Close
Help




JOURNAL

Evolutionary Bioinformatics

A New Hybrid MGBPSO-GSA Variant for Improving Function Optimization Solution in Search Space

Submit a Paper


Evolutionary Bioinformatics 2017:13 1176934317699855

Original Research

Published on 22 Mar 2017

DOI: 10.1177/1176934317699855


Further metadata provided in PDF



Sign up for email alerts to receive notifications of new articles published in Evolutionary Bioinformatics

Abstract

In this article, a newly hybrid nature-inspired approach (MGBPSO-GSA) is developed with a combination of Mean Gbest Particle Swarm Optimization (MGBPSO) and Gravitational Search Algorithm (GSA). The basic inspiration is to integrate the ability of exploitation in MGBPSO with the ability of exploration in GSA to synthesize the strength of both approaches. As a result, the presented approach has the automatic balance capability between local and global searching abilities. The performance of the hybrid approach is tested on a variety of classical functions, ie, unimodal, multimodal, and fixed-dimension multimodal functions. Furthermore, Iris data set, Heart data set, and economic dispatch problems are used to compare the hybrid approach with several metaheuristics. Experimental statistical solutions prove empirically that the new hybrid approach outperforms significantly a number of metaheuristics in terms of solution stability, solution quality, capability of local and global optimum, and convergence speed.



Downloads

PDF  (1.96 MB PDF FORMAT)

RIS citation   (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)

XML   (137.98 KB XML FORMAT)

BibTex citation   (BIBDESK, LATEX)




Quick Links


New article and journal news notification services