Description of Various Evolutionary Optimization Techniques

Evolutionary Optimization Techniques ◽

Despite the success of various classical optimization techniques, there remains a large class of problems where either these methods are unable to find the satisfactory results or the computational times are sufficiently large. Several heuristic methods have emerged in the recent years as complementary tools to various mathematical approaches. These methods include genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), differential evolution (DE), and so on. Researchers are constantly trying to learn from the behavioral pattern of organisms and implementing those ideas and philosophies in solving optimizing problems. In this chapter, a few efficient optimization algorithms, namely grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO) algorithms, and hybrid CRO (HCRO) are discussed, and in the subsequent chapters, the performance of the aforesaid algorithms are investigated by applying them in a few areas of power systems.

Optimal Power Flow Using Evolutionary Algorithms - Advances in Computer and Electrical Engineering ◽

Evolutionary Algorithm Applied to Economic Load Dispatch

10.4018/978-1-5225-6971-8.ch003 ◽

2018 ◽

pp. 65-116

Keyword(s):

Power Systems ◽

Economic Load Dispatch ◽

Grey Wolf ◽

Solution Quality ◽

Krill Herd Algorithm ◽

Reaction Optimization ◽

Krill Herd ◽

Teaching Learning ◽

Statistical Results

This chapter introduces various evolutionary algorithms, namely grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO) algorithms, for solving the economic load dispatch (ELD) problem of various power systems. To demonstrate the superiority of the proposed approaches in solving non-convex, non-linear and constrained ELD problem, the aforesaid approaches are implemented on 10-unit, 15-unit, 40-unit, 80-unit, and 140-unit test systems. It is observed from the simulation results that HCRO exhibits significantly better performance in terms of solution quality and convergence speed for all the cases compared to other discussed algorithms. Furthermore, the statistical results confirm the robustness of the proposed HCRO algorithm.

TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666191202113015 ◽

2020 ◽

Vol 10 (4) ◽

pp. 522-532 ◽

Cited By ~ 1

Author(s):

Sarat Chandra Nayak ◽

Subhranginee Das ◽

Mohammad Dilsad Ansari

Keyword(s):

Neural Networks ◽

Computational Cost ◽

Optimization Techniques ◽

Fine Tuning ◽

Functional Link ◽

Price Prediction ◽

Closing Price ◽

Artificial Neural ◽

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

Automatic generation control of multi-area power systems with diverse energy sources using Teaching Learning Based Optimization algorithm

Engineering Science and Technology an International Journal ◽

10.1016/j.jestch.2015.07.011 ◽

2016 ◽

Vol 19 (1) ◽

pp. 113-134 ◽

Cited By ~ 54

Author(s):

Rabindra Kumar Sahu ◽

Tulasichandra Sekhar Gorripotu ◽

Sidhartha Panda

Keyword(s):

Power Systems ◽

Optimization Algorithm ◽

Automatic Generation ◽

Automatic Generation Control ◽

Energy Sources ◽

Teaching Learning ◽

Generation Control

Advances in Computer and Electrical Engineering - Optimizing and Measuring Smart Grid Operation and Control ◽

PMU Placement Optimization for Fault Observation Using Different Techniques

10.4018/978-1-7998-4027-5.ch009 ◽

2021 ◽

pp. 196-220

Author(s):

Hamid Bentarzi

Keyword(s):

Cuckoo Search ◽

Optimal Number ◽

Optimization Techniques ◽

Grey Wolf Optimizer ◽

Placement Optimization ◽

Measurement Units ◽

Pmu Placement ◽

Teaching Learning ◽

Full Network

This chapter presents different techniques for obtaining the optimal number of the phasor measurement units (PMUs) that may be installed in a smart power grid to achieve full network observability under fault conditions. These optimization techniques such as binary teaching learning based optimization (BTLBO) technique, particle swarm optimization, the grey wolf optimizer (GWO), the moth-flame optimization (MFO), the cuckoo search (CS), and the wind-driven optimization (WDO) have been developed for the objective function and constraints alike. The IEEE 14-bus benchmark power system has been used for testing these optimization techniques by simulation. A comparative study of the obtained results of previous works in the literature has been conducted taking into count the simplicity of the model and the accuracy of characteristics.

