Comparison of Geometric Optimization Methods with Multiobjective Genetic Algorithms for Solving Integrated Optimal Design Problems

In this paper, system design methodologies for optimizing heterogenous power devices in electrical engineering are investigated. The concept of Integrated Optimal Design (IOD) is presented and a simplified but typical example is given. It consists in finding Pareto-optimal configurations for the motor drive of an electric vehicle. For that purpose, a geometric optimization method (i.e the Hooke and Jeeves minimization procedure) associated with an objective weighting sum and a Multiobjective Genetic Algorithm (i.e. the NSGA-II) are compared. Several performance issues are discussed such as the accuracy in the determination of Pareto-optimal configurations and the capability to well spread these solutions in the objective space

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Cancer prediction using graph-based gene selection and explainable classifier

Several Artificial Intelligence-based models have been developed for cancer prediction. In spite of the promise of artificial intelligence, there are very few models which bridge the gap between traditional human-centered prediction and the potential future of machine-centered cancer prediction. In this study, an efficient and effective model is developed for gene selection and cancer prediction. Moreover, this study proposes an artificial intelligence decision system to provide physicians with a simple and human-interpretable set of rules for cancer prediction. In contrast to previous deep learning-based cancer prediction models, which are difficult to explain to physicians due to their black-box nature, the proposed prediction model is based on a transparent and explainable decision forest model. The performance of the developed approach is compared to three state-of-the-art cancer prediction including TAGA, HPSO and LL. The reported results on five cancer datasets indicate that the developed model can improve the accuracy of cancer prediction and reduce the execution time

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Genetic algorithm optimization applied to planar and wire antennas

Antenna design has grown more stringent and difficult over the years as the world becomes strictly a wireless environment. The inherent tradeoffs that exist between gain, radiation pattern, bandwidth, and physical size and the multiple parameters that must be considered make antenna design a lengthy and tedious process. Methods have been devised which automate this complex process of antenna optimization through the use of genetic algorithms, particle swarm optimization, and simulated annealing. Genetic algorithms are capable of handling a large number of design parameters and work for optimization problems that have discontinuous or non-differentiable multi-dimensional solution spaces, making them ideal for antenna optimization. In the present work, a genetic algorithm has been used for size reduction in microstrip patch antennas and design tradeoff optimization between beamwidth and gain in helical antennas. A method for reducing the size of microstrip patch antennas by up to 75% by removing rectangular and circular slots from the metal of the microstrip patch is presented. A solid patch antenna that resonates at 10 GHz is forced to resonant at 6 GHz through the removal of the different shaped slots. Given the number and shape of the slots, the genetic algorithm is used to optimize the size and location of the slot on the patch. The designs are obtained by interfacing the genetic algorithm and Ansoft High Frequency System Simulator (HFSS) and validated through design, construction, and testing. High gain, with broad half-power beamwidths (HPBW) is traditionally extremely difficult to achieve due to the inherent tradeoff between the two. A genetic algorithm has been applied to design a helical antenna with a gain of 10 dB and HPBWs of 60 degrees. In order to achieve this, three physical parameters of the helix have been changed, namely the pitch, helix radius, and the ground plane geometry. The second objective is to create an antenna that displays different HPBWs in the two radiation planes. This could be extremely useful in many communication environments and there is yet no existing method to achieve this. The genetic algorithm produced a helical antenna that shows a 19 degrees difference in HPBW between the two radiation planes, while still displaying a 7 dB gain and low side lobes. Numerical Electromagnetic Code 4 (NEC4) is used, and a method of communication between MATLAB and NEC4 has been developed to make the genetic algorithm optimization possible

Speeding-Up Expensive Evaluations in High-Level Synthesis Using Solution Modeling and Fitness Inheritance

High-Level Synthesis (HLS) is the process of developing digital circuits from behavioral specifications. It involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary Algorithms have been already effectively applied to HLS to find good solution in presence of conflicting design objectives. In this paper, we present an evolutionary approach to HLS that extends previous works in three respects: (i) we exploit the NSGA-II, a multi-objective genetic algorithm, to fully automate the design space exploration without the need of any human intervention, (ii) we replace the expensive evaluation process of candidate solutions with a quite accurate regression model, and (iii) we reduce the number of evaluations with a fitness inheritance scheme. We tested our approach on several benchmark problems. Our results suggest that all the enhancements introduced improve the overall performance of the evolutionary search

Development of a Multi-Objective Optimization Capability for Heterogeneous Light Water Reactor Fuel Assemblies

As pressure grows on developed nations to move away from fossil fuel-based energy sources, so does the potential for nuclear energy to make its resurgence. However, the complex nature of the design process in nuclear engineering and a regulatory culture of ever-increasing safety standards create unique challenges to the nuclear industry. As in many engineering disciplines, the question is one of trade-offs between safety, performance, cost, and time required to develop the design from paper to real life operation. The possibilities facing a designer are virtually unlimited, with fuel choice, layout and operating conditions just three of the many categories which interact with one another in a highly non-linear manner, making it difficult to quantitatively define these trade-offs. Deciding upon an ‘optimal’ design is therefore traditionally done through expert judgement and an iterative design process. Mathematical optimization methods offer a more formal way to optimize designs by employing algorithms to explore the myriad of possibilities in a methodical manner which can yield increased performance over expert designs. In this thesis, an extensive review of the literature revealed gaps which present opportunities for novel research. Two new algorithms are created with the ability to solve optimization problems with multiple objectives simultaneously without requiring weighting or bias from the designer. They are then applied to a series of problems drawn from both the literature and real world designs. The results demonstrate the algorithms’ effectiveness and robustness as well as their ability to handle complex multi-physics problems with reasonably low computational requirements. This research offers an original and effective tool for performing optimization on nuclear fuel assembly design problems and has advanced the state of the art in both multi-objective optimization and its application to the nuclear engineering industry