The Whale Optimization Algorithm

Mirjalili, Seyedali; Lewis, Andrew

doi:10.1016/j.advengsoft.2016.01.008

Cited by 8,628 publications

(3,966 citation statements)

References 72 publications

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“…Whale optimization algorithm (WOA) 18 is a metaheuristic based algorithm which mimics the bubble-net hunting strategy of humpback whales. In this strategy, the whale follows stereotypical bubbles motivates the creation of circular or 9-shaped path while circumscribing victim during hunting.…”

Section: Proposed Methodsologymentioning

confidence: 99%

Diabetes Data Classification Using Whale Optimization Algorithm and Backpropagation Neural Network

Rajeshkumar¹,

Kousalya²

2017

Int. Res. J. Pharm.

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Diabetes also called as diabetes mellitus is a health issue which affects more people in a world. Diagnosis of diabetic problem depends on different parameters and requires experience or good algorithm to classify it optimally. Many researchers have found different classification algorithms to diagnose this health issue with promising results. In this paper, combinations of whale optimization algorithm and backpropagation neural network methodology are integrated to diagnose diabetes mellitus. This proposed method supports high convergence speed and improved accuracy. Due to this combination, local minima trapping problem which affects the quality of the solution is totally reduced. In the proposed methodology, Whale optimization technique develops new solutions in solution space and backpropagation algorithm finds the globally optimal solution. Experimental analysis compares the proposed methodology with other algorithms and finally concludes the proposed algorithm outperforms other methodologies.

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Section: Proposed Methodsologymentioning

confidence: 99%

Diabetes Data Classification Using Whale Optimization Algorithm and Backpropagation Neural Network

Rajeshkumar¹,

Kousalya²

2017

Int. Res. J. Pharm.

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“…The performance comparisons have been made by testing on 23 standard mathematical benchmark functions. Found that WOA perform efficient than many state-of-the-art optimization algorithms including PSO [28]. Authors in [29] improved the optimization performance by introducing new convergence factor in both exploration and exploitation phase.…”

Section: Related Workmentioning

confidence: 99%

“…In GSA randomly created candidate solutions for data clustering problem then the interaction have been made to each and other via Newton's gravitation law to search problem space [26], [27]. The WOA (Whale Optimization Algorithm) [28] has been devised based on the foraging strategy of hump back whales. The strategy of searching for prey, encircling strategy and the mass net searching towards getting the prey was considered in building up this algorithm.…”

Section: Related Workmentioning

confidence: 99%

Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Saidala¹,

Devarakonda²

2018

IJISA

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Abstract-Computational Intelligence (CI) is an as of emerging area in addressing complex real world problems. The WOA has taken its root from the collective intelligent foraging behavior of humpback whales (Megaptera Novaeangliae). The standard WOA is suffers from the selection of best agent while whales searching and encircling prey. This research paper deals with the multi-swarm cooperative strategies for finding the best agents which balances the two phase's exploration and exploitation. The performance of invoked Multi-Swarm cooperative strategies into standard WOA i.e, MsWOA is first benchmarked on a set of 23 standard mathematical benchmark function problems which includes 7 UniModal, 6 Multi-modal and 10 fixed dimension multimodal functions. The obtained graphical and statistical results have been portrayed along with the previously established techniques. The obtained results depicts that the proposed cooperative strategies for WOA outperforms in solving optimization problems of standard benchmark functions. This paper also studies the numerical efficiency of proposed techniques on the problem of data clustering where 10 real data clustering problems have been taken from data repository https://archive.ics.uci.edu.data. Statistical results for the obtained cluster centroids, intra-cluster distances and inter-cluster distances confirms that the cooperative strategies for best whale agent selection improves the performance WOA for function optimization problems as well as data clustering problems.

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“…However, the distance control parameter is linearly decreased from 2 to 0 a over the course of iterations [1]. Thus, in this paper, we introduce the different distance control parameter strategies to WOA to design the improved whale optimization algorithms (IWOAs).…”

Section: Introductionmentioning

confidence: 99%

“…Whale optimization algorithm (WOA) is a relatively new meta-heuristic optimization technique proposed by Mirjalili and Lewis [1], which inspires the bubble-net hunting behaviour of humpback whales. WOA easy to implement and has few adjustment parameters, which make it superior than particle swarm optimization (PSO), grey wolf optimizer (GWO) algorithm, and gravitational search algorithm (GSA), etc.…”

Section: Introductionmentioning

confidence: 99%

Whale optimization algorithm with nonlinear control parameter

Zhong

Long

2017

MATEC Web Conf.

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Abstract. Whale optimization algorithm (WOA) is a relatively novel intelligence optimization technique which has been shown to be competitive to other population-based algorithms. However, the control parameter is a major factor to affect the algorithm's convergence precision and speed. At present, few of a them are aiming at control parameter setting in WOA algorithm. This paper proposes corresponding improved WOA algorithm with different nonlinear adjustment strategy of control parameter by adopting a sinusoid, cosine, tangential, logarithmic and quadratic curves. The experimental results for six benchmark test functions show that the proposed nonlinear adjustment strategies are superior to the classical linear strategy.

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The Whale Optimization Algorithm

Cited by 8,628 publications

References 72 publications

Diabetes Data Classification Using Whale Optimization Algorithm and Backpropagation Neural Network

Diabetes Data Classification Using Whale Optimization Algorithm and Backpropagation Neural Network

Multi-Swarm Whale Optimization Algorithm for Data Clustering Problems using Multiple Cooperative Strategies

Whale optimization algorithm with nonlinear control parameter

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