Comparative Analysis of Swarm-Based Metaheuristic Algorithms on Benchmark Functions

Kashif Hussain, Mohd Najib Mohd Salleh, Shi Cheng, Yuhui Shi

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


Swarm-based metaheuristic algorithms inspired from swarm systems in nature have produced remarkable results while solving complex optimization problems. This is due to their capability of decentralized control of search agents able to explore search environment more effectively. The large number of metaheuristics sometimes puzzle beginners and practitioners where to start with. This experimental study covers 10 swarm-based metaheuristic algorithms introduced in last decade to be investigated on their performances on 12 test functions of high dimensions with diverse features of modality, scalability, and valley landscape. Based on simulations, it can be concluded that firefly algorithm outperformed rest of the algorithms while tested unimodal functions. On multimodal functions, animal migration algorithm produced outstanding results as compared to rest of the methods. In future, further investigation can be conducted on relating benchmark functions to real-world optimization problem so that metaheuristic algorithms can be grouped according to suitability of problem characteristics.
Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence
Subtitle of host publication8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 – August 1, 2017, Proceedings, Part I
EditorsYing Tan, Hideyuki Takagi, Yuhui Shi
Number of pages9
ISBN (Electronic)978-3-319-61824-1
ISBN (Print)978-3-319-61823-4
Publication statusPublished - 24 Jun 2017

Publication series

NameLecture Notes in Computer Science

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