Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems

Metaheuristic algorithms have been widely used to solve diverse kinds of optimization problems. For an optimization problem, population initialization plays a significant role in metaheuristic algorithms. These algorithms can influence the convergence to find an efficient optimal solution. Mainly, for recognizing the importance of diversity, several researchers have worked on the performance for the improvement of metaheuristic algorithms. Population initialization is a vital factor in metaheuristic algorithms such as PSO and DE. Instead of applying the random distribution for the initialization of the population, quasirandom sequences are more useful for the improvement the diversity and convergence factors. This study presents three new low-discrepancy sequences named WELL sequence, Knuth sequence, and Torus sequence to initialize the population in the search space. This paper also gives a comprehensive survey of the various PSO and DE initialization approaches based on the family of quasirandom sequences such as Sobol sequence, Halton sequence, and uniform random distribution. The proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO) and DE (DE-TO, DE-WE, and DE-KN) have been examined for well-known benchmark test problems and training of the artificial neural network. The finding of our techniques shows promising performance using the family of low-discrepancy sequences over uniform random numbers. For a fair comparison, the approaches using low-discrepancy sequences for PSO and DE are compared with the other family of low-discrepancy sequences and uniform random number and depict the superior results. The experimental results show that the low-discrepancy sequences-based initialization performed exceptionally better than a uniform random number. Moreover, the outcome of our work presents a foresight on how the proposed technique profoundly impacts convergence and diversity. It is anticipated that this low-discrepancy sequence comparative simulation survey would be helpful for studying the metaheuristic algorithm in detail for the researcher.

A General-Purpose Uniform Random Number Package

As computer capacities and simulation technologies advance, simulation has become the method of choice for modeling and analysis. The fundamental advantage of simulation is that it can tolerate far less restrictive modeling assumptions, leading to an underlying model that is more reflective of reality and thus more valid, leading to better decisions. Simulation studies are typically preceded by transforming in a more or less complicated way of a sequence of numbers between 0 and 1 produced by a pseudorandom generator into an observation of the measure of interest. Random numbers are a fundamental resource in science and technology. A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, random number generators also play an important role in cryptography and in the blockchain ecosystem. All samples of the sequence are generated independently of each other, and the value of the next sample in the sequence cannot be predicted, regardless of how many samples have already been produced.

IMPLEMENTASI METODE FUZZY MAMDANI DAN LINEAR CONGRUENTIAL GENERATOR (LCG) PADA GAME HIDDEN OBJECT

Game dewasa ini telah menjadi populer di kalangan masyarakat, sebagai suatu bentuk hiburan, mendukung interaksi sosial antara mereka. Game atau permainan sejatinya dibuat untuk digunakan sebagai sarana menghibur. Game hidden object merupakan salah satu konsep game yang ada pada genre game puzzle. Game puzzle merupakan salah satu permainan yang dapat mengasah otak dan sangat menantang. Metode Linear Congruential Genetator (LCG) digunakan untuk membangkitkan bilangan acak dengan distribusi uniform. Random number atau bilangan acak adalah sebuah bilangan yang dihasilkan dari sebuah proses, yang keluarannya tidak dapat diprediksi. Logika Fuzzy adalah peningkatan dari logika Boolean yang mengenalkan konsep kebenaran sebagian. Fuzzy Mamdani merupakan salah satu model dalam metode fuzzy yang sudah umum digunakan. Fuzzy mamdani sering juga dikenal dengan nama Metode Max-Min. Selain itu metode fuzzy mamdani terkenal sederhana dan tidak banyak proses komputasi yang dilakukan dimana proses komputasi dapat membuat sistem akan berjalan lebih lama. Penelitian ini berfokus pada implementasi metode LCG untuk mengacak list object yang ada pada game secara random, dan metode Fuzzy mamdani untuk menghitung skor dan sebagai penentuan level. Fokus Dari penelitian adalah untuk mencai tahu apakah metode LCG dan juga metode fuzzy mamdani dapat diimplemntasikan dengan baik pada game dengan konsep hidden object. Dari hasil implementasi fuzzy mamdani dan LCG didapati bahwa fuzzy mamdani memiliki tingkat keakuratan perhitungan sebesar 98,5% sedangkan LCG memiliki keakuratan pengacakan list objek sebesar 100%. Sehingga dapat ditarik kesimpulan bahwa metode fuzzy mamdani dan LCG dapat di implementasikan dengan baik pada game dengan konsep hidden object. Kata kunci: Game, Hidden object, LCG, Fuzzy Mamdani

Study of dynamic reaction of tubulins in microtubules. A qm/mm simulation

In this model one-dimensional microtubule is fixed at one of the two and simulated while the opposite end is allowed for growing in random situation. By this study at each step one tubulin has been added to the length for growing microtubule length. Computationally this can be done through generating a uniform random number between (0, 1). Microtubules are demonstrated as straight macromolecules consist of the linear chains of tubulin subunits in the length. QM/MM simulation has been applied to study dynamic instability of the microtubule length. It has been calculated a correct dimension around 10-6 meter of microtubules length consist of around 1650 tubulin dimers. Microtubule growth rate is related to the soluble tubulin dimer concentration and for all results shown here, simulation of any single condition was run 5–10 times.

Uniform Random Number Generation With Jumping Facilities

A facility for generating sequences of pseudorandom numbers is a fundamental part of computer simulation systems. Furthermore, multiple independent streams of random numbers are often required in simulation studies, for instance, to facilitate synchronization for variance-reduction purposes, and for making independent replications. A portable set of software utilities is described for uniform random-number generation. It provides for multiple generators (streams) running simultaneously, and each generator (stream) has its sequence of numbers partitioned into many long disjoint contiguous substreams. Simple procedure calls allow the user to make any generator “jump” ahead/back v steps (random numbers). Implementation issues are discussed. An efficient and portable code is also provided to implement the package. The basic underlying generator CMRG (combined multiple recursive generator) combines two multiple recursive random number generators with a period length of approximately 2191 (≈ 3.1× 1057), good speed, and excellent theoretical properties.