FYI
http://www.journals.elsevier.com/information-sciences/call-for-papers/n…
In the past few decades, many nature-inspired optimization algorithms have been developed for successfully solving a wide range of optimization problems. Evolutionary Algorithms (EAs), Simulated Annealing, Differential Evolution (DE), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Estimation of Distribution Algorithms (EDAs) are just some representative examples among many others. Although these nature-inspired techniques have demonstrated excellent search capabilities for solving small or medium-sized optimization problems, they still encounter serious challenges when applied to solving large scale optimization problems, i.e., problems with several hundreds to thousands of variables.
Many real-world optimization problems involve a large number of decision variables. For example, in shape optimization, a large number of shape design variables are often used to represent complex shapes, such as turbine blades, aircraft wings, and heat exchangers, etc. How well nature-inspired optimization algorithms handle this sort of real-world large scale global optimization (LSGO) problems still remains an open question. In recent years, researches on scaling up nature-inspired optimization algorithms such as EAs to tackle large scale optimization problems have gathered momentum on both theoretical and empirical studies. Considering the high-level research activities on LSGO and the practical significance of the topic, this special issue aims to highlight the recent advances in nature-inspired algorithms for handling LSGO problems, involving single or multiple objectives, unconstrained or constrained, binary/discrete or real, or mixed decision variables. More specifically, we encourage interested researchers to submit their original and unpublished work on the following topics (but are not limited to):
- Theoretical and experimental studies concerning the scalability issue of nature-inspired optimization algorithms;
- Novel decomposition methods for solving single or multiobjective LSGO problems;
- Novel approaches and algorithms for handling highly constrained LSGO problems;
- Novel methods for hybridizing classical optimization methods (e.g., simplex) and nature-inspired algorithms for LSGO problems;
- Studies on novel test suites that help understand LSGO problem characteristics;
- Studies on improving efficiency when handling LSGO problems;
- Real-world applications of nature-inspired optimization algorithms to LSGO problems, e.g., large scale vehicle routing problems, job shop scheduling problems, etc.
Submission Guidelines
Manuscripts should be prepared according to the author guidelines which can be found at: http://www.ees.elsevier.com/ins. All manuscripts should be submitted through Elsevier Editorial System (EES). The authors must select as “nature-inspired algorithms for LSGO” when they reach the “Article Type” step in the submission process. The EES website is located at: http://www.ees.elsevier.com/ins. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere. The review process will be managed by the Guest Editors of this Special Issue and the Editor-in-Chief, Professor W. Pedrycz (wpedrycz@ualberta.ca).
Important Dates
- 30 September 2013, Submission deadline
- 30 November 2013, Notification of the first-round review
- 28 February 2014, Revised submission due
- 30 April 2014, Final notice of acceptance/reject
- 30 May 2014, Final manuscript
The expected publication time of the special issue will be around August 2014.