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 2017-08-07
Special Issues
Special Issue on Differential Evolution
Submission Date: 2018-04-01

Differential Evolution (DE) is a population-based metaheuristic characterised by moving operators that require the support of other solutions and a one-to-one replacement scheme. After its definition in 1995, as a modified Nelder-Mead algorithm to empirically solve an industrial problem, DE has been broadly used and investigated. A plenty of applications of DE as well as many variants has been proposed over the past two decades. In particular, multiple implementations aiming at enhancing upon the original DE performance on some classes of problems. These enhancing implementations range from minor changes, such as a randomisation of a parameter, to major redesigns of parts of the DE structures. Examples of enhancing schemes include the integration of local search within some section of DE, the employment of multiple search operators, increase in the exploitation of the search operators, the randomisation of one or more parts of the algorithmic structure, and adaptive/self-adaptive rules in various phases of the optimisation. It must be remarked that the enhanced DE implementations never modify the one-to-one survivor selection that is a main trait of the DE algorithm. The DE algorithm has been used on a large number of real-world applications in various fields, including for example design engineering, economics and bioinformatics. Some valuable studies attempt to integrate the problem information into modified DE in order to tailor the search to the specific problem. This special issue aims at collecting recent advances in DE and covers different aspect of this algorithmic structure. We welcome theoretical studies, implementation studies, novel implementation, and applications of DE. Authors are invited to submit their original and unpublished work in the areas including (but not limited to) the following: - Theoretical analysis of the search mechanism, complexity of DE - Adaptation and tuning of the control parameters of DE - Development of new vector perturbation techniques for DE - Adaptive mixing of the perturbation techniques - Balancing explorative and exploitative tendencies in DE and memetic DE - DE for finding multiple global optima - DE for noisy and dynamic objective functions - DE for multi-objective optimization - Robust DE Variants - Rotationally Invariant DE - Constraints handling with DE - DE for high-dimensional optimization - DE-variants for handling mixed-integer, discrete, and binary optimization problems - Hybridization of DE with other search methods - Hybridization with Paradigms such as Neuro-fuzzy, Statistical Learning, Machine Learning, etc. - Development of challenging problem sets for DE - Applications of DE in any domain
Last updated by Dou Sun in 2017-08-07
Special Issue on Benchmarking Multi and Many Objective Evolutionary Algorithms on Challenging Test Problems
Submission Date: 2018-06-01

Multi-objective optimization problems (MOPs) are commonly encountered in real-world applications. Multi-objective evolutionary algorithms (MOEAs) are effective in solving MOPs with a few objectives. In recent years, it was observed that MOEAs face difficulties in solving MOPs with four or more objectives. These problems are known as Many-objective Optimization Problems (MaOPs). Challenges faced by population-based algorithms when solving MaOPs include the inability of dominance based MOEAs to converge to the Pareto front with good diversity, high computational complexity in the computation of performance indicators, and the difficulties in decision making, visualization, and understanding the relationships between objectives and articulated preferences. To tackle these issues, numerous many objective evolutionary algorithms (MaOEAs) have been developed and evaluated on standard benchmark problems. The objective of this special issue is to evaluate MOEAs as well as the recently developed MaOEAs on newly designed challenging MaOPs presented in the following technical report: Hui Li, Kalyanmoy Deb, Qingfu Zhang and P N Suganthan, “Challenging Novel Many and Multi-Objective Bound Constrained Benchmark Problems,”Technical Report, 2017. (TR updated on 11th Jan 2018. Codes updated on 5th Jan 2018. You can do test runs and give us feedback, if you find any problem) It is expected that to solve these challenging problems effectively, the state of the art algorithms will have to be improved. Hence, while including the novel problems also in their evaluation studies, researchers are invited to present their original works on the following multi and many objective optimization related issues (but not limited to): Algorithm design issues such as selection rules, reproduction, mating restriction, and so on. - Performance indicators - Objective reduction - Visualization techniques - Preference Articulation - Decision making methods - Hybridized algorithms - Development of further challenging Benchmark problems - Many-objective real-world optimization problems - Model learning - Estimating knee, nadir points - Constraint handling methods - EAs for MCDM
Last updated by Dou Sun in 2018-02-21
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