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Opposition-based multi-objective whale optimization algorithm with multi-leader guiding

SOFT COMPUTING 2021年第24期25卷 15131-15161页

作者： Li, Yang Li, Wei-gang Zhao, Yun-tao Liu, Ao Minist Educ Engn Res Ctr Met Automat & Measurement Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Sch Evergrande Management Wuhan 430081 Peoples R China

During recent decades, evolutionary algorithms have been widely studied in optimization problems. The multi-objective whale optimization algorithm based on multi-leader guiding is proposed in this paper, which attempts to offer a proper framework to apply whale optimization algorithm and other swarm intelligence algorithms to solving multi-objective optimization problems. The proposed algorithm adopts several improvements to enhance optimization performance. First, search agents are classified into leadership set and ordinary set by grid mechanism, and multiple leadership solutions guide the population to search the sparse spaces to achieve more homogeneous exploration in per iteration. Second, the differential evolution and whale optimization algorithm are employed to generate the offspring for the leadership and ordinary solutions, respectively. In addition, a novel opposition-based learning strategy is developed to improve the distribution of the initial population. The performance of the proposed algorithm is verified in contrast to 10 classic or state-of-the-arts algorithms on 20 bi-objective and tri-objective unconstrained problems, and experimental results demonstrate the competitive advantages in optimization quality and convergence speed. Moreover, it is tested on load distribution of hot rolling, and the result proves its good performance in real-world applications. Thus, all of the aforementioned experiments have indicated that the proposed algorithm is comparatively effective and efficient.

关键词： Multi-objective optimization problems whale optimization algorithm Multi-leader guiding Opposition-based learning strategy

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Risk management in perishable food distribution operations A distribution route selection model and whale optimization algorithm

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INDUSTRIAL MANAGEMENT & DATA SYSTEMS 2020年第2期120卷 291-311页

作者： Wang, Yingchao Yang, Chen Hou, Hanpo Beijing Technol & Business Univ Sch Business Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China

Purpose The purpose of this paper is to predict or even control the food safety risks during the distribution of perishable foods. Considering the food safety risks, the distribution route of perishable foods is reasonably arranged to further improve the efficiency of cold chain distribution and reduce distribution costs. Design/methodology/approach This paper uses the microbial growth model to identify a food safety risk coefficient to describe the characteristics of food safety risks that increase over time. On this basis, with the goal of minimizing distribution costs, the authors establish a vehicle routing problem with a food safety Risk coefficient and a Time Window (VRPRTW) for perishable foods. Then, the Weight-Parameter whale optimization algorithm (WPWOA) which introduces inertia weight and dynamic parameter into the native whale optimization algorithm is designed for solving this model. Moreover, benchmark functions and numerical simulation are used to test the performance of the WPWOA. Findings Based on numerical simulation, the authors obtained the distribution path of perishable foods under the restriction of food safety risks. Moreover, the WPWOA can significantly outperform other algorithms on most of the benchmark functions, and it is faster and more robust than the native WOA and avoids premature convergence. Originality/value This study indicates that the established model and the algorithm are effective to control the risk of perishable food in distribution process. Besides, it extends the existing literature and can provide a theoretical basis and practical guidance for the vehicle routing problem of perishable foods.

关键词： Risk management Supply chain management Vehicle routing problem whale optimization algorithm

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A hybrid whale optimization algorithm based on modified differential evolution for global optimization problems

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APPLIED INTELLIGENCE 2019年第5期49卷 1982-2000页

作者： Luo, Jun Shi, Baoyu Chongqing Univ Coll Optoelect Engn Chongqing 400030 Peoples R China Chongqing Univ Minist Educ Key Lab Optoelect Technol & Syst Chongqing 400030 Peoples R China

whale optimization algorithm(WOA) is a biological-inspired optimization algorithm with the advantage of global optimization ability, less control parameters and easy implementation. It has been proven to be effective for solving global optimization problems. However, WOA can easily get stuck in the local optimum and may lose the population diversity, suffering from premature convergence. In this work, a hybrid whale optimization algorithm called MDE-WOA was proposed. Firstly, in order to enhance local optimum avoidance ability, a modified differential evolution operator with strong exploration capability is embedded in WOA with the aid of a lifespan mechanism. Additionally, an asynchronous model is employed to accelerate WOA's convergence and improve its accuracy. The proposed MDE-WOA is tested with 13 numerical benchmark functions and 3 structural engineering optimization problems. The results show that MDE-WOA has better performance than others in terms of accuracy and robustness on a majority of cases.

