[Apologies for multiple postings. Please, distribute widely. This CFP could be found at the following link: https://www.journals.elsevier.com/swarm-and-evolutionary-computation/call-for-papers/special-issue-on-advances-in-evolutionary-multi-objective-op]

 

Swarm and Evolutionary Computation

(ISSN: 2210-6502 / Impact Factor: 2.963 / 5-Year Impact Factor: 5.770)

Special Issue on Advances in Evolutionary Multi-objective Optimization

Call For Papers

 

Guest Editors:

Dr. Slim Bechikh, SOIE lab, ISGT Computer Science

Department, University of Tunis, Tunisia

Email: [log in to unmask]

 

Prof. Carlos Artemio Coello Coello, CINVESTAV-IPN,

Department of Computer Science, Mexico, DF 07360, Mexico

Email: [log in to unmask]

 

I. AIMS AND SCOPE

Most real world problems involve the optimization of multiple possibly conflicting objectives that should be minimized or maximized simultaneously while respecting some constraints. Unlike single-objective optimization, the solution of a Multi-Objective Problem (MOP) corresponds to a set of trade-off solutions, each expressing a particular compromise between the different objectives. The image of these trade-off solutions in the objective space is called the Pareto Front (PF). The main goal of multi-objective optimization is to approximate the PF by ensuring convergence towards the front and diversity along it. Multi-Objective Evolutionary Algorithms (MOEAs) have shown a great success in approximating the PF over more than two decades. Unlike classical solution approaches, MOEAs are characterized by their ability to provide the user with an approximation of the PF in a single run in addition to their insensitivity to the geometrical features of the objective functions and the constraint ones. However, real world applications are usually complex and need further efforts to be solved. The complexity factors may include: the high-objective space dimensionality (many-objective problems), the high decision space dimensionality (large scale problems), the presence of time-dependent objectives and/or constraints (dynamic MOPs), the expensive evaluation of the objectives and/or constraints (expensive MOPs), the presence of uncertainty (stochastic MOPs), the hierarchy between the objectives (bi-level problems), the presence of a high number of constraints (highly constrained MOPs), the need for solution robustness, the incorporation of decision maker’s preferences, etc. During the last decade, many EMO (Evolutionary Multi-objective Optimization) works have been proposed to handle these complexity factors of MOPs. Even more recently, some researchers have proposed some evolutionary approaches that tackle multiple complexity factors simultaneously. The main challenge in such kind of works is how to handle the interaction between the evolutionary search process and the complexity factor(s) to come up with an interesting PF. The main goal of this special issue is to further develop the EMO research field towards solving highly complex MOPs using evolutionary computation and computational intelligence techniques.

 

II. THEMES

In this special issue, we invite researchers to submit papers that address the issue of multi-objective optimization with one or several complexity factors using evolutionary computation and computational intelligence approaches. The submitted papers should address at least one complexity factor among the following ones (but are not limited to):

- High number of objectives and/or constraints,

- High number of decision variables,

- Time-dependent objectives and/or constraints,

- Computationally expensive objectives and/or constraints,

- The presence of uncertainty,

- The presence of hierarchy between the objectives,

- The need for robustness,

- The need for innovization,

- The need for decision maker’s preferences incorporation.

 

III. SUBMISSION

The manuscripts should be prepared according to the “Guide for Authors” section of the journal found at: https://www.elsevier.com/journals/swarm-and-evolutionary-computation/2210-6502/guide-for-authors/ and submission should be done through the journal submission website: https://www.evise.com/profile/#/SWEVO/login/ by clearly noting “Advances in EMO Special Issue Paper” as comments to the Editor-in-Chief. Each submitted paper will be reviewed by at least three expert reviewers. Submission of a paper will be held to imply that it contains original unpublished work and is not being submitted for publication elsewhere.

 

IV. IMPORTANT DATES

The important dates are the following:

- Paper submission: May 30, 2017 (AOE).

- First round decision: July 30, 2017.

- Major revision due: August 30, 2017 (AOE).

- Final decision: September 30, 2017.

- Final manuscript due: October 15, 2017.