An improved teaching learning–based optimization algorithm for congestion management with the integration of solar photovoltaic system

Measurement and Control ◽

10.1177/0020294020914930 ◽

2020 ◽

Vol 53 (7-8) ◽

pp. 1231-1237

Author(s):

S T Suganthi ◽

D Devaraj

Keyword(s):

Power Systems ◽

Congestion Management ◽

Test System ◽

Photovoltaic System ◽

Management Technique ◽

Optimization Approach ◽

Solar Photovoltaic ◽

Solar Photovoltaic System ◽

In restructured power systems, transmission congestion is an imperative issue. Establishment of solar photovoltaic system at appropriate areas is likely to relieve congestion in transmission lines in the restructured power systems. Congestion management technique by utilizing solar photovoltaic sources, using an improved teaching learning–based optimization, is investigated in this article. Bus sensitivity factors which have the direct influence on the congested lines are utilized to locate the solar photovoltaic sources at appropriate areas. Congestion management is figured as an optimization problem with a goal of limiting the congestion management price utilizing the improved teaching learning–based optimization approach, which espouses the self-driven learning principle. IEEE-30 bus test system is simulated and tested in MATLAB environment so as to demonstrate the viability of the suggested methodology than different methodologies.

Rough set and teaching learning based optimization technique for optimal features selection

Open Computer Science ◽

10.2478/s13537-013-0102-4 ◽

2013 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Suresh Satapathy ◽

Anima Naik ◽

K. Parvathi

Keyword(s):

Feature Selection ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Optimization Technique ◽

Feature Selection Method ◽

Optimization Techniques ◽

Features Selection ◽

AbstractRough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough Set theory with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.

Optimized trajectory planning of a robotic arm using teaching learning based optimization (TLBO) and artificial bee colony (ABC) optimization techniques

2013 IEEE International Systems Conference (SysCon) ◽

10.1109/syscon.2013.6549910 ◽

2013 ◽

Cited By ~ 10

Author(s):

P. Savsani ◽

R. L. Jhala ◽

V. J. Savsani

Keyword(s):

Trajectory Planning ◽

Artificial Bee Colony ◽

Optimization Techniques ◽

Robotic Arm ◽

Bee Colony ◽

Optimal Power Flow Incorporating FACTS Devices and Stochastic Wind Power Generation Using Krill Herd Algorithm

Electronics ◽

10.3390/electronics9061043 ◽

2020 ◽

Vol 9 (6) ◽

pp. 1043 ◽

Cited By ~ 2

Author(s):

Arsalan Abdollahi ◽

Ali Ghadimi ◽

Mohammad Miveh ◽

Fazel Mohammadi ◽

Francisco Jurado

Keyword(s):

Power Generation ◽

Power Systems ◽

Wind Power ◽

Power Flow ◽

Optimal Power Flow ◽

Wind Power Generation ◽

Krill Herd Algorithm ◽

Facts Devices ◽

Optimal Power ◽

Krill Herd

This paper deals with investigating the Optimal Power Flow (OPF) solution of power systems considering Flexible AC Transmission Systems (FACTS) devices and wind power generation under uncertainty. The Krill Herd Algorithm (KHA), as a new meta-heuristic approach, is employed to cope with the OPF problem of power systems, incorporating FACTS devices and stochastic wind power generation. The wind power uncertainty is included in the optimization problem using Weibull probability density function modeling to determine the optimal values of decision variables. Various objective functions, including minimization of fuel cost, active power losses across transmission lines, emission, and Combined Economic and Environmental Costs (CEEC), are separately formulated to solve the OPF considering FACTS devices and stochastic wind power generation. The effectiveness of the KHA approach is investigated on modified IEEE-30 bus and IEEE-57 bus test systems and compared with other conventional methods available in the literature.