Greenfly Aphid Swarm Optimization Algorithm

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.

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Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2020.1231.25 ◽

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.

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A Hybrid Co-evolutionary Particle Swarm Optimization Algorithm for Solving Constrained Engineering Design Problems

Journal of Computers ◽

10.4304/jcp.5.6.965-972 ◽

2010 ◽

Vol 5 (6) ◽

Cited By ~ 3

Author(s):

Yongquan Zhou ◽

Shengyu Pei

Keyword(s):

Particle Swarm Optimization ◽

Engineering Design ◽

Optimization Algorithm ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Design Problems ◽

Swarm Optimization ◽

Engineering Design Problems

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A Hybrid Glowworm Swarm Optimization Algorithm for Constrained Engineering Design Problems

Applied Mathematics & Information Sciences ◽

10.12785/amis/070147 ◽

2013 ◽

Vol 7 (1) ◽

pp. 379-388 ◽

Cited By ~ 24

Author(s):

Yongquan Zhou ◽

Guo Zhou ◽

Junli Zhang

Keyword(s):

Engineering Design ◽

Optimization Algorithm ◽

Design Problems ◽

Swarm Optimization ◽

Glowworm Swarm Optimization ◽

Engineering Design Problems

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Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2019010103 ◽

2019 ◽

Vol 10 (1) ◽

pp. 38-57 ◽

Cited By ~ 9

Author(s):

Sunanda Hazra ◽

Tapas Pal ◽

Provas Kumar Roy

Keyword(s):

Renewable Energy ◽

Wind Power ◽

Optimization Algorithm ◽

Renewable Energy Sources ◽

Optimization Technique ◽

Wind Farms ◽

Integrated Approach ◽

Fitness Value ◽

Grasshopper Optimization Algorithm ◽

Grasshopper Optimization

This article presents an integrated approach towards the economical operation of a hybrid system which consists of conventional thermal generators and renewable energy sources like windmills using a grasshopper optimization algorithm (GOA). This is based on the social interaction nature of the grasshopper, considering a carbon tax on the emissions from the thermal unit and uncertainty in wind power availability. The Weibull distribution is used for nonlinearity of wind power availability. A standard system, containing six thermal units and two wind farms, is used for testing the dispatch model of three different loads. The GOA results are compared with those obtained using a recently developed quantum-inspired particle swarm optimization (QPSO) optimization technique available in the literature. The simulation results demonstrate the efficacy and ability of GOA over the QPSO algorithm in terms of convergence rate and minimum fitness value. Performance analysis under wind power integration and emission minimization further confirms the supremacy of the GOA algorithm.

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Index-based transmission for distributed generation in voltage stability and loss control incorporating optimization technique

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v9.i2.pp244-251 ◽

2020 ◽

Vol 9 (2) ◽

pp. 244

Author(s):

Fareed Danial Ahmad Kahar ◽

Ismail Musirin ◽

Muhamad Faliq Mohamad Nazer ◽

Shahrizal Jelani ◽

Mohd Helmi Mansor

Keyword(s):

Distributed Generation ◽

Optimization Algorithm ◽

Voltage Stability ◽

Optimization Technique ◽

Stability Index ◽

Test System ◽

Voltage Security ◽

Grasshopper Optimization Algorithm ◽

Grasshopper Optimization ◽

Loss Control

<span lang="EN-US">The integration of Distributed Generation (DG) in a distribution network may significantly affect distribution performance. With the penetration of DG, voltage security is no longer an issue in the transmission network. This paper presents a study of Distributed Generation on the IEEE 26-Bus Reliability Test System (RTS) with the use of Fast Voltage Stability Index (FVSI) for determining its location and incorporated with Grasshopper Optimization Algorithm (GOA) to optimize the sizing of the DG. The study emphasizes the power loss of the system in which a comparison between Evolutionary Programming (EP) and Grasshopper Optimization Algorithm is done to determine which optimization technique gives an optimal result for the DG solution. The results show that the proposed algorithm is able to provide a slightly better result compared to EP.</span>

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An Improved Grasshopper Optimizer for Global Tasks

Complexity ◽

10.1155/2020/4873501 ◽

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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Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm

Research Anthology on Clean Energy Management and Solutions ◽

10.4018/978-1-7998-9152-9.ch036 ◽

2021 ◽

pp. 869-890

Author(s):

Sunanda Hazra ◽

Tapas Pal ◽

Provas Kumar Roy

Keyword(s):

Renewable Energy ◽

Wind Power ◽

Optimization Algorithm ◽

Carbon Tax ◽

Renewable Energy Sources ◽

Optimization Technique ◽

Wind Farms ◽

Integrated Approach ◽

Grasshopper Optimization Algorithm ◽

Grasshopper Optimization

This article presents an integrated approach towards the economical operation of a hybrid system which consists of conventional thermal generators and renewable energy sources like windmills using a grasshopper optimization algorithm (GOA). This is based on the social interaction nature of the grasshopper, considering a carbon tax on the emissions from the thermal unit and uncertainty in wind power availability. The Weibull distribution is used for nonlinearity of wind power availability. A standard system, containing six thermal units and two wind farms, is used for testing the dispatch model of three different loads. The GOA results are compared with those obtained using a recently developed quantum-inspired particle swarm optimization (QPSO) optimization technique available in the literature. The simulation results demonstrate the efficacy and ability of GOA over the QPSO algorithm in terms of convergence rate and minimum fitness value. Performance analysis under wind power integration and emission minimization further confirms the supremacy of the GOA algorithm.

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