There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.

/pdf/on-the-performance-of-artificial-bee-colony-abc-algorithm-5234goeamq.pdf

On the performance of artificial bee colony (ABC) algorithm

A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented.

Multiobjective evolutionary algorithms: A survey of the state of the art

Over the last three decades, a large number of evolutionary algorithms have been developed for solving multi-objective optimization problems. However, there lacks an upto-date and comprehensive software platform for researchers to properly benchmark existing algorithms and for practitioners to apply selected algorithms to solve their real-world problems. The demand of such a common tool becomes even more urgent, when the source code of many proposed algorithms has not been made publicly available. To address these issues, we have developed a MATLAB platform for evolutionary multi-objective optimization in this paper, called PlatEMO, which includes more than 50 multiobjective evolutionary algorithms and more than 100 multi-objective test problems, along with several widely used performance indicators. With a user-friendly graphical user interface, PlatEMO enables users to easily compare several evolutionary algorithms at one time and collect statistical results in Excel or LaTeX files. More importantly, PlatEMO is completely open source, such that users are able to develop new algorithms on the basis of it. This paper introduces the main features of PlatEMO and illustrates how to use it for performing comparative experiments, embedding new algorithms, creating new test problems, and developing performance indicators. Source code of PlatEMO is now available at: http://bimk.ahu.edu.cn/index.php?s=/Index/Software/index.html.

PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum]

Optimization techniques are applied widely in iron and steel making process to solve complicated process-related problems. These methods and the models created through them are regularly used in this field to find out the optimum operating conditions in terms of cost, quality, quantity and effectiveness of the process. The various blast furnace parameters like burden distribution, oxygen enrichment, productivity improvement, composition of the top gas, quality of hot metal production, etc., are very difficult to effectively optimize. In recent times data-driven models of diverse nature have been quite successfully applied for this purpose, where evolutionary approaches have made a significant impact in simultaneous optimization of multiple number of objectives in problems related to the iron and steel making industry. In this chapter implementation of various evolutionary strategies are discussed, which are recently applied in this domain to tackle some real-world problems.

Data-Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning

The assessed phase diagram of Ti-C system shown in Fig. 1is taken from [1998Oka]. Other recent work was done on thissystem by [1995Alb], [1996Sei1], and [1996Jon]. The systemconsists of two terminal solids αTi and βTi and a refractorymonocarbide TiC. Since there is a tendency of carbon orderingat the compositions below stoichiometry, the phase Ti

The Ti-V-C system (titanium - vanadium - carbon)

Molecular sequence alignment isoneofthemost essential toolsofthemolecular biology. Itpermits totrack changes andsimilarities between molecular sequences. Inthis paperthemolecular sequence alignment problem isformulated suitable foranEvolutionary Algorithm (EA), andtwoproblem instances aresolved usingGeneralized Differential Evolution 3 (GDE3), whichisageneral purpose EA.Regardless ofrelatively large numberofdecision variables, theinstances weresolvable andresults werecomparable tothosebysequence alignment solvers incomparison.

https://ieeexplore.ieee.org/iel5/4222970/4222971/04223020.pdf

Solving theMolecular Sequence Alignment Problem withGeneralized Differential Evolution 3(GDE3)

The design of NaCl(B1) and CsCl(B2) lattices are carried out in this work using multi-objective genetic algorithms, generalizing it for all the compounds possessing B1 and B2 crystal lattices. Three objectives, fracture toughness, density and energy of the system, were taken into the purview of this investigation, where the first objective is maximized and the remaining two are minimized. The interionic potential model used is a combination of Born Mayer and Coulomb potentials. The objectives are computed as a function of the ionic masses along with the inter-ionic distance. The fracture toughness studies are carried out by molecular dynamics studies (in which dependence on loading rate, crack size and electronegativity are also studied) and multi-objective optimization problem is solved by the well known multi-objective optimization algorithm NSGA II.

Multi-Objective Materials Design by Genetic Algorithms—Generalized for B1 and B2 Ionic Structures

Induction smelting process, ISP, is a direct smelting process in which self-fluxed composite pellets (made of iron ore, brown coal and lime) are melted in a single phase induction furnace in conjunction with top blown oxygen. A mass transfer model to predict the concentration of iron oxide, has been developed for this process. Transient mass balance equation with complex boundary conditions has been worked out by adopting a relatively new approach known as the cell model approach. These equations have been solved numerically using finite difference technique to obtain the iron oxide concentration profile in the induction smelting process. Numerical and experimental results obtained for the iron oxide concentration show a reasonable agreement with each other

Nirupam Chakraborti

Papers

Data-Driven Optimization of Blast Furnace Iron Making Process Using Evolutionary Deep Learning

The Ti-V-C system (titanium - vanadium - carbon)

Solving theMolecular Sequence Alignment Problem withGeneralized Differential Evolution 3(GDE3)

Multi-Objective Materials Design by Genetic Algorithms—Generalized for B1 and B2 Ionic Structures

Prediction of an iron oxide concentration in the induction smelting process