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

The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

A comprehensive review of firefly algorithms

Finding energy sources to satisfy the world's growing demand is one of society's foremost challenges for the next half-century. The challenge in converting sunlight to electricity via photovoltaic solar cells is dramatically reducing $/watt of delivered solar electricity. In this context the sun trackers are such devices for efficiency improvement. The diurnal and seasonal movement of earth affects the radiation intensity on the solar systems. Sun-trackers move the solar systems to compensate for these motions, keeping the best orientation relative to the sun. Although using sun-tracker is not essential, its use can boost the collected energy 10–100% in different periods of time and geographical conditions. However, it is not recommended to use tracking system for small solar panels because of high energy losses in the driving systems. It is found that the power consumption by tracking device is 2–3% of the increased energy. In this paper different types of sun-tracking systems are reviewed and their cons and pros are discussed. The most efficient and popular sun-tracking device was found to be in the form of polar-axis and azimuth/elevation types.

A review of principle and sun-tracking methods for maximizing solar systems output

The use of central receiver system (CRS) for electricity production promises to be one of the most viable options to replace fossil fuel power plants. Indeed, research and development activities on its basic subsystems have been booming rapidly since 1980s. This paper reviews the most important studies on the major components of central receiver solar thermal power plants including the heliostat field, the solar receiver and the power conversion system. After an overview of Concentrating Solar Power (CSP) technology, current status and applications of the CRSs are highlighted. Next, a detailed literature survey of existing design comprising optical, thermal and thermodynamic analysis, and techniques used to assess components have been arranged. This is followed by experimental investigations in which design concepts are established. The last section contains recent subsequent improvement of such key components as heliostat, receiver and hybrid solar gas turbine that are boosting in many R&D activities merging international collaboration during the past 30 years.

A review of studies on central receiver solar thermal power plants

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

Gray wolf optimizer (GWO) is employed in solving the optimal reactive power dispatch (ORPD) problems.Three case studies have been utilized to show the effectiveness of GWO.GWO able to find minimum loss and voltage deviation solution than those determined by other techniques. This paper presents the use of a new meta-heuristic technique namely gray wolf optimizer (GWO) which is inspired from gray wolves' leadership and hunting behaviors to solve optimal reactive power dispatch (ORPD) problem. ORPD problem is a well-known nonlinear optimization problem in power system. GWO is utilized to find the best combination of control variables such as generator voltages, tap changing transformers' ratios as well as the amount of reactive compensation devices so that the loss and voltage deviation minimizations can be achieved. In this paper, two case studies of IEEE 30-bus system and IEEE 118-bus system are used to show the effectiveness of GWO technique compared to other techniques available in literature. The results of this research show that GWO is able to achieve less power loss and voltage deviation than those determined by other techniques.

http://umpir.ump.edu.my/9087/1/fkee-2015-herwan-Using%20The%20Gray%20Wolf%20Optimizer%20abs.pdf

Using the gray wolf optimizer for solving optimal reactive power dispatch problem

Non-Imaging, Focusing Heliostat

Report of the first prototype of non-imaging focusing heliostat and its application in high temperature solar furnace

This paper presents an application of Firefly Algorithm (FA) in determining the optimal location and size of Distributed Generation (DG) in distribution power networks. FA is a meta-heuristic algorithm which is inspired by the flashing behavior of fireflies. The primary purpose of firefly's flash is to act as a signal system to attract other fireflies. In this paper, IEEE 69-bus distribution test system is used to show the effectiveness of the FA. Comparison with other method is also given.

Optimal allocation and sizing of Distributed Generation in distribution system via Firefly Algorithm

This paper presents a particle swarm optimization approach for the placement of distributed generation (DG) in the distribution system. DG installation in the distribution system is very useful in reducing the line losses, as well as improving the voltage profiles. The proposed method combines particle swarm optimization and the Newton-Raphson load flow method to determine the location and size of the DG. The objective function to be minimized in this problem is the total power losses of the system. The proposed approach has been tested on IEEE 69-bus distribution test system and the program was simulated using MATLAB software. Test results show the effectiveness of the developed algorithm.

Omar Aliman

Papers

Using the gray wolf optimizer for solving optimal reactive power dispatch problem

Non-Imaging, Focusing Heliostat

Report of the first prototype of non-imaging focusing heliostat and its application in high temperature solar furnace

Optimal allocation and sizing of Distributed Generation in distribution system via Firefly Algorithm

Distributed generation installation using particle swarm optimization