Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization

The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests.This research was supported by the Spanish Ministry of Science, Innovation and Universities and the Research State Agency under Grant RTI2018-098156-B-C54 cofinanced by FEDER funds and the Ministry of Science and Innovation and the Research State Agency under Grant PID2020-120213RB-I00 cofinanced by FEDER funds

Population-based JPEG Image Compression: Problem Re-Formulation

The JPEG standard is widely used in different image processing applications. One of the main components of the JPEG standard is the quantisation table (QT) since it plays a vital role in the image properties such as image quality and file size. In recent years, several efforts based on population-based metaheuristic (PBMH) algorithms have been performed to find the proper QT(s) for a specific image, although they do not take into consideration the user's opinion. Take an android developer as an example, who prefers a small-size image, while the optimisation process results in a high-quality image, leading to a huge file size. Another pitfall of the current works is a lack of comprehensive coverage, meaning that the QT(s) can not provide all possible combinations of file size and quality. Therefore, this paper aims to propose three distinct contributions. First, to include the user's opinion in the compression process, the file size of the output image can be controlled by a user in advance. Second, to tackle the lack of comprehensive coverage, we suggest a novel representation. Our proposed representation can not only provide more comprehensive coverage but also find the proper value for the quality factor for a specific image without any background knowledge. Both changes in representation and objective function are independent of the search strategies and can be used with any type of population-based metaheuristic (PBMH) algorithm. Therefore, as the third contribution, we also provide a comprehensive benchmark on 22 state-of-the-art and recently-introduced PBMH algorithms on our new formulation of JPEG image compression. Our extensive experiments on different benchmark images and in terms of different criteria show that our novel formulation for JPEG image compression can work effectively.Comment: 39 pages, this paper is submitted to the related journa

Optimising Sidelobes and Grating Lobes in Frequency Modulated Pulse Compression

Pulse compression is a signal processing technique used in radar systems to achieve long range target detection capability, which is a characteristic of long duration pulse, without compromising the high range resolution capability, which is characteristic of a short duration pulse. For this, the received signal at the receiver is compressed by a matched filter to produce a compressed version of the signal for better resolution. As the range resolution is inversely proportional to the bandwidth, high range resolution is ensured by using a transmitted pulse of greater bandwidth. LFM pulse is better used than a constant frequency pulse because of its larger bandwidth. The bandwidth of a signal can further be increased by taking a train of pulses with the center frequency of consecutive pulses stepped by some frequency step ∆f. A train of pulses with each pulse of duration T, separated by time Tr gives rise to grating lobes in its autocorrelation function (ACF), when T∆f>1. ACF of a single LFM pulse has also sidelobes of its own. Grating lobes and sidelobes may act individually or together to mask smaller targets in close vicinity of a larger target, hence are needed to be reduced. In the first part of the work, two optimization algorithms called Clonal Particle Swarm Optimization and Differential Evolution has been used to find out specific windows that shape an LFM pulse to reduce the ACF sidelobes to their optimal minima. Temporal windows has been found out using three coefficient window expressions and four coefficient window expressions. Resulting windows have been found to reduce sidelobes to an extent which was not possible by the classical windows. Grating lobes in a train of pulses can be lowered by the use of LFM pulses instead of fixed frequency pulses. Nullification of the ACF grating lobes is possible when T, ∆f, and B satisfy a special relationship that puts the ACF nulls due to a single LFM pulse exactly at the positions of grating lobes. The scheme is valid if and only if Tr/T>2, which restricts the extent of increase in bandwidth by limiting the number of frequency steps for a signal of particular time duration. In the second part of the work presented in this thesis, a scheme has been proposed that allows to accommodate more bandwidth by taking Tr/T=1. It allows more number of pulses within the same signal time, and hence more number of frequency stepping to result a larger total bandwidth

Advances in Spacecraft Attitude Control

Spacecraft attitude maneuvers comply with Euler's moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research. This book is meant for basic scientifically inclined readers, and commences with a chapter on the basics of spaceflight and leverages this remediation to reveal very advanced topics to new spaceflight enthusiasts. The topics learned from reading this text will prepare students and faculties to investigate interesting spaceflight problems in an era where cube satellites have made such investigations attainable by even small universities. It is the fondest hope of the editor and authors that readers enjoy this book

Advances in Spacecraft Attitude Control

Spacecraft attitude maneuvers comply with Euler's moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research. This book is meant for basic scientifically inclined readers, and commences with a chapter on the basics of spaceflight and leverages this remediation to reveal very advanced topics to new spaceflight enthusiasts. The topics learned from reading this text will prepare students and faculties to investigate interesting spaceflight problems in an era where cube satellites have made such investigations attainable by even small universities. It is the fondest hope of the editor and authors that readers enjoy this book

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Design, Analysis, And Optimization Of Diffractive Optical Elements Under High Numerical Aperture Focusing

The demand for high optical resolution has brought researchers to explore the use of beam shaping diffractive optical elements (DOEs) for improving performance of high numerical aperture (NA) optical systems. DOEs can be designed to modulate the amplitude, phase and/or polarization of a laser beam such that it focuses into a targeted irradiance distribution, or point spread function (PSF). The focused PSF can be reshaped in both the transverse focal plane and along the optical axis. Optical lithography, microscopy and direct laser writing are but a few of the many applications in which a properly designed DOE can significantly improve optical performance of the system. Designing DOEs for use in high-NA applications is complicated by electric field depolarization that occurs with tight focusing. The linear polarization of off-axis rays is tilted upon refraction towards the focal point, generating additional transverse and longitudinal polarization components. These additional field components contribute significantly to the shape of the PSF under tight focusing and cannot be neglected as in scalar diffraction theory. The PSF can be modeled more rigorously using the electromagnetic diffraction integrals derived by Wolf, which account for the full vector character of the field. In this work, optimization algorithms based on vector diffraction theory were developed for designing DOEs that reshape the PSF of a 1.4-NA objective lens. The optimization techniques include simple exhaustive search, iterative optimization (Method of Generalized Projections), and evolutionary computation (Particle Swarm Optimization). DOE designs were obtained that can reshape either the transverse PSF or the irradiance distribution along the optical axis. In one example of transverse beam shaping, all polarization components were simultaneously reshaped so their vector addition generates a focused flat-top square irradiance pattern. Other designs were obtained that can be used to narrow the axial irradiance distribution, giving a focused beam that is superresolved relative to the diffraction limit. In addition to theory, experimental studies were undertaken that include (1) fabricating an axially superresolving DOE, (2) incorporating the DOE into the optical setup, (3) imaging the focused PSF, and (4) measuring aberrations in the objective lens to study how these affect performance of the DOE

An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data

Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data. A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used. PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods