Grass Fibrous Root Optimization Algorithm

Akkar, Hanan A. R.; Mahdi, Firas R.

doi:10.5815/ijisa.2017.06.02

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…In the optimization field, a set of mathematical functions with optimal solutions is usually used to test the performance of different optimization algorithms quantitatively and the test functions should be diverse so that the conclusions are not too one-sided. In this paper, three groups of test functions with different characteristics are used to benchmark the performance of the proposed algorithm which are unimodal functions, multimodal functions and fixeddimension multimodal functions [27][28][29][30].The specific form of the function is given in Tables (3)- (5), where represents the dimension of the function, Range represents the range of independent variables, that is, the range of population, and represents the minimum value of the function. Fig.…”

Section: Tests Performance Of Algorithmmentioning

confidence: 99%

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Akkar¹,

Salman²

2020

IJIES

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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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Section: Tests Performance Of Algorithmmentioning

confidence: 99%

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Akkar¹,

Salman²

2020

IJIES

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“…41,42 Considering the logistics map mechanism and chaos conception, the suggested new FSO direction is as follows: Figure 3 shows the flowchart diagram of the IFSO algorithm. To validate the suggested IFSO algorithm, four different benchmark functions are used and the results are compared with some various meta-analysis processes such as CGOA, 43 GRA, 44 and basic FSO. 44…”

Section: Fluid Search Optimization Algorithm Based On Chaos Theory (Imentioning

confidence: 99%

Evaluating climate changes and land use changes on water resources using hybrid Soil and Water Assessment Tool‐DEEP optimized by metaheuristics

Liu

Mizzi

2020

Concurrency and Computation

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The objective of this research is to investigate the effect of land-use and climate change on the volume of runoff produced and its impact on water resources. It is clear that the more accurate the assessment of runoff in the future, the better the evaluation of the impact of land-use and climate changes on water resources. Thus, to more accurately predict water sources in the future, runoff is estimated using two Soil and Water Assessment Tool (SWAT) and SWAT-DEEP-IFSO models. The results showed that the SWAT-DEEP-IFSO model has a better estimation than the SWAT method. Because Deep Belief Network and an improved optimization algorithm called Improved Fluid Search Optimization (IFSO) algorithm have been utilized in optimizing the hybrid SWAT model that can reduce the error of runoff simulation. After selecting the best model for estimating runoff, the effect of land-use and climate change is evaluated using different scenarios. Results show that the simulated runoff under the influence of climate and land-use changes can impact on the average yearly and monthly runoff. These factors have a significant effect on the average yearly runoff changes and are predicted an increasing trend for average yearly runoff. But climate and land-use changes have a different impact on the different months and for some months of the year the runoff has an increasing trend and for some months the runoff has a decreasing trend. Based on land-use scenarios, it is found that due to the decrease in rangelands and increase in agricultural lands, there is a possibility of severe floods and drought periods in the region, which reduces the water resources of the study area. K E Y W O R D S deep belief network, fluid search optimization algorithm, runoff, soil and water assessment tool, water resource 1 INTRODUCTION Today, all climate scientists agree that global warming causes important climatic phenomena and due to climate changes, the probability of heavy rainfall and flood risk has been increased. 1 Global warming dramatically affects rainfall characteristics, soil moisture, water levels of the seas and oceans, and airflow patterns. Also, it can cause severe floods and storms and reduce glaciers. Reducing snowfall and rain and increasing drought, thereby increasing soil salinity and reducing freshwater resources in coastal areas can be another result of global warming. 2 One of the effects of climate change can be seen in renewables resources such as water resources which have different impacts from point-to-point. This effect of

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“…Sandeep et al [24] have proposed a modified PSO algorithm to train the Radial Basis Function Neural Network (RBFNN) more efficiently to classify the epileptic brain seizures. Akkar et al [25,26] have proposed a GRO algorithm to control mobile robot path tracking; also they conclude that GRO has faster convergence with a minimum number of iterations than other ten compared algorithms.…”

Section: Ann Weights Optimization Algorithmsmentioning

confidence: 99%

Intelligent Training Algorithm for Artificial Neural Network EEG Classifications

Akkar¹,

Jasim²

2018

IJISA

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Abstract-Artificial neural networks (ANN) have been widely used in classification. They are complicated networks due to the training algorithm used to fix their weights. To achieve better neural network performance, many evolutionary and meta-heuristic algorithms are used to optimize the network weights. The aim of this paper is to implement recently evolutionary algorithms for optimizing neural weights such as Grass Root Optimization (GRO), Artificial Bee Colony (ABC), Cuckoo Search Optimization (CSA) and Practical Swarm Optimization (PSO). This ANN was examined to classify three classes of EEG signals healthy subjects, subjects with interictal epilepsy seizure, and subjects with ictal epilepsy seizures. The above training algorithms are compared according to classification rate, training and testing mean square error, average time, and maximum iteration.

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Grass Fibrous Root Optimization Algorithm

Cited by 14 publications

References 11 publications

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

Evaluating climate changes and land use changes on water resources using hybrid Soil and Water Assessment Tool‐DEEP optimized by metaheuristics

Intelligent Training Algorithm for Artificial Neural Network EEG Classifications

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