Journal Information
Swarm and Evolutionary Computation
Impact Factor:

Call For Papers
To tackle complex real world problems, scientists have been looking into natural processes and creatures - both as model and metaphor - for years. Optimization is at the heart of many natural processes including Darwinian evolution, social group behavior and foraging strategies. Over the last few decades, there has been remarkable growth in the field of nature-inspired search and optimization algorithms. Currently these techniques are applied to a variety of problems, ranging from scientific research to industry and commerce. The two main families of algorithms that primarily constitute this field today are the evolutionary computing methods and the swarm intelligence algorithms. Although both families of algorithms are generally dedicated towards solving search and optimization problems, they are certainly not equivalent, and each has its own distinguishing features. Reinforcing each other's performance makes powerful hybrid algorithms capable of solving many intractable search and optimization problems.

About the journal:
Swarm and Evolutionary Computation is the first peer-reviewed publication of its kind that aims at reporting the most recent research and developments in the area of nature-inspired intelligent computation based on the principles of swarm and evolutionary algorithms. It publishes advanced, innovative and interdisciplinary research involving the theoretical, experimental and practical aspects of the two paradigms and their hybridizations. Swarm and Evolutionary Computation is committed to timely publication of very high-quality, peer-reviewed, original articles that advance the state-of-the art of all aspects of evolutionary computation and swarm intelligence. Survey papers reviewing the state-of-the-art of timely topics will also be welcomed as well as novel and interesting applications.

Topics of Interest:
Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.

Furthermore, the journal fosters industrial uptake by publishing interesting and novel applications in fields and industries dealing with challenging search and optimization problems from domains such as (but not limited to): Aerospace, Systems and Control, Robotics, Power Systems, Communication Engineering, Operations Research and Decision Sciences, Financial Services and Engineering, (Management) Information Systems, Business Intelligence, internet computing, Sensors, Image Processing, Computational Chemistry, Manufacturing, Structural and Mechanical Designs, Bioinformatics, Computational Biology, Mathematical and Computational Psychology, Cognitive Neuroscience, Brain-computer Interfacing, Future Computing Devices, Nonlinear statistical and Applied Physics, and Environmental Modeling and Software.
Last updated by Dou Sun in 2017-03-05
Special Issues
Special Issue on Advances in Evolutionary Multi-objective Optimization
Submission Date: 2017-05-30

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: and submission should be done through the journal submission website: by clearly noting “Advances in EMO” 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.
Last updated by Dou Sun in 2017-03-05
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