International Journal of Mathematical Modelling and Numerical Optimisation


Mathematical Optimization Physics & Mathematics (General)

IJMMNO proposes and fosters discussion on the mathematical modelling, algorithm development, numerical methods, computer simulations and numerical optimisation as well as applications and case studies. As a fully refereed international journal, IJMMNO will

* focus on the multidisciplinary and cross-disciplinary research
* communicate new algorithms and techniques in mathematical modelling and numerical optimisation
* promote real-world applications in all major areas of sciences, engineering and industry

IJMMNO publishes full-length original research papers, with a section dedicated to short research notes and concise case studies using modelling, simulation and optimisation.

IJMMNO aims to cover a wide range of research areas in applied mathematics, numerical algorithms, mathematical modelling and optimisation techniques and applications, including but not limited to

* Mathematical modelling and mathematical analysis
* Review, analysis and comparison of mathematical and numerical models
* New mathematical models and novel algorithms
* Numerical algorithm formulation and analysis (evolutionary, stochastic, nature-inspired and higher-level algorithms)
* Modelling of physical, chemical, biological, environmental and industrial processes
* Numerical analysis, error estimation, and stability
* Numerical methods, including solution of PDEs, finite difference methods, finite volume methods, finite element methods, meshless and element-free methods, extended finite element methods, discrete element methods, boundary element methods, and smooth particle hydrodynamics, spectral methods, and others
* Computer simulations and visualisation
* Optimisation techniques (linear and nonlinear programming, stochastic search, nature-inspired algorithms and techniques such as particle swarm optimisation, simulated annealing, genetic algorithms, and other metaheuristic algorithms)
* Statistical simulations and techniques (Monte Carlo, Markov chain Monte Carlo, stochastic modelling and analysis, machine learning, Bayesian inference)
* Network modelling and theory (small-world networks, scale-free networks, power-law networks, financial networks, neural networks)
* Soft computing, natural computing, and bio-inspired computing with focus on algorithm development
* Review and comparison studies of algorithms and techniques (both conventional and unconventional)
* Multidisciplinary research combining algorithms, modelling and optimisation
* Case studies and applications in all areas of sciences, engineering and industry, including economics, earth sciences, environmental science, meteorology, and social sciences

Authors are invited to contribute original, unpublished, high-quality papers.
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