Información de la Revista
Swarm and Evolutionary Computation
Factor de Impacto:

Solicitud de Artículos
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.
Última Actualización Por Dou Sun en 2018-07-16
Special Issues
Special Issue on Quantum Inspired Swarm and Evolutionary Computing Algorithms for Optimization Problems
Día de Entrega: 2019-09-30

1. Aim and Scope Natural computing algorithm represent a very important field in computational intelligence, soft computing and optimization in a general sense. For this purpose, we noticed clearly that they attracted outstanding interest from many researchers across the globe. Indeed, past and ongoing research in this field covers an important group of subjects, from basic research to a large number of real-world applications. On the other hand, many research efforts in the field on quantum computing have been made since 1990, after the demonstration that computer based on principle of quantum mechanics can offer more processing power for some classes of problems. Typically, quantum computing is based on the principle of superposition, which states that a particle can be in two different states simultaneously, suggest that a high degree of parallelism can be achieved using this kind of computers. The superiority of quantum computing was demonstrated with few algorithms, namely the Shor's algorithm (used for factoring large number) and Grover's algorithm (used for searching databases). The integration of natural computing algorithm and quantum computing has become topic of increasing interest for both researchers and practitioners from academic fields and industry world-wide. It is foreseeable that quantum inspired swarm and evolutionary computing algorithms (QISWEVCA) will be one of the main approaches for the next generation of intelligent system and optimization research. In recent years, QISWEVCA has become a new hotspot of intelligent computing research. The combined package of QISWEVCA has demonstrated great benefits to industry and showed potential to be used in a wide variety of applications. Hence, the interest of researchers bends towards the recent development of QISWEVCA for different applications. 2. Themes The principle aim of this Special Issue is to assemble state-of-the-art contributions on the latest research and development, up-to-date issues, and challenges in the field of QISWEVCA. Proposed submission should be original, unpublished, and should present, novel-in-depth fundamental research contribution either from a methodological perspective or from an application point of view. The topics relevant to the special issue include (but not limited to): Optimization problems (Large scale optimization problems, Multi-modal optimization problems, Multi-objective optimization problems etc.). QISWEVCA for pattern recognition and machine vision. QISWEVCA for combinatorial optimization problems. QISWEVCA for Engineering design problems. QISWEVCA for Quadratic assignment problems. Natural language model inspired by QISWEVCA. QISWEVCA for robotics. Job-shop scheduling problems. QISWEVCA for big data analytics. QISWEVCA for data mining. QISWEVCA for intelligent vehicle communication. Error detection and correction using QISWEVCA. Knowledge discoveries using QISWEVCA. QISWEVCA for machine learning and deep learning. Real world applications.
Última Actualización Por Dou Sun en 2019-08-11
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