scholarly journals Penyelesaian Container Stowage Problem untuk Kontainer Ukuran 20 Feet menggunakan Whale Optimization Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 100
Author(s):  
Quinn Nathania PJY ◽  
Asri Bekti Pratiwi ◽  
Herry Suprajitno
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Small Data ◽  
C Programming Language ◽  
Whale Optimization ◽  
C Programming ◽  
Complex Optimization ◽  
Unloading Time

This paper has purpose to solve Container Stowage Problem (CSP) for 20 feet container using Whale Optimization Algorithm (WOA). CSP is a problem discussing about how to stowage a container on the ship where the purpose to minimize the unloading time. Moreover, 20 feet container is one of container types. WOA is a recently developed swarm-based metaheuristic algorithm that is based on the bubble net hunting maneuver technique of humpback whales for solving complex optimization problems. WOA had three procedures, first encircling prey, second bubble-net attacking method or exploitation phase, and third search for prey or exploration phase. WOA application program or resolving solve CSP for 20 feet container was made by using Borland C++ programming language which was implemented in three cases types of CSP data, first, the small data taking about nine containers with the number of  bays, rows and tiers, respectively, are 4, 4, 4. The second and third data was medium data and big data with 62 containers and 95 containers each data, and had the number of bays, rows and tiers, respectively, are 14, 4, 5. After executing the program can be concluded the unloading time will be better if the number of whales is larger, while the number of iterations and the number of parameter control for shape of a logaritma spiral  don’t affect the solution.

scholarly journals Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems

2020 ◽  
Vol 5 (3) ◽  
pp. 147-155
Author(s):  
I-Ming Chao ◽  
Shou-Cheng Hsiung ◽  
Jenn-Long Liu
Keyword(s):  
Global Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Position Vector ◽  
Hunting Behavior ◽  
Inertia Weight ◽  
Whale Optimization ◽  
Solution Accuracy

Whale Optimization Algorithm (WOA) is a new kind of swarm-based optimization algorithm that mimics the foraging behavior of humpback whales. WOA models the particular hunting behavior with three stages: encircling prey, bubble-net attacking, and search for prey. In this work, we proposed a new linear decreasing inertia weight with a random exploration ability (LDIWR) strategy. It also compared with the other three inertia weight WOA (IWWOA) methods: constant inertia weight (CIW), linear decreasing inertia weight (LDIW), and linear increasing inertia weight (LIIW) by adding fixed or linear inertia weights to the position vector of the reference whale. The four IWWOAs are tested with 23 mathematical and theoretical optimization benchmark functions. Experimental results show that most of IWWOAs outperform the original WOA in terms of solution accuracy and convergence rate when solving global optimization problems. Accordingly, the LDIWR strategy produces a better balance between exploration and exploitation capabilities for multimodal functions.

scholarly journals An Improved Whale Optimization Algorithm for the Traveling Salesman Problem

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 48
Author(s):  
Jin Zhang ◽  
Li Hong ◽  
Qing Liu
Keyword(s):  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Relevant Literature ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Whale Optimization Algorithm ◽  
Neighborhood Search ◽  
Whale Optimization ◽  
The Traveling Salesman Problem

The whale optimization algorithm is a new type of swarm intelligence bionic optimization algorithm, which has achieved good optimization results in solving continuous optimization problems. However, it has less application in discrete optimization problems. A variable neighborhood discrete whale optimization algorithm for the traveling salesman problem (TSP) is studied in this paper. The discrete code is designed first, and then the adaptive weight, Gaussian disturbance, and variable neighborhood search strategy are introduced, so that the population diversity and the global search ability of the algorithm are improved. The proposed algorithm is tested by 12 classic problems of the Traveling Salesman Problem Library (TSPLIB). Experiment results show that the proposed algorithm has better optimization performance and higher efficiency compared with other popular algorithms and relevant literature.

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.

Whale optimization algorithm fused with SVM to detect stress in EEG signals

2021 ◽  
Vol 15 (1) ◽  
pp. 87-97
Author(s):  
Richa Gupta ◽  
M. Afshar Alam ◽  
Parul Agarwal
Keyword(s):  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Support Vector ◽  
Eeg Signals ◽  
Hunting Behavior ◽  
Positron Emission ◽  
Whale Optimization ◽  
Signal Activity ◽  
Work Done

Identifying stress and its level has always been a challenging area for researchers. A lot of work is going on around the world on the same. An attempt has been made by the authors in this paper as they present a methodology for detecting stress in EEG signals. Electroencephalogram (EEG) is commonly used to acquire brain signal activity. Though there exist other techniques to extract the same like Functional magnetic resonance imaging (fMRI), positron emission tomography (PET) we have used EEG as it is economical. We have used an open-source dataset for EEG data. Various images are used as the target stressor for collecting EEG signals. After feature selection and extraction, a support vector machine (SVM) with a whale optimization algorithm (WOA) in its kernel function for classification is used. WOA is a bio-inspired meta-heuristic algorithm, based on the hunting behavior of humpback whales. Using this method, we had obtained 91% accuracy for detecting the stress. The paper also compared the previous work done in detecting stress with the work proposed in this paper.

