scholarly journals Microgrid Operations Planning Based on Improving the Flying Sparrow Search Algorithm

Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 168
Author(s):  
Trong-The Nguyen ◽  
Truong-Giang Ngo ◽  
Thi-Kien Dao ◽  
Thi-Thanh-Tan Nguyen
Keyword(s):  
Firefly Algorithm ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Clean Energy ◽  
Economic Optimization ◽  
Operations Planning ◽  
Power Sources ◽  
Optimal Output ◽  
Mutation Strategy

Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues.

Multi-Objective Optimal Power Flow Using Metaheuristic Optimization Algorithms With Unified Power Flow Controller to Enhance the Power System Performance

2018 ◽  
pp. 1-33
Author(s):  
G. V. Nagesh Kumar ◽  
B. Venkateswara Rao ◽  
D. Deepak Chowdary ◽  
Polamraju V. S. Sobhan
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Unified Power Flow Controller ◽  
Multi Objective ◽  
Optimal Power

In this chapter a multi objective optimal power flow (OPF) is obtained by using latest Metaheuristic optimization techniques BAT search algorithm (BAT), cuckoo search algorithm (CSA) and firefly algorithm (FA) with Unified power flow controller (UPFC). UPFC is a voltage source converter type Flexible Alternating Current Transmission System (FACTS) device. It is able to control the voltage magnitudes, voltage angles and line impedances individually or simultaneously. To enhance the power system performance, the optimal power flow has been incorporated UPFC along with BAT algorithm, cuckoo search algorithm and firefly algorithm based multi objective function comprising of two objectives those are total real power loss and the fuel cost of total real power generation. The BAT algorithm, cuckoo search algorithm and firefly algorithm based OPF has been examined and tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results obtained with BAT algorithm, cuckoo search algorithm and firefly algorithms are compared with Differential Evaluation (DE).

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

A Comparative Study of Global-Best Harmony Search and Bat Algorithms on Optimization Problems

2013 ◽  
Vol 464 ◽  
pp. 352-357
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Computation Time ◽  
Finite Sequence ◽  
Bat Algorithm ◽  
Harmony Search Algorithm ◽  
Response Surfaces ◽  
Sample Mean ◽  
Constrained Problems

The engineering optimization problems are large and complex. Effective methods for solving these problems using a finite sequence of instructions can be categorized into optimization and meta-heuristics algorithms. Meta-heuristics techniques have been proved to solve various real world problems. In this study, a comparison of two meta-heuristic techniques, namely, Global-Best Harmony Search algorithm (GHSA) and Bat algorithm (BATA), for solving constrained optimization problems was carried out. GHSA and BATA are optimization algorithms inspired by the structure of harmony improvisation search process and social behavior of bat echolocation for decision direction. These algorithms were implemented under different natures of three optimization, which are single-peak, multi-peak and curved-ridge response surfaces. Moreover, both algorithms were also applied to constrained engineering problems. The results from non-linear continuous unconstrained functions in the context of response surface methodology and constrained problems can be shown that Bat algorithm seems to be better in terms of the sample mean and variance of design points yields and computation time.

scholarly journals A Novel Bat Algorithm Based on Differential Operator and Lévy Flights Trajectory

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Jian Xie ◽  
Yongquan Zhou ◽  
Huan Chen
Keyword(s):  
Differential Operator ◽  
Dimensional Space ◽  
Bat Algorithm ◽  
High Dimensional ◽  
Local Minima ◽  
Lévy Flights ◽  
Levy Flights ◽  
Mutation Strategy ◽  
Differential Algorithm ◽  
Simulation Results

Aiming at the phenomenon of slow convergence rate and low accuracy of bat algorithm, a novel bat algorithm based on differential operator and Lévy flights trajectory is proposed. In this paper, a differential operator is introduced to accelerate the convergence speed of proposed algorithm, which is similar to mutation strategy “DE/best/2” in differential algorithm. Lévy flights trajectory can ensure the diversity of the population against premature convergence and make the algorithm effectively jump out of local minima. 14 typical benchmark functions and an instance of nonlinear equations are tested; the simulation results not only show that the proposed algorithm is feasible and effective, but also demonstrate that this proposed algorithm has superior approximation capabilities in high-dimensional space.

scholarly journals The Global Survey of the Electrical Energy Distribution System: A Review

2019 ◽  
Vol 9 (4) ◽  
pp. 2247 ◽  
Author(s):  
Archana Sudhakar Talhar ◽  
Sanjay B. Bodkhe
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Electrical Power ◽  
Electrical Energy ◽  
Clean Energy ◽  
Central Intelligence Agency ◽  
Energy Sources ◽  
Power Sources ◽  
Energy Scenario ◽  
The World

This paper gives a review of energy scenario in India and other countries. Today’s demand of the world is to minimize greenhouse gas emissions, during the production of electricity. Henceforth over the world, the production of electrical power is changing by introducing abundantly available renewable energy sources like sun and wind. But, because of the intermittent nature of sustainable power sources, the electrical power network faces many problems, during the transmission and distribution of electricity. For resolving these issues, Electrical Energy Storage (EES) is acknowledged as supporting technology. This paper discusses about the world electrical energy scenario with top renowned developed countries in power generation and consumption. Contribution of traditional power sources changed after the introduction of renewable energy sources like sun and wind. Worldwide Agencies are formed like International Energy Agency (IEA), The Central Intelligence Agency, (CIS) etc. The main aim of these agencies is to provide reliable, affordable and clean energy. This paper will discuss about the regulatory authority and government policies/incentives taken by different countries.  At the end of this paper, author focuses on obstacles in implementation, development and benefits of renewable energy.

