scholarly journals Economic and emission dispatch using cuckoo search algorithm

2019 ◽  
Vol 9 (5) ◽  
pp. 3384
Author(s):  
Rachid Habachi ◽  
Abdellah Boulal ◽  
Achraf Touil ◽  
Abdelkabir Charkaoui ◽  
Abdelwahed Echchatbi
Keyword(s):  
Brood Parasitism ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Energy Systems ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Cuckoo Search Algorithm ◽  
Heuristic Optimization ◽  
Generator System ◽  
Emission Dispatch

<p class="Default">The economic dispatch problem of power plays a very important role in the exploitation of electro-energy systems to judiciously distribute power generated by all plants. This paper proposes the use of Cuckoo Search Algorithm (CSA) for solving the economic and Emission dispatch. The effectiveness of the proposed approach has been tested on 3 generator system. CSA is a new meta-heuristic optimization method inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species.The results shows that performance of the proposed approach reveal the efficiently and robustness when compared results of other optimization algorithms reported in literature</p>

scholarly journals Cuckoo search algorithm to solve the problem of economic emission dispatch with the incorporation of facts devices under the valve-point loading effect

2021 ◽  
Vol 34 (4) ◽  
pp. 569-588
Author(s):  
Larouci Benyekhlef ◽  
Sitayeb Abdelkader ◽  
Boudjella Houari ◽  
Ayad Ahmed Nour El Islam
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Fuel Cost ◽  
Loading Effect ◽  
Facts Devices ◽  
Emission Dispatch ◽  
Valve Point Loading Effect ◽  
Economic Emission Dispatch

The essential objective of optimal power flow is to find a stable operating point which minimizes the cost of the production generators and its losses, and keeps the power system acceptable in terms of limits on the active and reactive powers of the generators. In this paper, we propose the nature-inspired Cuckoo search algorithm (CSA) to solve economic/emission dispatch problems with the incorporation of FACTS devices under the valve-point loading effect (VPE). The proposed method is applied on different test systems cases to minimize the fuel cost and total emissions and to see the influence of the integration of FACTS devices. The obtained results confirm the efficiency and the robustness of the Cuckoo search algorithm compared to other optimization techniques published recently in the literature. In addition, the simulation results show the advantages of the proposed algorithm for optimizing the production fuel cost, total emissions and total losses in all transmission lines.

scholarly journals Cuckoo search algorithm for applied structural and design optimization: Float system for experimental setups

2018 ◽  
Vol 6 (2) ◽  
pp. 159-172 ◽  
Author(s):  
Mostafa Jalal ◽  
Maral Goharzay
Keyword(s):  
Constrained Optimization ◽  
Design Optimization ◽  
Structural Design ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Structural Constraints ◽  
Generalized Reduced Gradient ◽  
And Performance

Abstract In the present study, Cuckoo Search (CS) as a nature-inspired optimization algorithm was applied for structural and design optimization of a new float system for experimental setups. For this purpose, based on the setup configuration, it was tried to minimize the total length of the float, while maintaining the structural and performance-based constraints. Different geometries for the float structure were examined to come up with the feasible options. The problem was formulated into a constrained optimization in terms of four or five variables, depending on the geometry, along with two performance-based constraints and some structural constraints. CS was used to solve the constrained optimization problem and the convergence trends of the parameters to optimal solutions were examined in details. Generalized reduced gradient (GRG) method known as GRG nonlinear was also used for validation and comparison purpose. The results of the optimization and the performance of the float produced showed that CS can be used as a powerful tool for applied structural and design problems. It should be mentioned that the float problem can be used as a benchmark structural design problem for validation of new optimization algorithms. Besides, the optimal float can be produced for various experimental setups with different structures and constraints, depending on the application. Highlights Cuckoo Search (CS) algorithm as a metaheuristic approach. Constrained optimization in structural design using CS algorithm. Designing a new float for experimental setups. Production of an optimal float for measurement system. Float design as a benchmark problem for optimization algorithms.

An Overview on Stochastic Global Optimization and its Multi-Domain Applications

2019 ◽  
Vol 8 (2) ◽  
pp. 1-6
Author(s):  
Venkateswarlu Chimmiri
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Bee Colony ◽  
Highly Nonlinear ◽  
Evolutionary Optimization Algorithms ◽  
Discrete Optimization Problems

Optimization is of great interest and it has widespread applications in engineering and science. It has become a major technology contributor to the growth of industry. It is extensively used in solving a wide variety of problems in design, operation, and analysis of engineering and technological processes. Optimization of large-scale problems pose difficulties concerning to dimensionality, differentiability, multimodality and nonlinearity in objective functions and constraints. In order to overcome such difficulties, there has been a rapidly growing interest in advanced optimization algorithms. Stochastic and evolutionary optimization algorithms are increasingly used to solve challenging optimization problems. These algorithms include genetic algorithm, simulated annealing, differential evolution, ant colony optimization, tabu search, particle swarm optimization, artificial bee colony algorithm, and cuckoo search algorithm. These algorithms are typically inspired by some phenomena from nature and they are robust. These algorithms do not require any gradient information and are even suitable to solve discrete optimization problems. These methods are extensively used to solve the optimization problems concerning to systems that are highly nonlinear, high dimensional, and noisy or for solving problems that are not easily solved by classical deterministic methods of optimization.

A Dynamic Switched Compensation Scheme for Grid-Connected Wind Energy Systems Using Cuckoo Search Algorithm

2019 ◽  
Vol 7 (2) ◽  
pp. 64 ◽  
Author(s):  
Ahmed I. Omar ◽  
Ziad M. Ali ◽  
Shady H. E. Abdel Aleem ◽  
Essam E. M. Abou-El-Zahab ◽  
Adel M. Sharaf
Keyword(s):  
Wind Energy ◽  
Search Algorithm ◽  
Energy Systems ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Compensation Scheme ◽  
Wind Energy Systems

A new hybrid cuckoo search and firefly optimization

2018 ◽  
Vol 24 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Mariam Elkhechafi ◽  
Hanaa Hachimi ◽  
Youssfi Elkettani
Keyword(s):  
Global Optimization ◽  
Hybrid Algorithm ◽  
Firefly Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Firefly Optimization ◽  
New Generation

Abstract In this paper, we present a new hybrid algorithm which is a combination of a hybrid Cuckoo search algorithm and Firefly optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, Cuckoo Search (CS) and Firefly Algorithm (FA) for the global optimization. Denoted as CS-FA. The hybrid CS-FA technique incorporates concepts from CS and FA and creates individuals in a new generation not only by random walk as found in CS but also by mechanisms of FA. To analyze the benefits of hybridization, we have comparatively evaluated the classical Cuckoo Search and Firefly Algorithms versus the proposed hybridized algorithms (CS-FA).

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