd, Mohammed 

作者机构：Amer Univ Kuwait Dept Comp Engn Safat 13034 Kuwait 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2012年第182卷第1期

页      面：243-263页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Foraging algorithms Evolutionary algorithms Performance comparison Evolutionary optimization 

摘      要：The class of foraging algorithms is a relatively new field based on mimicking the foraging behavior of animals, insects, birds or fish in order to develop efficient optimization algorithms. The artificial bee colony (ABC), the bees algorithm (BA), ant colony optimization (ACO), and bacterial foraging optimization algorithms (BFOA) are examples of this class to name a few. This work provides a complete performance assessment of the four mentioned algorithms in comparison to the widely known differential evolution (DE), genetic algorithms (GAs), harmony search (HS), and particle swarm optimization (PSO) algorithms when applied to the problem of unconstrained nonlinear continuous function optimization. To the best of our knowledge, most of the work conducted so far using foraging algorithms has been tested on classical functions. This work provides the comparison using the well-known CEC05 benchmark functions based on the solution reached, the success rate, and the performance rate. (C) 2011 Elsevier Inc. All rights reserved.