关键词： whale optimization algorithm Differential evolution Global optimization Benchmark functions

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A whale optimization algorithm with Distributed Collaboration and Reverse Learning Ability

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Computers, Materials & Continua 2023年第6期75卷 5965-5986页

作者： Zhedong Xu Yongbo Su Fang Yang Ming Zhang School of Architecture and Civil Engineering Anhui Polytechnic UniversityWuhu241000China

Due to the development of digital transformation,intelligent algorithms are getting more and more *** whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization ***,with the increased dimensions,higher requirements are put forward for algorithm *** double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization *** the DCRWOA algorithm,the novel double population search strategy is ***,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search *** experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed *** results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly ***,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.

关键词： whale optimization algorithm double population cooperation distribution reverse learning convergence speed

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Two-dimensional Otsu multi-threshold image segmentation based on hybrid whale optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第10期82卷 15007-15026页

作者： Ning, Guiying Liuzhou Inst Technol Liuzhou 545616 Guangxi Peoples R China

Threshold segmentation is a commonly used method to deal with image segmentation problems. Aiming at the problems of the traditional maximum inter-class variance method (Otsu) in multi-threshold image segmentation, such as large amount of computation, long computation time and low segmentation accuracy. This paper proposes a two-dimensional Otsu multi-threshold image segmentation algorithm based on hybrid whale optimization algorithm. Firstly, the two-dimensional Otsu single-threshold segmentation method is extended to the two-dimensional Otsu multi-threshold segmentation method to improve the segmentation effect. At the same time, in order to reduce the calculation time and improve the solution accuracy, the new hybrid whale optimization algorithm proposed in this paper is used to calculate the threshold. The test is carried out through a set of classical image threshold segmentation sets, and the widely used image segmentation evaluation standards PSNR and SSIM are used for judgment. The results of this paper are also compared with the results of other novel algorithms, including the results of one-dimensional Otsu multi-threshold segmentation method. The results show that the proposed two-dimensional Otsu single-threshold segmentation improves the segmentation efficiency and quality, it is an effective image segmentation method.

关键词： Maximum inter-class variance algorithm Two-dimensional Otsu Image segmentation Nonlinear convergence factor whale optimization algorithm

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Kapur's Entropy for Underwater Multilevel Thresholding Image Segmentation Based on whale optimization algorithm

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IEEE ACCESS 2021年 9卷 41294-41319页

作者： Yan, Zheping Zhang, Jinzhong Yang, Zewen Tang, Jialing Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

Multilevel thresholding is an effective and indispensable technology for image segmentation that has attracted extensive attention in recent years. However, the multilevel thresholding method has some disadvantages, such as a large computational complexity and low segmentation accuracy. Therefore, this paper proposes a whale optimization algorithm (WOA) based on Kapur's entropy method to solve the image segmentation problem. The WOA can effectively balance exploration and exploitation to avoid falling into premature convergence and obtain the global optimal solution. To verify the segmentation performance of the WOA, a series of experiments on underwater images from the experimental pool of Harbin Engineering University are conducted, and the segmentation results are compared with those of the BA, the FPA, MFO, the MSA, PSO and WWO by maximizing the fitness value of Kapur's entropy method. The fitness value, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), execution time and Wilcoxon's rank-sum test are used to evaluate the overall performance of each algorithm. The experimental results reveal that the WOA is superior to the other comparison algorithms and has a higher segmentation accuracy, better segmentation effect and stronger robustness. In addition, the feasibility and efficiency of the WOA are verified.

关键词： Image segmentation Entropy optimization Thresholding (Imaging) whales Object segmentation algorithms Multilevel thresholding whale optimization algorithm image segmentation Kapur’ s entropy

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Hybrid enhanced whale optimization algorithm for contrast and detail enhancement of color images

