Swarm and Evolutionary Computation

英文简介

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.Applications: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.

中文简介

为了解决复杂的现实世界问题，科学家们多年来一直在研究自然过程和生物——无论是模型还是比喻。优化是许多自然过程的核心，包括达尔文进化论、社会群体行为和觅食策略。在过去的几十年中，自然启发的搜索和优化算法领域有了显著的发展。目前，这些技术被应用于各种问题，从科学研究到工商业。目前主要构成这一领域的两大算法家族是进化计算方法和群智能算法。虽然这两个算法家族通常都致力于解决搜索和优化问题，但它们肯定不是等价的，而且每个算法都有其独特的特点。相互增强的性能使得强大的混合算法能够解决许多难以解决的搜索和优化问题。关于期刊《群计算与进化计算》是同类刊物中第一本经过同行评议的刊物，旨在报道基于群和进化算法原理的自然启发智能计算领域的最新研究和发展。它出版先进的、创新的和跨学科的研究，涉及理论、实验和实践方面的两种范式及其杂交。群和进化计算致力于及时出版非常高质量的，同行评议的，原创的文章，推进所有方面的进化计算和群体智能的艺术状态。此外，我们亦欢迎市民就最新的研究课题发表意见，并提供新颖和有趣的应用。感兴趣的题目：感兴趣的课题包括但不限于：遗传算法、遗传规划、进化策略、进化规划、差异进化、人工免疫系统、粒子群、蚁群、细菌觅食、人工蜜蜂、萤火虫算法、和谐搜索、人工生命、数字生物、分布算法估计、随机扩散搜索、量子计算、纳米计算、膜计算、以人为中心的计算、算法杂交、模因计算、自主计算、自组织系统、组合、离散、二进制、约束、多目标、多模态、动态、大规模优化。应用程序：此外，该期刊还通过在各领域和行业发表有趣和新颖的应用来促进工业的吸收，这些领域和行业处理具有挑战性的搜索和优化问题（但不限于）：航空航天、系统和控制、机器人、电力系统、通信工程、业务研究和决策科学、金融服务和工程、(管理)信息系统、商业情报、互联网计算、传感器、图像处理、计算化学、制造、结构和机械设计、生物信息学、计算生物学、数学和计算心理学；认知神经科学，脑机接口，未来计算设备，非线性统计和应用物理，环境建模和软件。

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