Lightning search algorithm: a comprehensive survey

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper

Performance comparison of various probability gate assisted binary lightning search algorithm

Recently, many new nature-inspired optimization algorithms have been introduced to further enhance the computational intelligence optimization algorithms. Among them, lightning search algorithm (LSA) is a recent heuristic optimization method for resolving continuous problems. It mimics the natural phenomenon of lightning to find out the global optimal solution around the search space. In this paper, a suitable technique to formulate binary version of lightning search algorithm (BLSA) is presented. Three common probability transfer functions, namely, logistic sigmoid, tangent hyperbolic sigmoid and quantum bit rotating gate are investigated to be utilized in the original LSA. The performances of three transfer functions based BLSA is evaluated using various standard functions with different features and the results are compared with other four famous heuristic optimization techniques. The comparative study clearly reveals that tangent hyperbolic transfer function is the most suitable function that can be utilized in the binary version of LSA

Transient Analysis of Quasi Oppositional Based Lightning Search Algorithm Optimized PID Controller in Isolated Small Hydro Power Plant

In this paper, Small Hydro Plant (SHP) of 1.3 MW is simulated with two conventional PID controllers in excitation system and governor to enhance the capability to handle the transiency of the generator. Excitation voltage control and turbine speed control are the two basic control schemes, to regulate reactive power or terminal voltage and real power or frequency respectively. The selection parameters of the PID controllers are significant to enhance the performance of the system. Quasi Oppositional Base­­d Lightning Search Algorithm (QOLSA) is validated in this paper to optimize the PID controllers over LSA and PSO. Renewable energy source like SHP is environment friendly and very imperative to meet the vigorously growing load demand. The simulation of the SHP is established in MATLAB/SIMULINK environment. Finally, QOLSA optimized PID controller contributes better control in terminal voltage and power over LSA and PSO algorithms.  Citation: Kaushaley, S., and Shaw, B. (2018). Transient Analysis of Quasi Oppositional Based Lightning Search Algorithm Optimized PID Controller in Isolated Small Hydro Power Plant. Trends in Renewable Energy, 4, 34-43. DOI: 10.17737/tre.2018.4.3.004

Optimal allocation of DSTATCOM and PV array in distribution system employing fuzzy-lightning search algorithm

Increased electric energy demand is the major factor that cracks the researchers to focus on electrical energy demand to meet the challenge. Accordingly, the power loss should be minimized. Due to this, Distribution Flexible Alternating Current Transmission System (DFACTS) and Distributed Generation (DG) must be placed in an appropriate way in distribution systems. Fuzzy-Lightning Search Algorithm (FLSA) has been proposed to minimize the power loss in the radial distribution network by solving the optimal placement problem of Distribution STATic COMpensator (DSTATCOM) as DFACTS device and photo voltaic (PV) array unit as DG. The improved voltage profile values, less power loss and reduced stability problems have been attained by using FLSA. The proposed FLSA technique is evaluated through IEEE 30-bus system with MATLAB software. The power flow investigation has been done by Newton Raphson method. Optimum siting of DSTATCOM and PV array induced outstandingly decreased the losses to 7.0073 kW. The accomplished objectives and results have validated the FLSA is more advantageous than other optimization techniques

Minimizing harmonic distortion impact cause by CS using meta heuristic technique

Non-linear load in the distribution system has caused negative impact to its power quality especially on harmonic distortion. Charging Station (CS) is a non-linear load that widely promoted with the aim to support the continuous usage of Electric Vehicle (EV). This research is focusing on optimal placement and sizing of multiple passive filter to mitigate harmonic distortion due to CS usage at distribution system. There are 6 units of CS which being placed in low voltage buses which indirectly will inject harmonic to the system during charging. Power system harmonic flow, passive filter, CS, battery and the analysis will be model in MATLAB. Multi-objective function which are weight summation approach (WSA) and Pareto Front are used to assist meta heuristic technique which is Modified Lightning Search Algorithm (MLSA) to identify optimum location and sizing of passive filter based on improvement on propose five parameters. From the result, the optimal placements and sizing of passive filter able to reduce the maximum Total Harmonic Distortion (THD) for voltage, current and apparent losses respectively. Therefore, the propose method is suitable to reduce harmonic distortion as well as apparent losses at distribution system with present of CS

Role of optimization algorithms based fuzzy controller in achieving induction motor performance enhancement.

Three-phase induction motors (TIMs) are widely used for machines in industrial operations. As an accurate and robust controller, fuzzy logic controller (FLC) is crucial in designing TIMs control systems. The performance of FLC highly depends on the membership function (MF) variables, which are evaluated by heuristic approaches, leading to a high processing time. To address these issues, optimisation algorithms for TIMs have received increasing interest among researchers and industrialists. Here, we present an advanced and efficient quantum-inspired lightning search algorithm (QLSA) to avoid exhaustive conventional heuristic procedures when obtaining MFs. The accuracy of the QLSA based FLC (QLSAF) speed control is superior to other controllers in terms of transient response, damping capability and minimisation of statistical errors under diverse speeds and loads. The performance of the proposed QLSAF speed controller is validated through experiments. Test results under different conditions show consistent speed responses and stator currents with the simulation results

Smart home energy management

The new challenges on Information and Communication Technologies (ICT) in Automatic Home Systems (AHS) focus on the methods useful to monitor, control, and optimize the data management flow and the use of energy. An AHS is a residential dwelling, in some cases with a garden or an outdoor space, equipped with sensors and actuators to collect data and send controls according to the activities and expectations of the occupants/users. Home automation provides a centralized or distributed control of electrical appliances. Adding intelligence to the home environment, it would be possible to obtain, not only excellent levels of comfort, but also energy savings both inside and outside the dwelling, for instance using smart solutions for the management of the external lights and of the garden

Grey Wolf Cuckoo Search Algorithm for Training Feedforward Neural Network and Logic Gates Design

This paper presents a new hybrid Swarm Intelligence (SI) algorithm based on the Cuckoo Search Algorithm (CSA) and Grey Wolf Optimizer (GWO) called the Grey Wolf Cuckoo Search (GWCS) algorithm. The GWCS algorithm extracts and combines CSA and GWO features for efficient optimization. To carry out the comprehensive validation, the developed algorithm is applied to three different scenarios with their counterparts. The first validation is carried out on standard optimization benchmark problems. Further, they are used to train Feedforward Neural Networks and finally applied to design logic gates. The comprehensive results are presented and it is found that the proposed GWCS algorithms perform better compared to the state-of-the-art