Comparison of grey wolf, whale, water cycle, ant lion and sine-cosine algorithms for the optimization of a vehicle engine connecting rod

2018 ◽  
Vol 60 (3) ◽  
pp. 311-315 ◽  
Author(s):  
Betül Sultan Yıldız ◽  
Ali Rıza Yıldız
Keyword(s):  
Water Cycle ◽  
Grey Wolf ◽  
Connecting Rod ◽  
Vehicle Engine ◽  
Ant Lion

scholarly journals Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers

2021 ◽  
Vol 13 (4) ◽  
pp. 2336
Author(s):  
Hossein Moayedi ◽  
Amir Mosavi
Keyword(s):  
Power Plants ◽  
Water Cycle ◽  
Electrical Power ◽  
Optimal Solution ◽  
Population Based ◽  
Influential Factors ◽  
Combined Cycle ◽  
Power Prediction ◽  
Satin Bowerbird ◽  
Ant Lion

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented.

scholarly journals Search-in-forest optimizer: A bio-inspired meta-heuristic algorithm for global optimization problems

2021 ◽  
Author(s):  
Atefeh Amindoust ◽  
Amin Ahwazian ◽  
Reza Tavakkoli-Moghaddam ◽  
Mehrdad Nikbakhta
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Mathematical Structure ◽  
Grey Wolf ◽  
Missing Persons ◽  
Search Field ◽  
Ant Lion ◽  
Search Structure ◽  
Group Search ◽  
Numerical Representations

Abstract The present research proposes a new particle swarm optimization-based metaheuristic algorithm entitled “search in forest (SIF) optimizer” to solve the global optimization problems. The algorithm is designed based on the organized behavior of search teams looking for missing persons in a forest. According to SIF optimizer, a number of teams each including several experts in the search field spread out across the forest and gradually move in the same direction by finding clues from the target until they find the missing person. This search structure was designed in a mathematical structure in the form of intra-group search operators and transferring the expert member to the top team. In addition, the efficiency of the algorithm was assessed by comparing the results to the standard representations and a problem with the genetic, grey wolf, salp swarm, and ant lion optimizers. According to the results, the proposed algorithm was efficient for solving many numerical representations, compared to the other algorithms.

scholarly journals Stress and Fatigue of Connecting Rod in Light Vehicle Engine

2013 ◽  
Vol 7 (1) ◽  
pp. 14-17 ◽  
Author(s):  
Bin Zheng ◽  
Yongqi Liu ◽  
Ruixiang Liu
Keyword(s):  
Safety Factor ◽  
Maximum Stress ◽  
Critical Surface ◽  
Connecting Rod ◽  
Finite Element Software ◽  
Vehicle Engine ◽  
Maximum Stretch ◽  
Light Vehicle ◽  
Engine Failure ◽  
Stress And Deformation

The connecting rod (CR) is the main moving parts and an important component of engine. If the reliability is not strong enough, fatigue failure of the CR would occur, thereby leading to component fracture and engine failure. So much so that CR fracture. It will lead to engine fault as well as serious outcome. In this paper, stress distribution and fatigue life of CR in light vehicle engine were analyzed using the commercial 3D finite element software, ANSYSTM. The results showed that the medial surface of small end will be the critical surface whereby damage will initiate at the maximum stretch condition. The maximum stress and deformation values are 190.23 MPa and 0.0507mm respectively. The critical location is at the transition region between the big end and connecting shank at maximum compression condition. The maximum stress and deformation values are 459.21 MPa and 0.0702283 mm respectively. Safety factor is 1.584. In order to increase the reliability of CR, some improvement is carried out. Safety factor of CR increases by 59%.

