scholarly journals Optimization of wind solar and battery hybrid renewable system using backtrack search algorithm

2021 ◽  
Vol 24 (3) ◽  
pp. 1269
Author(s):  
Ingudam Chitrasen Meitei ◽  
Rajen Pudur
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cost Optimization ◽  
Combinatorial Optimization Problems ◽  
Hybrid Combination ◽  
The Matrix ◽  
Electrical Engineers ◽  
Backtrack Search ◽  
Efficiency Cost

<p>Penetration of renewable sources to the grid is always a problem for electrical engineers, apart from reliability and efficiency, cost optimization is also a big concern among them. Wind, solar and battery hybrid combinations (WSB-HPS) are also very common among hybrid systems, but this WSBHPS combines wind and solar energy power generation reduces the charge and discharge time of the battery. Therefore, this system improves the reliability of the power supply by fully utilizing the wind and solar power generation and improves the charging and discharging state of the battery and hence reduces the whole cost as the investment in battery is reduced. backtrack search algorithm (BSA) is the highly efficient and powerful algorithm to solve combinatorial optimization problems. In this paper an attempt is made to optimize the hybrid combination using BSA in the matrix laboratory (MATLAB) environment and comparable study is made using HOMER. A complete optimised data is generated for a particular area in Manipur and reduced cost is suggested.</p>

Modified Firefly Algorithm With Chaos Theory for Feature Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Sujata Dash ◽  
Ruppa Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
High Dimensional ◽  
Support Vector ◽  
Combinatorial Optimization Problems ◽  
Meta Search ◽  
Combined Algorithm

Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts.

Study on Free Search for Combinatorial Optimization Problem

2013 ◽  
Vol 411-414 ◽  
pp. 1904-1910
Author(s):  
Kai Zhong Jiang ◽  
Tian Bo Wang ◽  
Zhong Tuan Zheng ◽  
Yu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Process ◽  
Standard Data ◽  
Combinatorial Optimization Problems ◽  
Free Search

An algorithm based on free search is proposed for the combinatorial optimization problems. In this algorithm, a feasible solution is converted into a full permutation of all the elements and a transformation of one solution into another solution can be interpreted the transformation of one permutation into another permutation. Then, the algorithm is combined with intersection elimination. The discrete free search algorithm greatly improves the convergence rate of the search process and enhances the quality of the results. The experiment results on TSP standard data show that the performance of the proposed algorithm is increased by about 2.7% than that of the genetic algorithm.

scholarly journals Optimal economic dispatch of power generation solution using lightning search algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 371
Author(s):  
Murad Yahya Nassar ◽  
Mohd Noor Abdullah ◽  
Asif Ahmed Rahimoon
Keyword(s):  
Power Generation ◽  
Power System ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Thermal Power ◽  
Economic Dispatch ◽  
Optimal Solution ◽  
Fuel Price ◽  
Solution Quality ◽  
Generation Cost

Economic dispatch (ED) is the power demand allocating process for the committed units at minimum generation cost while satisfying system and operational constraints. Increasing cost of fuel price and electricity demand can increase the cost of thermal power generation. Therefore, robust and efficient optimization algorithm is required to determine the optimal solution for ED problem in power system operation and planning. In this paper the lightning search algorithm (LSA) is proposed to solve the ED problem. The system constraints such as power balance, generator limits, system transmission losses and valve-points effects (VPE) are considered in this paper. To verify the effectiveness of LSA in terms of convergence characteristic, robustness, simulation time and solution quality, the two case studies consists of 6 and 13 units have been tested. The simulation results show that the LSA can provide optimal cost than many methods reported in literature. Therefore, it has potential to solve many optimization problems in power dispatch and power system applications.

scholarly journals Asymptotic Behavior of Memristive Circuits

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 789
Author(s):  
Francesco Caravelli
Keyword(s):  
Lyapunov Function ◽  
Spin Glasses ◽  
Optimization Problems ◽  
Strong Dependence ◽  
Mean Field ◽  
Stationary Points ◽  
Circuit Topology ◽  
Combinatorial Optimization Problems ◽  
The Matrix ◽  
Memristive Circuits

The interest in memristors has risen due to their possible application both as memory units and as computational devices in combination with CMOS. This is in part due to their nonlinear dynamics, and a strong dependence on the circuit topology. We provide evidence that also purely memristive circuits can be employed for computational purposes. In the present paper we show that a polynomial Lyapunov function in the memory parameters exists for the case of DC controlled memristors. Such a Lyapunov function can be asymptotically approximated with binary variables, and mapped to quadratic combinatorial optimization problems. This also shows a direct parallel between memristive circuits and the Hopfield-Little model. In the case of Erdos-Renyi random circuits, we show numerically that the distribution of the matrix elements of the projectors can be roughly approximated with a Gaussian distribution, and that it scales with the inverse square root of the number of elements. This provides an approximated but direct connection with the physics of disordered system and, in particular, of mean field spin glasses. Using this and the fact that the interaction is controlled by a projector operator on the loop space of the circuit. We estimate the number of stationary points of the approximate Lyapunov function and provide a scaling formula as an upper bound in terms of the circuit topology only.

