Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics

Bayraktar, Zikri; Kömürcü, Müge; Werner, Douglas H.

doi:10.1109/aps.2010.5562213

Cited by 162 publications

(96 citation statements)

References 4 publications

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“…The CS algorithm has a better explorative capability. Metaheuristic algorithms developed in recent years by many researchers are Firefly Algorithm (FA) [12] [13], Bat algorithm [14] [15], Stochastic Fractal Search (SFS) [16], Wind Driven Algorithm (WDA) [17], Grey Wolf Optimizer (GWO) [18], Symbiotic Organisms Search (SOS) [19] and Grass Fibrous Root Optimization Algorithm (GRA) [20]. A comparison of GA, ACO, Reverse Formation Dynamics (RFD), FA and CS algorithm for Traveling Sales Man problem had been studied in [21].…”

Section: Related Workmentioning

confidence: 99%

Performance Estimation of Differential Evolution, Particle Swarm Optimization and Cuckoo Search Algorithms

Prajapati¹,

Shah²

2018

IJISA

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Abstract-Most design optimization problems in engineering are in general extremely nonlinear and deal with various design variables under complex restrictions. Traditional mathematical optimization procedure may fail to find the optimum solution to real-world problems. Evolutionary Algorithms (EAs) can serve as an efficient approach for these types of optimization problems. In this paper, Particle Swarm Optimization (PSO), Differential Evolution (DE) and Cuckoo Search (CS) algorithms are used to find the optimal solution for some typical unimodal and multimodal benchmark functions. The source codes of all these algorithms are developed using C language and tested on a core i5, 2.4 GHz processor with 8 GB internal RAM. PSO algorithm has a simplicity of implementation and good convergence speed. In contrast, CS algorithm has good ability to find a global optimum solution. To use the advantages of CS and PSO algorithms, a hybrid algorithm of CS and PSO (CSPSO) is implemented and tested with the same benchmark functions. The experimental simulation results obtained by all these algorithms show that hybrid CSPSO outperforms with PSO, DE and CS algorithms.

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Section: Related Workmentioning

confidence: 99%

Performance Estimation of Differential Evolution, Particle Swarm Optimization and Cuckoo Search Algorithms

Prajapati¹,

Shah²

2018

IJISA

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“…The Wind Driven Optimization algorithm, which was introduced in [7], is a nature inspired population based iterative heuristic global optimization method and can be implemented in any field and application where GA, PSO or any type of evolutionary strategy are utilized [8,9]. It was inspired by the physical equations describing the trajectory of an individual air parcel under the influence of various natural forces in our atmosphere in hydrostatic balance.…”

Section: Wind Driven Optimization (Wdo)mentioning

confidence: 99%

“…The position and the velocity of the air parcel are iteratively updated in the search space until it converges to an optimum point or maximum number of iterations is reached. A detailed description of the algorithm and the parameter study can be found in [6] and [7], hence we will only present the velocity and position update rules below. The velocity update equation is expressed as,…”

Section: Wind Driven Optimization (Wdo)mentioning

confidence: 99%

Adaptive Wind Driven Optimization

Bayraktar¹,

Kömürcü²

2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

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In this paper, we propose two new methods to create an adaptive Wind Driven Optimization (WDO) algorithm, both of which are shown to outperform the classical WDO method while eliminating the need for fine-tuning the coefficients of the update equations. While the classical WDO offers a simple and efficient metaheuristic optimization algorithm, the coefficients that are inherent to the workings of the algorithm create an undesired level of complexity especially for the novice users. To alleviate this complexity and automate the coefficient selection, two adaptive Wind Driven Optimization (AWDO) methods are proposed in this paper. First method is to replace the fixed values of the coefficients with randomly generated numbers from a uniform distribution at each iteration and the second method is to optimize the selection of the coefficients with the Covariance Matrix Adaptation Evaluation Strategy (CMAES). To evaluate the performance of the proposed methods for AWDO, four wellknown numerical benchmark functions from the literature are utilized and results are compared against the classical WDO. Both of the new methods outperform the classical WDO while the AWDO using CMAES performs the best among of all.

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“…In order to test the proposed metaheuristic GRA, seven standard test functions will be used. The obtained results will be compared with other nine well-known, and recently proposed algorithms which are Particle Swarm Optimization (PSO) [4], Differential Evolutionary algorithm (DEA) [5], Bee Colony Optimization (BCO) [6], Cuckoo Search Algorithm (CSO) [7], Wind Driven Algorithm (WDA) [8], Stochastic Fractal Search (SFS) [9], Symbiotic Organisms Search (SOS) [10], Grey Wolf Optimizer (GWO) [11], and Novel Bat Algorithm (NBA) [12]. This work proposes and implements a general grass root optimization algorithm GRA, comparing it with other meta-heuristic algorithms through using a variety of test function to evaluate the average mean absolute error, average number of effective iteration, and average effective processing time.…”

Section: Introductionmentioning

confidence: 99%

Grass Fibrous Root Optimization Algorithm

Akkar¹,

Mahdi²

2017

IJISA

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Abstract-This paper proposes a novel meta-heuristic optimization algorithm inspired by general grass plants fibrous root system, asexual reproduction, and plant development. Grasses search for water and minerals randomly by developing its location, length, primary root, regenerated secondary roots, and small branches of roots called hair roots. The proposed algorithm explore the bounded solution domain globally and locally. Globally using the best grasses survived by the last iteration, and the root system of the best grass obtained so far by the iteration process and locally uses the primary roots, regenerated secondary roots and hair roots of the best global grass. Each grass represents a global candidate solution, while regenerated secondary roots stand for the locally obtained solution. Secondary generated hair roots are equal to the problem dimensions. The performance of the proposed algorithm is tested using seven standard benchmark test functions, comparing it with other metaheuristic well-known and recently proposed algorithms.

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Wind Driven Optimization (WDO): A novel nature-inspired optimization algorithm and its application to electromagnetics

Cited by 162 publications

References 4 publications

Performance Estimation of Differential Evolution, Particle Swarm Optimization and Cuckoo Search Algorithms

Performance Estimation of Differential Evolution, Particle Swarm Optimization and Cuckoo Search Algorithms

Adaptive Wind Driven Optimization

Grass Fibrous Root Optimization Algorithm

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