scholarly journals Beetle swarm optimization algorithm: Theory and application

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5121-5137
Author(s):  
Tiantian Wang ◽  
Long Yang ◽  
Qiang Liu
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Pso Algorithm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Algorithm Theory ◽  
Grasshopper Optimization ◽  
Practical Impact

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization (BSO) algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and grasshopper optimization algorithm (GOA). Numerical experiments show that the BSO algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau?s optimization problem, are also considered and the proposed BSO algorithm is shown to be competitive in those applications.

scholarly journals Greenfly Aphid Swarm Optimization Algorithm

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Hanan A.R. Akkar ◽  
Sameem Abbas Salman
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Optimization Technique ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Grasshopper Optimization ◽  
Swarm Intelligence Optimization ◽  
Practical Impact

A new metaheuristic swarm intelligence optimization technique, called general greenfly aphid swarm optimization algorithm, which is proposed by enhancing the performance of swarm optimization through cockroach swarm optimization algorithm. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, cockroach swarm optimization and grasshopper optimization algorithm. Numerical experiments show that the greenfly aphid swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblau’s optimization problem, are also considered and the proposed greenfly aphid swarm optimization algorithm is shown to be competitive in those applications.

scholarly journals Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

2020 ◽  
Vol 13 (6) ◽  
pp. 279-293
Author(s):  
Hanan Akkar ◽  
◽  
Sameem Salman ◽  
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Optimization Methods ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Minimum Number ◽  
Grasshopper Optimization ◽  
Design Pressure

This paper proposes a new meta-heuristic swarm optimization algorithm called Cicada Swarm Optimization (CISO) algorithm, which mimics the behavior of bio-inspired swarm optimization methods. The CISO algorithm is tested with 23 benchmark functions and taken two problems engineering design, pressure vessel problem and himmelblau’s problem. The performance of CISO algorithm is compared with meta-heuristic well-known and recently proposed algorithms (Cockroach Swarm Optimization (CSO), Grasshopper Optimization algorithm (GOA) and Particle Swarm Optimization (PSO)). The obtained results showed that the proposed algorithm succeeded in improving the test functions and solved engineering design problems that could not be improved by other algorithms according to the chosen parameters and the limits of the research space, also showed that CISO has a faster convergence with the minimum number of iterations and also have an accurate calculation efficiency Satisfactory compared to other optimization algorithms.

scholarly journals Grasshopper Optimization Algorithm: Theory, Variants, and Applications

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 50001-50024
Author(s):  
Yassine Meraihi ◽  
Asma Benmessaoud Gabis ◽  
Seyedali Mirjalili ◽  
Amar Ramdane-Cherif
Keyword(s):  
Optimization Algorithm ◽  
Grasshopper Optimization Algorithm ◽  
Algorithm Theory ◽  
Grasshopper Optimization

scholarly journals Combination of Intermittent Search Strategy and an Improve Particle Swarm Optimization algorithm (IPSO) for damage detection of steel frame

2021 ◽  
Vol 16 (59) ◽  
pp. 141-152
Author(s):  
Cuong Le Thanh ◽  
Thanh Sang-To ◽  
Hoang-Le Hoang-Le ◽  
Tran-Thanh Danh ◽  
Samir Khatir ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Search Strategy ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Search Speed ◽  
Eagle Strategy

Modality and intermittent search strategy in combination with an Improve Particle Swarm Optimization algorithm (IPSO) to detect damage structure via using vibration analysis basic principle of a decline stiffness matrix a structure is presented in the study as a new technique. Unlike an optimization problem using a simplistic algorithm application, the combination leads to promising results. Interestingly, the PSO algorithm solves the optimal problem around the location determined previously. In contrast, Eagle Strategy (ES) is the charging of locating the position in intermittent space for the PSO algorithm to search locally. ES is easy to deal with its problem via drastic support of Levy flight. As known, the PSO algorithm has a fast search speed, yet the accuracy of the PSO algorithm is not as good as expected in many problems. Meanwhile, the combination is powerful to solve two problems: 1) avoiding local optimization, and 2) obtaining more accurate results. The paper compares the results obtained from the PSO algorithm with the combination of IPSO and ES for some problems and between experiment and FEM to demonstrate its effectiveness. Natural frequencies are used in the objective function to solve this optimization problem. The results show that the combination of IPSO and ES is quite effective.

Influence of Optimization Algorithm on Airfoil Shape Optimization of Aircraft Wings

2012 ◽  
Vol 232 ◽  
pp. 614-619
Author(s):  
R. Mukesh ◽  
K. Lingadurai ◽  
K. Elamvaluthi
Keyword(s):  
Particle Swarm Optimization ◽  
Shape Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Aerodynamic Shape Optimization ◽  
Swarm Optimization ◽  
Shape Optimization Problem ◽  
Aerodynamic Shape

Computational fluid dynamics (CFD) is one of the computer-based solution methods which are more widely employed in aerospace engineering. The computational power and time required to carry out the analysis increases as the fidelity of the analysis increases. Aerodynamic shape optimization has become a vital part of aircraft design in the recent years. The Method of search algorithms or optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays various optimization methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc., are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterisation becomes an important factor to be considered during the aerodynamic shape optimization process. Generally if we want to optimize an airfoil we have to describe the airfoil and for that, we need to have at least hundred points of x and y co-ordinates. It is really difficult to optimize airfoils with this large number of co-ordinates. Nowadays many different schemes of parameter sets are used to describe general airfoil such as B-spline, Hicks- Henne Bump function, PARSEC etc. The main goal of these parameterization schemes is to reduce the number of needed parameters as few as possible while controlling the important aerodynamic features effectively. Here the work has been done on the PARSEC geometry representation method. The objective of this work is to introduce the knowledge of describing general airfoil using twelve parameters by representing its shape as a polynomial function. And also we have introduced the concept of Particle Swarm optimization Algorithm which is one kind of Non-Traditional Optimization technique to optimize the aerodynamic characteristics of a general airfoil for specific conditions. An aerodynamic shape optimization problem is formulated for NACA 2411 airfoil and solved using the method of Particle Swarm Optimization for 5.0 deg angle of attack. A MATLAB program has been developed to implement PARSEC, Panel Technique, and PSO Algorithm. This program has been tested for a standard NACA 2411 airfoil and optimized to improve its coefficient of lift. Pressure distribution and co-efficient of lift for airfoil geometries has been calculated using panel method. NACA 2411 airfoil has been generated using PARSEC and optimized for 5.0 deg angle of attack using PSO Algorithm. The results show that the particle swarm optimization scheme is more effective in finding the optimum solution among the various possible solutions.

scholarly journals An Improved Grasshopper Optimizer for Global Tasks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Hanfeng Zhou ◽  
Zewei Ding ◽  
Hongxin Peng ◽  
Zitao Tang ◽  
Guoxi Liang ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Genetic Mutation ◽  
Convergence Speed ◽  
Search System ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Cauchy Mutation ◽  
Grasshopper Optimization ◽  
Unconstrained Problems

The grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full use of the strong searchability of Cauchy mutation and the diversity of genetic mutation. Then, the effective factors of grasshopper swarm are strengthened by an orthogonal learning mechanism to improve the convergence speed of the algorithm. Moreover, the application of probability in this paper greatly balances the advantages of each strategy and improves the comprehensive ability of the original GOA. Note that several representative benchmark functions are used to evaluate and validate the proposed MOLGOA. Experimental results demonstrate the superiority of MOLGOA over other well-known methods both on the unconstrained problems and constrained engineering design problems.

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