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 231-267页

作者： Braik, Malik Al Balqa Appl Univ Dept Comp Sci Al Salt Jordan

Image enhancement is an essential step in image analysis and processing as it helps people to recognize and understand images because their perception is greatly influenced by image quality. Incomplete beta function (IBF) is a broadly employed transformation function for image contrast enhancement (ICE). However, IBF has low parameter selection efficiency, a limited range of mutable parameters to stretch areas with high or low gray levels, and image enhancement with stretching at both ends is almost ineffectual. In this paper, a hybrid whale optimization algorithm (WOA) with the Chameleon Swarm algorithm (CSA), referred to as HWOA, is presented to adaptively determine the optimal parameters of IBF for ICE. Then, bilateral gamma correction (BGC) is utilized to produce better contrast and brightness while preserving edge detail. The proposed HWOA algorithm follows a multi-phased process of strategies. Many improvements were made to the mathematical model of WOA followed by its hybridization with CSA for further exploration and exploitation aspects. The proposed HWOA is tested over some standard images along with well-known available Kodak image dataset and assessed using several standard measures. The experimental results showed that the proposed algorithm can satisfactorily surpass many other algorithms that used the same image enhancement approach as well as other traditional image enhancement methods deemed here for comparison. Specifically, the findings on ten color images revealed that the performance of HWOA in terms of average peak signal-to-noise ratio, average structural similarity index, and average values of entropy results are more than 33.0, 96.6%, and 7.3, respectively, and these results are much better than all other comparative methods in the corresponding evaluation criteria.

关键词： Image enhancement whale optimization algorithm Incomplete beta function optimization Bilateral gamma correction Meta-heuristic

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Optimal processing of nearest-neighbor user queries in crowdsourcing based on the whale optimization algorithm

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SOFT COMPUTING 2020年第17期24卷 13037-13050页

作者： Bhaskar, N. Kumar, P. Mohan Anna Univ Dept IT KCG Coll Technol Chennai Tamil Nadu India Anna Univ Dept CSE Jeppiaar Engn Coll Chennai Tamil Nadu India

Generally, human and machine-based query operations can be modified with the use of crowdsourcing. Location-based queries are classified into range and k-nearest neighbor (KNN) queries. Space and point of interest (POI) information can be obtained from both range and KNN queries. In this paper, we expose the trust stage computation of range and KNN query answers with the help of the whale optimization algorithm (WOA). The system chooses either parallel or serial processing, and the experiments are carried out using real-time crowdsourcing. The effectiveness of the proposed concept is evaluated through various consequences such as gang dimension, POI information, space information, and range and KNN query consequences. Each of these effects produces an optimal and reliable result. Finally, the computation time and communication overhead performance of serial and parallel processing are analyzed by examining consequences and production of optimal outcomes.

关键词： Optimal query Crowdsource whale optimization algorithm KNN and range query

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A Novel whale optimization algorithm for Cryptanalysis in Merkle-Hellman Cryptosystem

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MOBILE NETWORKS & APPLICATIONS 2018年第4期23卷 723-733页

作者： Abdel-Basset, Mohamed El-Shahat, Doaa El-henawy, Ibrahim Sangaiah, Arun Kumar Ahmed, Syed Hassan Zagazig Univ Fac Comp & Informat Dept Operat Res Zagazig Egypt Zagazig Univ Fac Comp & Informat Comp Sci Dept El Zera Sq Zagazig Sharqiyah Egypt Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India Univ Cent Florida Dept Elect & Comp Engn Orlando FL 32816 USA

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle-Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The whale optimization algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature.

关键词： whale optimization algorithm Knapsack cipher Subset sum problem Public key cryptosystem Merkle-Hellman Cryptosystem Cryptanalysis

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An improved whale optimization algorithm with armed force program and strategic adjustment

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APPLIED MATHEMATICAL MODELLING 2020年第0期81卷 603-623页

作者： Jiang, Ruiye Yang, Ming Wang, Songyan Chao, Tao Harbin Inst Technol Control & Simulat Syst Harbin 150001 Heilongjiang Peoples R China

In this paper, we propose a new whale army optimization algorithm with a view to solving multifarious optimization problems. The key novelty of our approach is to modify the original whale optimization algorithm to make it effective to solve the complicated, large-scale and constrained optimization problems. Our modifications mainly embody two aspects: the beneficial strategic adjustment to set key parameters and to help establish base principles in the original optimizer and the introduction of armed force program which classifies the search whales into different categories to achieve efficient cooperation. We evaluate the performance of the proposed algorithm, using three simple benchmark test functions over thirty CEC-2014 real-parameter numerical optimization problems and three constraint engineering design problems. The test results indicate that this algorithm can provide a faster local convergence rate, a higher convergence accuracy, and a lower computational complexity in comparison to traditional whale optimization algorithms and other sophisticated state of the art whale optimizers. Performance wise, it also surpasses many advanced methods for large-scaled complex functions. Furthermore, in this paper we propose a variant of whale army optimization algorithm to specifically address and solve optimizing constrained problems with a high degree of precision. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Constrained functions Computation intelligence CEC optimization functions Engineering design problems Swarm intelligence whale optimization algorithm

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