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The Scientific World Journal has retracted this article. After conducting a thorough investigation, we have strong reason to believe that the peer review process was compromised.

This article was originally submitted to a Special Issue titled “Recent Advances in Metaheuristics and its Hybrids.” In late 2015, Dr. Xavier Delorme, the lead guest editor on the Special Issue, alerted us that his identity had been compromised. After further investigation, we discovered that several peer review reports in this issue had been submitted from similarly compromised email accounts.

We are retracting the articles in keeping with the “COPE statement on inappropriate manipulation of the peer review process.” There is no evidence that any of the authors or editors, including Dr. Delorme, were aware of this misconduct.

View the original article here.


  1. A. C. Biju, T. Aruldoss Albert Victoire, and K. Mohanasundaram, “An improved differential evolution solution for software project scheduling problem,” The Scientific World Journal, vol. 2015, Article ID 232193, 9 pages, 2015.
The Scientific World Journal
Volume 2015 (2015), Article ID 232193, 9 pages
Research Article

An Improved Differential Evolution Solution for Software Project Scheduling Problem

Anna University, Regional Centre, Coimbatore, Tamilnadu 641047, India

Received 6 December 2014; Accepted 4 March 2015

Academic Editor: Xavier Delorme

Copyright © 2015 A. C. Biju et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a differential evolution (DE) method for the software project scheduling problem (SPSP). The interest on finding a more efficient solution technique for SPSP is always a topic of interest due to the fact of ever growing challenges faced by the software industry. The curse of dimensionality is introduced in the scheduling problem by ever increasing software assignments and the number of staff who handles it. Thus the SPSP is a class of NP-hard problem, which requires a rigorous solution procedure which guarantees a reasonably better solution. Differential evolution is a direct search stochastic optimization technique that is fairly fast and reasonably robust. It is also capable of handling nondifferentiable, nonlinear, and multimodal objective functions like SPSP. This paper proposes a refined DE where a new mutation mechanism is introduced. The superiority of the proposed method is experimented and demonstrated by solving the SPSP on 50 random instances and the results are compared with some of the techniques in the literature.