Microcanonical Optimization Applied to the Traveling Salesman Problem

1998 ◽  
Vol 09 (01) ◽  
pp. 133-146 ◽  
Author(s):  
Alexandre Linhares ◽  
José R. A. Torreão
Keyword(s):  
Simulated Annealing ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
New Physics ◽  
Traveling Salesman ◽  
Tabu Search Algorithm ◽  
Combinatorial Optimization Problems ◽  
Microcanonical Annealing ◽  
The Traveling Salesman Problem

Optimization strategies based on simulated annealing and its variants have been extensively applied to the traveling salesman problem (TSP). Recently, there has appeared a new physics-based metaheuristic, called the microcanonical optimization algorithm (μO), which does not resort to annealing, and which has proven a superior alternative to the annealing procedures in various applications. Here we present the first performance evaluation of μO as applied to the TSP. When compared to three annealing strategies (simulated annealing, microcanonical annealing and Tsallis annealing), and to a tabu search algorithm, the microcanonical optimization has yielded the best overall results for several instances of the euclidean TSP. This confirms μO as a competitive approach for the solution of general combinatorial optimization problems.

scholarly journals Design of Flotation Circuits Using Tabu-Search Algorithms: Multispecies, Equipment Design, and Profitability Parameters

Minerals ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 181 ◽  
Author(s):  
Freddy Lucay ◽  
Edelmira Gálvez ◽  
Luis Cisternas
Keyword(s):  
Tabu Search ◽  
Approximate Method ◽  
Short Term Memory ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Optimization Techniques ◽  
Adaptive Procedure ◽  
Problem Size ◽  
Combinatorial Optimization Problems

The design of a flotation circuit based on optimization techniques requires a superstructure for representing a set of alternatives, a mathematical model for modeling the alternatives, and an optimization technique for solving the problem. The optimization techniques are classified into exact and approximate methods. The first has been widely used. However, the probability of finding an optimal solution decreases when the problem size increases. Genetic algorithms have been the approximate method used for designing flotation circuits when the studied problems were small. The Tabu-search algorithm (TSA) is an approximate method used for solving combinatorial optimization problems. This algorithm is an adaptive procedure that has the ability to employ many other methods. The TSA uses short-term memory to prevent the algorithm from being trapped in cycles. The TSA has many practical advantages but has not been used for designing flotation circuits. We propose using the TSA for solving the flotation circuit design problem. The TSA implemented in this work applies diversification and intensification strategies: diversification is used for exploring new regions, and intensification for exploring regions close to a good solution. Four cases were analyzed to demonstrate the applicability of the algorithm: different objective function, different mathematical models, and a benchmarking between TSA and Baron solver. The results indicate that the developed algorithm presents the ability to converge to a solution optimal or near optimal for a complex combination of requirements and constraints, whereas other methods do not. TSA and the Baron solver provide similar designs, but TSA is faster. We conclude that the developed TSA could be useful in the design of full-scale concentration circuits.

An Iterated Local Search Algorithm for Fuel Consumption Optimization of Vehicle Routing Problems

2011 ◽  
Vol 148-149 ◽  
pp. 1248-1251
Author(s):  
Xu Dong Wu
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Iterated Local Search ◽  
Vehicle Routing Problems ◽  
Local Search Algorithm ◽  
Routing Problems ◽  
Combinatorial Optimization Problems

The iterated local search algorithm has been widely used in combinatorial optimization problems. A new fuel consumption objective for the vehicle routing problems was presented in this paper. A fuel consumption modal of the vehicle load is introduced and an improved iterated local search algorithm is used for the problem. An initial solution is generated by the Solomon I1 algorithm, and then the iterated local search algorithm is proposed for the fuel consumption optimization.

TABU SEARCH ALGORITHM FOR OPTIMIZING PILE FOUNDATION LAYOUT ON FOOTING OF RESIDENTIAL HOUSE

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Takahiro Hara ◽  
Buntara S. Gan
Keyword(s):  
Tabu Search ◽  
Pile Foundation ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Tabu Search Algorithm ◽  
Design Practice ◽  
Local Optima ◽  
Combinatorial Optimization Problems ◽  
Recent Advancement ◽  
Residential House

In practice, positioning pile foundation on footing layout of a residential house is often based on empirical judgment and long working experiences of a designer. After the pile requirements calculated, some conditions like number of piles, spacing placement of all piles on the footing, and locations such as under columns, corner, or intersection have to be determined. Besides, the coincidence of shear and gravity centers is also another essential design consideration, which is often ignored in design practice. The present study is aimed at eliminating the horizontal torsion effects during the earthquakes occurrences by reducing the eccentricity distance between the centers of gravity and shear. The scope of present study excludes any foundation or structural related design and analysis. Recent advancement of computing capabilities of computers has made the structural analysis become a convenient tool at hand for designers. In this study, the Tabu Search algorithm was adopted as an optimization tool for pile placing on the footing of residential houses. Tabu Search algorithm was known as an efficient method for solving combinatorial optimization problems where it can find some quality solutions in relatively short running time without getting stuck in local optima. An example of footing layout design was demonstrated to show the effectiveness of the present study as a decision-making tool in design practice.

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