scholarly journals An Explosion Based Algorithm to Solve the Optimization Problem in Quadcopter Control

Aerospace ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 125
Author(s):  
Mohamad Norherman Shauqee ◽  
Parvathy Rajendran ◽  
Nurulasikin Mohd Suhadis
Keyword(s):  
Comparative Study ◽  
Optimization Algorithm ◽  
Global Optimum ◽  
The Other ◽  
Lower Percentage ◽  
Bee Colony ◽  
Local Space ◽  
Benchmark Test Functions ◽  
Control Application ◽  
Sooty Tern

This paper presents an optimization algorithm named Random Explosion Algorithm (REA). The fundamental idea of this algorithm is based on a simple concept of the explosion of an object. This object is commonly known as a particle: when exploded, it will randomly disperse fragments around the particle within the explosion radius. The fragment that will be considered as a search agent will fill the local space and search that particular region for the best fitness solution. The proposed algorithm was tested on 23 benchmark test functions, and the results are validated by a comparative study with eight well-known algorithms, which are Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Multi-Verse Optimizer (MVO), Moth Flame Optimizer (MFO), Firefly Algorithm (FA), and Sooty Tern Optimization Algorithm (STOA). After that, the algorithm was implemented and analyzed for a quadrotor control application. Similarly, a comparative study with the other algorithms stated was done. The findings reveal that the REA can yield very competitive results. It also shows that the convergence analysis has proved that the REA can converge more quickly toward the global optimum than the other metaheuristic algorithms. For the control application result, the REA controller can better track the desired reference input with shorter rise time and settling time, lower percentage overshoot, and minimal steady-state error and root mean square error (RMSE).

A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm

2016 ◽  
Vol 15 (02) ◽  
pp. 1650011 ◽  
Author(s):  
Wang Yong ◽  
Wang Tao ◽  
Zhang Cheng-Zhi ◽  
Huang Hua-Juan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Bat Algorithm ◽  
Global Optimum ◽  
The Other ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Fast Convergence Rate

A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.

scholarly journals Introducing modified version of penguins search optimization algorithm (PeSOA) and its application in optimal operation of reservoir systems

2017 ◽  
Vol 18 (4) ◽  
pp. 1484-1496 ◽  
Author(s):  
Afshin Mansouri ◽  
Babak Aminnejad ◽  
Hassan Ahmadi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Real World ◽  
Global Optimum ◽  
Optimal Operation ◽  
The Other ◽  
Reservoir System ◽  
Search Optimization ◽  
Real World Problem ◽  
Reservoir Systems

Abstract In the current study, modified version of the penguins search optimization algorithm (PeSOA) was introduced, and its usage was assessed in the water resources field. In the modified version (MPeSOA), the Gaussian exploration was added to the algorithm. The MPeSOA performance was evaluated in optimal operation of a hypothetical four-reservoir system and Karun-4 reservoir as a real world problem. Also, genetic algorithm (GA) was used as a criterion for evaluating the performance of PeSOA and MPeSOA. The results revealed that in a four-reservoir system problem, the PeSOA performance was much weaker than the GA; but on the other hand, the MPeSOA had better performance than the GA. In the mentioned problem, PeSOA, GA, and MPeSOA reached 78.43, 97.46, and 98.30% of the global optimum, respectively. In the operation of Karun-4 reservoir, although PeSOA performance had less difference with the two other algorithms than four-reservoir problem, its performance was not acceptable. The average values of objective function in this case were equal to 26.49, 23.84, and 21.48 for PeSOA, GA, and MPeSOA, respectively. According to the results obtained in the operation of Karun-4 reservoir, the algorithms including MPeSOA, GA, and PeSOA were situated in ranks one to three in terms of efficiency, respectively.

Learning automata-based butterfly optimization algorithm for engineering design problems

2018 ◽  
Vol 07 (04) ◽  
pp. 1850021 ◽  
Author(s):  
Sankalap Arora ◽  
Priyanka Anand
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Random Search ◽  
Learning Automata ◽  
Global Optimum ◽  
Design Problems ◽  
Local Optima ◽  
Learning Automaton ◽  
Engineering Design Problems ◽  
Benchmark Test Functions

Butterfly Optimization Algorithm (BOA) is a novel meta-heuristic algorithm inspired by the food foraging behavior of the butterflies. The performance of BOA critically depends upon the probability parameter which decides whether the butterfly has to move towards the best butterfly of the population or perform a random search. Therefore, in order to increase the potential of the BOA, which focuses on exploration phase in the initial stages and on exploitation in the later stages of the optimization, learning automata have been embedded in BOA in which a learning automaton takes the role of configuring the behavior of a butterfly in order to create a proper balance between the process of global and local search. The introduction of learning automata accelerates the global convergence speed to the true global optimum while preserving the main feature of the basic BOA. In order to validate the effectiveness of the proposed algorithm, it is evaluated on 17 benchmark test functions and 3 classical engineering design problems with different characteristics, having real-world applications. The simulation results demonstrate that the introduction of learning automata in BOA has significantly boosted the performance of BOA in terms of achievement of true global optimum and avoidance of local optima entrapment.

