A new Levy Flight Trajectory-Based Grasshopper optimization Algorithm for Multi-objective optimization Problems

In recent years, fractional order proportional-integral-derivative (FOPID) controllers have been applied in different areas in the academy due to their superior performance over conventional proportional-integral-derivative (PID) controllers. When the literature is reviewed, it has been observed that lack of studies that use swarm-based and multi-objective optimization algorithms together with FOPID controllers in frequency control of micro-grid. The load frequency control (LFC) problem is considered as two objectives in order to eliminate the complications that occur when only the frequency deviation is minimized. In our study, a method called MOGOA-FOPID in which both the frequency deviation and the control signal are minimized together for the frequency control in the microgrid is proposed. By using the multi-objective grasshopper optimization algorithm (MOGOA), both the frequency deviation and the control signal are minimized together, and thus, it is aimed to limit the battery capacity, reduce the flywheel jerk and avoid high diesel fuel consumption as well as an effective frequency control. In order to obtain a more realistic system, not only the photovoltaic (PV) solar and wind power but also the load demand is taken in a stochastic structure. Then, the results of the proposed MOGOA-FOPID are compared with the results of multi-objective genetic algorithm (MOGA)-based PID/FOPID and MOGOA-PID and its superiority is demonstrated. Finally, robustness tests of the system are performed under the perturbed parameters and outperform of MOGOA-FOPID over other methods is seen.

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An Improved Grasshopper Optimization Algorithm for Solving Numerical Optimization Problems

Lecture Notes in Networks and Systems - Advances in Intelligent Computing and Communication ◽

10.1007/978-981-15-2774-6_22 ◽

2020 ◽

pp. 179-188

Author(s):

Puneet Mishra ◽

Vishal Goyal ◽

Aasheesh Shukla

Keyword(s):

Optimization Algorithm ◽

Numerical Optimization ◽

Optimization Problems ◽

Grasshopper Optimization Algorithm ◽

Grasshopper Optimization

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A Hybrid Optimization Algorithm for Single and Multi-Objective Optimization Problems

Handbook of Research on Machine Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2229-4.ch021 ◽

2017 ◽

pp. 491-521 ◽

Cited By ~ 2

Author(s):

Rizk M. Rizk-Allah ◽

Aboul Ella Hassanien

Keyword(s):

Optimization Algorithm ◽

Firefly Algorithm ◽

Optimization Problems ◽

Optimal Solution ◽

Hybrid Optimization ◽

Multi Objective Optimization ◽

Local Optima ◽

Fruit Fly Optimization ◽

Multi Objective ◽

Hybrid Optimization Algorithm

This chapter presents a hybrid optimization algorithm namely FOA-FA for solving single and multi-objective optimization problems. The proposed algorithm integrates the benefits of the fruit fly optimization algorithm (FOA) and the firefly algorithm (FA) to avoid the entrapment in the local optima and the premature convergence of the population. FOA operates in the direction of seeking the optimum solution while the firefly algorithm (FA) has been used to accelerate the optimum seeking process and speed up the convergence performance to the global solution. Further, the multi-objective optimization problem is scalarized to a single objective problem by weighting method, where the proposed algorithm is implemented to derive the non-inferior solutions that are in contrast to the optimal solution. Finally, the proposed FOA-FA algorithm is tested on different benchmark problems whether single or multi-objective aspects and two engineering applications. The numerical comparisons reveal the robustness and effectiveness of the proposed algorithm.

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Modified Grasshopper Optimization Algorithm Based Genetic Algorithm for Global Optimization Problems: The System of Nonlinear Equations Case Study

10.21203/rs.3.rs-382227/v1 ◽

2021 ◽

Author(s):

Hala A. Omar ◽

Mohammed El-Shorbagy

Keyword(s):

Genetic Algorithm ◽

Optimization Algorithm ◽

Local Minimum ◽

Optimization Problems ◽

Search Space ◽

System Of Nonlinear Equations ◽

Grasshopper Optimization Algorithm ◽

Speed Up ◽

Grasshopper Optimization

Abstract Grasshopper optimization algorithm (GOA) is one of the promising optimization algorithms for optimization problems. But, it has the main drawback of trapping into a local minimum, which causes slow convergence or inability to detect a solution. Several modifications and combinations have been proposed to overcome this problem. In this paper, a modified grasshopper optimization algorithm (MGOA) based genetic algorithm (GA) is proposed to overcome this problem. Modifications rely on certain mathematical assumptions and varying the domain of the Cmax control parameter to escape from the local minimum and move the search process to a new improved point. Parameter C is one of the most important parameters in GOA where it balances the exploration and exploitation of the search space. These modifications aim to lead to speed up the convergence rate by reducing the repeated solutions and the number of iterations. The proposed algorithm will be tested on the 19 main test functions to verify and investigate the influence of the proposed modifications. In addition, the algorithm will be applied to solve 5 different cases of nonlinear systems with different types of dimensions and regularity to show the reliability and efficiency of the proposed algorithm. Good results were achieved compared to the original GOA.

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The improved grasshopper optimization algorithm and its applications

Scientific Reports ◽

10.1038/s41598-021-03049-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Peng Qin ◽

Hongping Hu ◽

Zhengmin Yang

Keyword(s):

Neural Network ◽

Optimization Algorithm ◽

Bp Neural Network ◽

Optimization Problems ◽

Stock Exchange ◽

Gravity Force ◽

Shanxi Province ◽

Grasshopper Optimization Algorithm ◽

Predicted Model ◽

Grasshopper Optimization

AbstractGrasshopper optimization algorithm (GOA) proposed in 2017 mimics the behavior of grasshopper swarms in nature for solving optimization problems. In the basic GOA, the influence of the gravity force on the updated position of every grasshopper is not considered, which possibly causes GOA to have the slower convergence speed. Based on this, the improved GOA (IGOA) is obtained by the two updated ways of the position of every grasshopper in this paper. One is that the gravity force is introduced into the updated position of every grasshopper in the basic GOA. And the other is that the velocity is introduced into the updated position of every grasshopper and the new position are obtained from the sum of the current position and the velocity. Then every grasshopper adopts its suitable way of the updated position on the basis of the probability. Finally, IGOA is firstly performed on the 23 classical benchmark functions and then is combined with BP neural network to establish the predicted model IGOA-BPNN by optimizing the parameters of BP neural network for predicting the closing prices of the Shanghai Stock Exchange Index and the air quality index (AQI) of Taiyuan, Shanxi Province. The experimental results show that IGOA is superior to the compared algorithms in term of the average values and the predicted model IGOA-BPNN has the minimal predicted errors. Therefore, the proposed IGOA is an effective and efficient algorithm for optimization.

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