scholarly journals Improved Backtracking Search Algorithm Based on Population Control Factor and Optimal Learning Strategy

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zhao ◽  
Zhicheng Jia ◽  
Lei Chen ◽  
Yanju Guo
Keyword(s):  
Learning Strategy ◽  
Search Algorithm ◽  
Population Control ◽  
Pso Algorithm ◽  
Convergence Speed ◽  
Evolutionary Equation ◽  
Control Factor ◽  
Local Optimum ◽  
Backtracking Search ◽  
Backtracking Search Algorithm

Backtracking search algorithm (BSA) is a relatively new evolutionary algorithm, which has a good optimization performance just like other population-based algorithms. However, there is also an insufficiency in BSA regarding its convergence speed and convergence precision. For solving the problem shown in BSA, this article proposes an improved BSA named COBSA. Enlightened by particle swarm optimization (PSO) algorithm, population control factor is added to the variation equation aiming to improve the convergence speed of BSA, so as to make algorithm have a better ability of escaping the local optimum. In addition, enlightened by differential evolution (DE) algorithm, this article proposes a novel evolutionary equation based on the fact that the disadvantaged group will search just around the best individual chosen from previous iteration to enhance the ability of local search. Simulation experiments based on a set of 18 benchmark functions show that, in general, COBSA displays obvious superiority in convergence speed and convergence precision when compared with BSA and the comparison algorithms.

scholarly journals Multi-Objective Operation of Cascade Hydropower Reservoirs Using TOPSIS and Gravitational Search Algorithm with Opposition Learning and Mutation

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2040 ◽  
Author(s):  
Feng ◽  
Liu ◽  
Jiang ◽  
Luo ◽  
Miao
Keyword(s):  
Learning Strategy ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Method ◽  
Multi Objective ◽  
Practical Constraint ◽  
Hydropower System ◽  
Order Preference ◽  
Gravitational Search ◽  
The Individual

In this research, a novel enhanced gravitational search algorithm (EGSA) is proposed to resolve the multi-objective optimization model, considering the power generation of a hydropower enterprise and the peak operation requirement of a power system. In the proposed method, the standard gravity search algorithm (GSA) was chosen as the fundamental execution framework; the opposition learning strategy was adopted to increase the convergence speed of the swarm; the mutation search strategy was chosen to enhance the individual diversity; the elastic-ball modification strategy was used to promote the solution feasibility. Additionally, a practical constraint handling technique was introduced to improve the quality of the obtained agents, while the technique for order preference by similarity to an ideal solution method (TOPSIS) was used for the multi-objective decision. The numerical tests of twelve benchmark functions showed that the EGSA method could produce better results than several existing evolutionary algorithms. Then, the hydropower system located on the Wu River of China was chosen to test the engineering practicality of the proposed method. The results showed that the EGSA method could obtain satisfying scheduling schemes in different cases. Hence, an effective optimization method was provided for the multi-objective operation of hydropower system.

Multiloop FOPID Controller Design for TITO Process Using Evolutionary Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 117-130 ◽  
Author(s):  
Lakshmanaprabu S.K. ◽  
Najumnissa Jamal D. ◽  
Sabura Banu U.
Keyword(s):  
Evolutionary Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Internal Model Control ◽  
Model Control ◽  
Fractional Order Pid

In this article, the tuning of multiloop Fractional Order PID (FOPID) controller is designed for Two Input Two Output (TITO) processes using an evolutionary algorithm such as the Genetic algorithm (GA), the Cuckoo Search algorithm (CS) and the Bat Algorithm (BA). The control parameters of FOPID are obtained using GA, CS, and BA for minimizing the integral error criteria. The main objective of this article is to compare the performance of the GA, CS, and BA for the multiloop FOPID controller problem. The integer order internal model control based PID (IMC-PID) controller is designed using the GA and the performance of the IMC-PID controller is compared with the FOPID controller scheme. The simulation results confirm that BA offers optimal controller parameter with a minimum value of IAE, ISE, ITAE with faster settling time.

scholarly journals A Firefly Algorithm Optimization-Based Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Light Rail Vehicle

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2665 ◽  
Author(s):  
Han Zhang ◽  
Jibin Yang ◽  
Jiye Zhang ◽  
Pengyun Song ◽  
Xiaohui Xu
Keyword(s):  
Fuel Cell ◽  
Fuel Consumption ◽  
Firefly Algorithm ◽  
Operating Cost ◽  
Operating Mode ◽  
Light Rail ◽  
Power Sources ◽  
Rail Vehicle ◽  
Mode Control ◽  
Equivalent Consumption Minimization Strategy

To coordinate multiple power sources properly, this paper presents an optimal control strategy for a fuel cell/battery/supercapacitor light rail vehicle. The proposed strategy, which uses the firefly algorithm to optimize the equivalent consumption minimization strategy, improves the drawback that the conventional equivalent consumption minimization strategy takes insufficient account of the global performance for the vehicle. Moreover, the strategy considers the difference between the two sets of optimized variables. The optimization objective is to minimize the daily operating cost of the vehicle, which includes the total fuel consumption, initial investment, and cycling costs of power sources. The selected case study is a 100% low-floor light rail vehicle. The advantages of the proposed strategy are investigated by comparison with the operating mode control, firefly algorithm-based operating mode control, and equivalent consumption minimization strategy. In contrast to other methods, the proposed strategy shows cost reductions of up to 39.62% (from operating mode control), 18.28% (from firefly algorithm-based operating mode control), and 13.81% (from equivalent consumption minimization strategy). In addition, the proposed strategy can reduce fuel consumption and increase the efficiency of the fuel cell system.

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.

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