Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Elaziz, Mohamed Abd; Ewees, Ahmed A.; Hassanien, Aboul Ella

doi:10.1016/j.eswa.2017.04.023

Cited by 502 publications

(204 citation statements)

References 47 publications

(70 reference statements)

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“…The Whale Optimization Algorithm (WOA) is a developed swarm-based optimization technique for solving optimization problems in many applications [51][52][53][54][55][56][57][58][59]. WOA is a meta-heuristic algorithm, which is first introduced and developed by Mirjalili and Lewis in 2016.…”

Section: Wot Overview and Methodologymentioning

confidence: 99%

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A Reconfigured Whale Optimization Technique (RWOT) for Renewable Electrical Energy Optimal Scheduling Impact on Sustainable Development Applied to Damietta Seaport, Egypt

et al. 2018

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This paper studies the effect on the rate of growth of carbon dioxide emission in seaports' atmosphere of replacing a part of the fossil fuel electrical power generation by clean renewable electrical energies, through two different scheduling strategies. The increased rate of harmful greenhouse gas emissions due to conventional electrical power generation severely affects the whole global atmosphere. Carbon dioxide and other greenhouse gases emissions are responsible for a significant share of global warming. Developing countries participate in this environmental distortion to a great percentage. Two different suggested strategies for renewable electrical energy scheduling are discussed in this paper, to attain a sustainable green port by the utilization of two mutual sequential clean renewable energies, which are biomass and photovoltaic (PV) energy. The first strategy, which is called the eco-availability mode, is a simple method. It is based on operating the renewable electrical energy sources during the available time of operation, taking into consideration the simple and basic technical issues only, without considering the sophisticated technical and economical models. The available operation time is determined by the environmental condition. This strategy is addressed to result on the maximum available Biomass and PV energy generation based on the least environmental and technical conditions (panel efficiency, minimum average daily sunshine hours per month, minimum average solar insolation per month). The second strategy, which is called the Intelligent Scheduling (IS) mode, relies on an intelligent Reconfigured Whale Optimization Technique (RWOT) based-model. In this strategy, some additional technical and economical issues are considered. The studied renewable electrical energy generation system is considered in two scenarios, which are with and without storage units. The objective (cost) function of the scheduling optimization problem, for both scenarios, are developed. Also, the boundary conditions and problem constraints are concluded. The RWOT algorithm is an updated Whale Optimization Algorithm (WOA). It is developed to accelerate the rate of reaching the optimal solution for the IS problem. The two strategies simulation and implementation are illustrated and applied to the seaport of Damietta, which is an Egyptian port, located 10 km to the west of the Nile River (Damietta Branch). The scheduling of PV and biomass energy generation during the different year months is examined for both strategies. The impact of renewable electrical energies generation scheduling on carbon dioxide emission and consequently global warming is discussed. The saving in carbon dioxide emission is calculated and the efficient results of the suggested models are clarified. The carbon dioxide emission is reduced to around its fifth value, during renewable energy operation. This work focuses on decreasing the rate of growth of carbon dioxide emission coming from fossil fuel electrical power generation in Egypt, targ...

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Section: Wot Overview and Methodologymentioning

confidence: 99%

“…An overview of statistical performance evaluation for different evolutionary algorithms is studied [51][52][53][54][55][56][57][58][59]. Comprehensive simulations and statistical analysis are implemented on different algorithm (WOA, PSO and GA) for different standard test functions, showing that:…”

Section: Search For Prey (Exploration Phase)mentioning

confidence: 99%

A Reconfigured Whale Optimization Technique (RWOT) for Renewable Electrical Energy Optimal Scheduling Impact on Sustainable Development Applied to Damietta Seaport, Egypt

et al. 2018

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“…Hence, a new and competent optimization technique is always appreciated by researchers worldwide. WOA is a latest established algorithm by Mirjalili 25 and has been successfully applied for optimal thresh holding for image segmentation 26 and extraction of maximum power from the solar PV. 27 It shows that the WOA offers better accuracy compared with other evolutionary algorithms.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of dual‐mode PI controller using quasi‐oppositional whale optimization algorithm for load frequency control

Simhadri

Mohanty

2019

Int Trans Electr Energ Syst

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Summary In this work, a novel optimization technique named quasi‐oppositional whale optimization algorithm (QOWOA) is employed for the LFC problem. In this study to improve the convergence speed, accuracy, and reliability of WOA, WOA is integrated with quasi‐oppositional–based learning theory and applied first time for the LFC problem. To show the efficacy of the QOWOA technique, at first, it is applied to an interconnected two‐area system with governor dead band nonlinearity then expanded to a realistic three‐area hybrid power system, and another system with two‐area four‐unit hydro‐thermal system is considered. Proportional‐integral and dual‐mode PI controllers are considered as secondary controller for system analysis. The optimization supremacy of the QOWOA technique is validated contrasting the outcomes with original WOA, moth flame optimization algorithm, other recent competitive algorithms, and fuzzy PI controller for an identical system. Robustness of the projected approach is tested with random load perturbation at area‐1 and step load perturbation at diverse location at diverse instant that establishes the efficacy of QOWOA. The critical system analysis demonstrates that the proposed QOWOA technique may be effectively applied for the LFC problem.

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“…11 Hassanien et al used WOA for the binarization of handwritten Arabic manuscripts. Aziz et al applied WOA to multilevel thresholding image segmentation.…”

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confidence: 99%

Distributed Whale Optimization Algorithm based on MapReduce

Khalil

Alshayeji

Ahmad

2018

Concurrency and Computation

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Whale Optimization Algorithm (WOA) is a recent swarm intelligence based meta-heuristic optimization algorithm, which simulates the natural behavior of bubble-net hunting strategy of humpback whales and has been successfully applied to solve complex optimization problems in a wide range of disciplines. However, when applied to large-size problems, its performance degrades due to the need of massive computational work load. Distributed computing is one of the effective ways to improve the scalability of WOA for solving large-scale problems. In this paper, we propose a simple and robust distributed implementation of WOA using Hadoop MapReduce named MR-WOA. MapReduce paradigm is adopted as the distribution model since it is one of the most mature technologies to develop parallel algorithms which automatically handles communication, load balancing, data locality, and fault tolerance. The design of MR-WOA is discussed in details using the MapReduce paradigm. Experiments are conducted for a set of well-known benchmarks for evaluating the quality, speedup, and scalability of MR-WOA. The conducted experiments reveal that our approach achieves a promising speedup. For some benchmarks, speedup scales linearly with increasing the number of computational nodes.

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Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

Cited by 502 publications

References 47 publications

A Reconfigured Whale Optimization Technique (RWOT) for Renewable Electrical Energy Optimal Scheduling Impact on Sustainable Development Applied to Damietta Seaport, Egypt

A Reconfigured Whale Optimization Technique (RWOT) for Renewable Electrical Energy Optimal Scheduling Impact on Sustainable Development Applied to Damietta Seaport, Egypt

Performance analysis of dual‐mode PI controller using quasi‐oppositional whale optimization algorithm for load frequency control

Distributed Whale Optimization Algorithm based on MapReduce

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