Improved whale optimization algorithm based on variable spiral position update strategy and adaptive inertia weight

2021 ◽  
pp. 1-17
Author(s):  
Maodong Li ◽  
Guanghui Xu ◽  
Yuanwang Fu ◽  
Tingwei Zhang ◽  
Li Du
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Convergence Speed ◽  
Whale Optimization Algorithm ◽  
Original Algorithm ◽  
Inertia Weight ◽  
Whale Optimization ◽  
Multimodal Function ◽  
Update Strategy ◽  
Improved Algorithm

 In this paper, a whale optimization algorithm based on adaptive inertia weight and variable spiral position updating strategy is proposed. The improved algorithm is used to solve the problem that the whale optimization algorithm is more dependent on the randomness of the parameters, so that the algorithm’s convergence accuracy and convergence speed are insufficient. The adaptive inertia weight, which varies with the fitness of individual whales, is used to balance the algorithm’s global search ability and local exploitation ability. The variable spiral position update strategy based on the collaborative convergence mechanism is used to dynamically adjust the search range and search accuracy of the algorithm. The effective combination of the two can make the improved whale optimization algorithm converge to the optimal solution faster. It had been used 18 international standard test functions, including unimodal function, multimodal function, and fixed-dimensional function to test the improved whale optimization algorithm in this paper. The test results show that the improved algorithm has faster convergence speed and higher algorithm accuracy than the original algorithm and several classic algorithms. The algorithm can quickly converge to near the optimal value in the early stage, and then effectively jump out of the local optimal through adaptive adjustment, and has a certain ability to solve large-scale optimization problems.

scholarly journals Application of Whale Optimization Algorithm to Inverse Scattering of an Imperfect Conductor with Corners

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Kun-Chou Lee ◽  
Pai-Ting Lu
Keyword(s):  
Inverse Scattering ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Spline Interpolation ◽  
Optimal Solution ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Hunting Behavior ◽  
Whale Optimization ◽  
The Moment

In this paper, the whale optimization algorithm (WOA) is applied to the inverse scattering of an imperfect conductor with corners. The WOA is a new metaheuristic optimization algorithm. It mimics the hunting behavior of humpback whales. The inspiration results from the fact that a whale recognizes the location of a prey (i.e., optimal solution) by swimming around the prey within a shrinking circle and along a spiral-shaped path simultaneously. Initially, the inverse scattering is first transformed into a nonlinear optimization problem. The transformation is based on the moment method solution for scattering integral equations. To treat a target with corners and implement the WOA inverse scattering, the cubic spline interpolation is utilized for modelling the target shape function. Numerical simulation shows that the inverse scattering by WOA not only is accurate but also converges fast.

scholarly journals Nature-Inspired Whale Optimization Algorithm for Optimal Coordination of Directional Overcurrent Relays in Power Systems

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2297 ◽  
Author(s):  
Wadood ◽  
Khurshaid ◽  
Farkoush ◽  
Yu ◽  
Kim ◽  
...  
Keyword(s):  
Power Systems ◽  
Optimization Algorithm ◽  
Operating Time ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Test Systems ◽  
Whale Optimization ◽  
Overcurrent Relays ◽  
Optimal Coordination ◽  
Hunting Strategy

In power systems protection, the optimal coordination of directional overcurrent relays (DOCRs) is of paramount importance. The coordination of DOCRs in a multi-loop power system is formulated as an optimization problem. The main objective of this paper is to develop the whale optimization algorithm (WOA) for the optimal coordination of DOCRs and minimize the sum of the operating times of all primary relays. The WOA is inspired by the bubble-net hunting strategy of humpback whales which leads toward global minima. The proposed algorithm has been applied to six IEEE test systems including the IEEE three-bus, eight-bus, nine-bus, 14-bus, 15-bus, and 30-bus test systems. Furthermore, the results obtained using the proposed WOA are compared with those obtained by other up-to-date algorithms. The obtained results show the effectiveness of the proposed WOA to minimize the relay operating time for the optimal coordination of DOCRs.

scholarly journals Near Infrared and Visible Image Registration using Whale Optimization Algorithm

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Image Registration ◽  
Optimization Algorithm ◽  
Near Infrared ◽  
Optimization Problems ◽  
Search Space ◽  
Whale Optimization Algorithm ◽  
Visible Image ◽  
Local Optima ◽  
Multimodal Image Registration ◽  
Whale Optimization

This paper reports the use of a nature-inspired metaheuristic algorithm known as ‘Whale Optimization Algorithm’ (WOA) for multimodal image registration. WOA is based on the hunting behaviour of Humpback whales and provides better exploration and exploitation of the search space with small possibility of trapping in local optima. Though WOA is used in various optimization problems, no detailed study is available for its use in image registration. For this study different sets of NIR and visible images are considered. The registration results are compared with the other state of the art image registration methods. The results show that WOA is a very competitive algorithm for NIR-visible image registration. With the advantages of better exploration of search space and local optima avoidance, the algorithm can be a suitable choice for multimodal image registration.

scholarly journals Optimal Reconfiguration of Electrical Distribution System by Whale Optimization Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 2392-2398
Keyword(s):  
Optimization Algorithm ◽  
Distribution System ◽  
Electrical Power ◽  
Whale Optimization Algorithm ◽  
Humpback Whales ◽  
Voltage Profile ◽  
Electrical Power System ◽  
Electrical Distribution ◽  
Whale Optimization ◽  
Optimal Reconfiguration

The prime motto of the electrical power system is to provide the good and high quality power to the consumers. As the life in the society is expanding hugely, hence the need of the electrical power is additionally expanding suggestively. In this manner expanding the power generation as well as beating the significant issues in the electrical distribution system has turned into a test. The strange conditions can't be normal however when happened; the recuperation ought to be made as quickly as time permits. In this work, a modern artificial intelligence based algorithm is implemented for the reconfiguration of an electrical radial distribution network. This algorithm helps to bring down the active power loss and intensify the voltage profile of the network. This paper has proposed a nature-based guided metaheuristic Whale Optimization Algorithm (WOA). WOA is motivated by the smart foraging approach of the humpback whales. To ratify the efficiency of the proposed approach, WOA is successfully simulated on IEEE standard 69 bus and 119 bus system.

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