Comparative Study of Proactivators of the Fibrinolysin System in Three Mammalian Species

1969 ◽  
Vol 21 (03) ◽  
pp. 594-603 ◽  
Author(s):  
Y Takada ◽  
A Takada ◽  
J. L Ambrus
Keyword(s):  
Guinea Pig ◽  
Comparative Study ◽  
Human Plasma ◽  
Mammalian Species ◽  
Gel Filtration ◽  
Plasminogen Activators ◽  
The Other ◽  
Rabbit Plasma ◽  
Plate Test ◽  
Fibrin Plate

SummarySephadex gel filtration of human plasma gave results suggesting the presence of two proactivators of plasminogen, termed proactivators A and B.Activity resembling that of proactivator A was found in rabbit plasma, but not in guinea pig plasma.Plasminogen activators produced by the interaction of proactivator A of human plasma with streptokinase had no caseinolytic or TAMe esterolytic effect.Proactivator A can be separated in a form apparently free from plasminogen, as shown by the heated fibrin plate test and by immunological analysis. On the other hand, proactivator B concentrates prepared so far are contamined with plasminogen.Human proactivators appear to be far more susceptible to streptokinase than are rabbit proactivators.Inhibitors of the fibrinolysin system were observed in the plasmas of all 3 species. These inhibitors are not present in the euglobulin fraction of plasma. Sephadex fractionation of euglobulin fractions results in proactivator preparations that do not contain inhibitors.

Empire Loyalism and Nationalism in Ukraine and Ireland

2020 ◽  
Vol 53 (3) ◽  
pp. 88-106
Author(s):  
Taras Kuzio
Keyword(s):  
Soviet Union ◽  
Northern Ireland ◽  
Comparative Study ◽  
The Other ◽  
History And Memory ◽  
Memory Politics ◽  
Irish History ◽  
British Rule ◽  
Five Factors ◽  
Post Colonialism

This is the first comparative article to investigate commonalities in Ukrainian and Irish history, identity, and politics. The article analyzes the broader Ukrainian and Irish experience with Russia/Soviet Union in the first and Britain in the second instance, as well as the regional similarities in conflicts in the Donbas region of Eastern Ukraine and the six of the nine counties of Ulster that are Northern Ireland. The similarity in the Ukrainian and Irish experiences of treatment under Russian/Soviet and British rule is starker when we take into account the large differences in the sizes of their territories, populations, and economies. The five factors that are used for this comparative study include post-colonialism and the “Other,” religion, history and memory politics, language and identities, and attitudes toward Europe.

Liturgical Poetry in the Late Antique Near East

2010 ◽  
Vol 1 (3) ◽  
pp. 336-361 ◽  
Author(s):  
Ophir Münz-Manor
Keyword(s):  
Comparative Study ◽  
Late Antiquity ◽  
Near East ◽  
The Other ◽  
Late Antique ◽  
Biblical Poetry ◽  
Contemporary View ◽  
Cultural Dialogue ◽  
The One

The article presents a contemporary view of the study of piyyut, demonstrating that Jewish poetry of late antiquity (in Hebrew and Aramaic) was closely related to Christian liturgical poetry (both Syriac and Greek) and Samaritan liturgy. These relations were expressed primarily by common poetic and prosodic characteristics, derived on the one hand from ancient Semitic poetry (mainly biblical poetry), and on the other from innovations of the period. The significant connections of content between the different genres of poetry reveal the importance of comparative study. Thus the poetry composed in late antiquity provides additional evidence for the lively cultural dialogue that took place at that time.

scholarly journals A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1477
Author(s):  
Chun-Yao Lee ◽  
Guang-Lin Zhuo
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Initial Population ◽  
Test Functions ◽  
Benchmark Test ◽  
Thermal Exchange ◽  
Whale Optimization ◽  
Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

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