A parallel numerical method for solving optimal control problems based on whale optimization algorithm

Mehne, Hamed Hashemi; Mirjalili, Seyedali

doi:10.1016/j.knosys.2018.03.024

Cited by 55 publications

(16 citation statements)

References 19 publications

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“…Shrinking and surrounding mechanism: it is achieved by reducing the convergence factor a in equations (3) and (4).…”

Section: Surrounding Prey Stagementioning

confidence: 99%

“…e main idea of the algorithm is to solve the target problem by imitating the whale's predatory behavior [2]. Since its introduction, the WOA has been favored by many scholars, and it has been widely used in optimal allocation of water resources [3], optimal control [4], and feature selection [5]. But as a swarm intelligence optimization algorithm, like DE, PSO, ACO, and other algorithms, they all have the shortcomings of slow convergence and easy to fall into local optimum.…”

Section: Introductionmentioning

confidence: 99%

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Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Ning

Cao

2021

Discrete Dynamics in Nature and Society

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In view of the shortcomings of the whale optimization algorithm (WOA), such as slow convergence speed, low accuracy, and easy to fall into local optimum, an improved whale optimization algorithm (IWOA) is proposed. First, the standard WOA is improved from the three aspects of initial population, convergence factor, and mutation operation. At the same time, Gaussian mutation is introduced. Then the nonfixed penalty function method is used to transform the constrained problem into an unconstrained problem. Finally, 13 benchmark problems were used to test the feasibility and effectiveness of the proposed method. Numerical results show that the proposed IWOA has obvious advantages such as stronger global search ability, better stability, faster convergence speed, and higher convergence accuracy; it can be used to effectively solve complex constrained optimization problems.

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“…Shrinking and surrounding mechanism: it is achieved by reducing the convergence factor a in equations (3) and (4).…”

Section: Surrounding Prey Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Ning

Cao

2021

Discrete Dynamics in Nature and Society

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“…Mafarja and Mirjalili [32] proposed that the Whale optimization algorithm has a better selection effect in feature selection of data sets, especially in searching for optimal feature subsets. Mehne and Mirjalili [33] proposed that Whale optimization algorithm be used for parallel processing in the optimal control problem. The experimental results show that the processing effect of using the Whale optimization algorithm is better.…”

Section: Related Researchmentioning

confidence: 99%

Research on New Adaptive Whale Algorithm

Chen

2020

IEEE Access

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Bionic algorithms have always played an important role in industrial, agricultural, and scientific research. The optimization of bionics algorithms has always been the focus of scholars in various countries. A whale algorithm based on optimization based on adaptive convergence and Levy features (IWOA) is proposed to overcome the disadvantages, such as low precision, slow convergence speed and tending to involve the local optimum of the whale algorithm. The improved Bernouilli Shift map is used to initialize the population to maintain diversity of the population. Optimizing the adaptive convergence factor is able to balance the local and global optimization ability. The Levy flight mechanism is introduced to optimize foraging behavior and improve global searching ability. In addition, the trigger rule is applied to screen individuals after each iteration to maintain individual vitality and enhance overall performance of the algorithm. In the simulation, IWOA, Ant Colony Optimization, Particle Swarm Optimization, Whale Optimization Algorithm and the optimized whale algorithms CWOA, LWOA are compared using the 20 classical test functions. The simulation results demonstrate that the IWOA algorithm possesses good global and local searching ability, especially in solving multi-peak and high-dimensional functions.

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“…In this paper, application and product cross entropy as the fitness function of HSOA, by calculating the minimum cross entropy to search the parameter optimal solution. The minimum cross entropy is to represent the differences between the different probability distributions using the cross entropy represented by the following convex function, and there is always a threshold to reduce the amount of information on the front and back of the image, and the minimum cross entropy can be used to evaluate the image segmentation result by calculating the entropy of this threshold [39,40]. Cross entropy describes information differences between probability distributions P = {P 1 , P 2 , · · · , P N } and Q = {Q 1 , Q 2 , · · · , Q N }.…”

Section: Fitness Functionmentioning

confidence: 99%

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

2019

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This paper proposes a three dimensional pulse coupled neural network (3DPCNN) image segmentation method based on a hybrid seagull optimization algorithm (HSOA) to solve the oil pollution image. The image of oil pollution is taken by the unmanned aerial vehicle (UAV) in the oil field area. The UAV is good at shooting the ground area, but its ability to identify the oil pollution area is poor. In order to solve this problem, a 3DPCNN-HSOA algorithm is proposed to segment the oil pollution image, and the oil pollution area is segmented to identify the dirty oil area and improve the inspection of environmental pollution. The 3DPCNN image segmentation method has simple structure and good segmentation effect, but it has many parameters and poor segmentation effect for complex oil images. Therefore, we apply HSOA algorithm to optimize the parameters of 3DPCNN algorithm, so as to improve the segmentation accuracy and solve the segmentation of oil pollution images. The experimental results show that the 3DPCNN-HSOA model can separate the oil pollution area from the complex background.

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A parallel numerical method for solving optimal control problems based on whale optimization algorithm

Cited by 55 publications

References 19 publications

Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Improved Whale Optimization Algorithm for Solving Constrained Optimization Problems

Research on New Adaptive Whale Algorithm

Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation

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