Failure analysis of a special vehicle engine connecting rod

2017 ◽  
Vol 79 ◽  
pp. 98-109 ◽  
Author(s):  
Slavko Rakic ◽  
Ugljesa Bugaric ◽  
Igor Radisavljevic ◽  
Zeljko Bulatovic
Keyword(s):  
Failure Analysis ◽  
Connecting Rod ◽  
Vehicle Engine ◽  
Special Vehicle

scholarly journals An Adaptive Hybrid Routing Scheme For Energy Management In Wireless Sensor Networks

2021 ◽  
Author(s):  
Raviteja Kocherla ◽  
Chandra sekhar M ◽  
Ramesh Vatambeti
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Life Time ◽  
Residual Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Hybrid Routing ◽  
Huge Data ◽  
Ant Lion

Abstract In Wireless Sensor Network (WSN) the life time of nodes and energy management are important issues, because the nodes in WSN required more energy when it is used in different applications. On the other hand, unstable energy consumption among intermediate nodes tends to huge data loss. To address this problem the present research introduced a novel Hybrid Gossip Grey Wolf Ant lion (HGGW-AL) protocol to afford an efficient and better transmission channel. Here, the fitness of grey wolf and ant lion helps to categorize the energy drained node and also, to predict the malicious activities. Furthermore, the novel Rest Awake (RA) is initialized to process the clustering strategy to maintain the residual energy in WSN. Moreover, it enhances the energy level of sensor nodes by increasing its lifetime. Finally, the efficiency of the proposed strategy is compared with the existing works and achieved better performance by reducing the energy consumption of each sensor node.

scholarly journals Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 901
Author(s):  
Medine Colak ◽  
Mehmet Yesilbudak ◽  
Ramazan Bayindir
Keyword(s):  
Solar Radiation ◽  
Solar Energy ◽  
Multilayer Perceptron ◽  
Grey Wolf ◽  
Activation Functions ◽  
Power Prediction ◽  
Grey Wolf Optimization ◽  
Photovoltaic Power ◽  
Whale Optimization ◽  
Ant Lion

Solar energy is a safe, clean, environmentally-friendly and renewable energy source without any carbon emissions to the atmosphere. Therefore, there are many studies in the field of solar energy in order to obtain the maximum solar radiation during the day time, to estimate the amount of solar energy to be produced, and to increase the efficiency of solar energy systems. In this study, it was aimed to predict the daily photovoltaic power production using air temperature, relative humidity, total horizontal solar radiation and diffuse horizontal solar radiation parameters as multi-tupled inputs. For this purpose, grey wolf, ant lion and whale optimization algorithms were integrated to the multilayer perceptron. In addition, the effects of sigmoid, sinus and hyperbolic tangent activation functions on the prediction performance were analyzed in detail. As a result of overall accuracy indictors achieved, the grey wolf optimization algorithm-based multilayer perceptron model was found to be more successful and competitive for the daily photovoltaic power prediction. Furthermore, many meaningful patterns were revealed about the constructed models, input tuples and activation functions.

scholarly journals Performance and evaluation of energy optimization techniques for wireless body area networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Naveen Bilandi ◽  
Harsh Kumar Verma ◽  
Renu Dhir
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Relay Node ◽  
Body Area Networks ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Body Area ◽  
Swarm Optimization ◽  
Ant Lion Optimization ◽  
Ant Lion

Abstract Background Wireless body area networks are created to retrieve and transmit human health information by using sensors on the human body. Energy efficiency is considered a foremost challenge to increase the lifetime of a network. To deal with energy efficiency, one of the important mechanisms is selecting the relay node, which can be modeled as an optimization problem. These days nature-inspired algorithms are being widely used to solve various optimization problems. With regard to this, this paper aims to compare the performance of the three most recent nature-inspired metaheuristic algorithms for solving the relay node selection problem. Results It has been found that the total energy consumption calculated using grey wolf optimization decreased by 23% as compared to particle swarm optimization and 16% compared to ant lion optimization. Conclusions The results suggest that grey wolf optimization is better than the other two techniques due to its social hierarchy and hunting behavior. The findings showed that, compared to well-known heuristics such as particle swarm optimization and ant lion optimization, grey wolf optimization was able to deliver extremely competitive results. Graphical Abstract

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