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An Improved golden jackal optimization Algorithm Based on Multi-strategy Mixing for Solving Engineering optimization Problems

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Journal of Bionic Engineering 2024年第2期21卷 1092-1115页

作者： Jun Wang Wen-chuan Wang Kwok-wing Chau Lin Qiu Xiao-xue Hu Hong-fei Zang Dong-mei Xu College of Water Resources North China University of Water Resources and Electric PowerZhengzhou450046People’s Republic of China Department of Civil and Environmental Engineering The Hong Kong Polytechnic UniversityHong KongPeople’s Republic of China

Nowadays,optimization techniques are required in various engineering domains to find optimal solutions for complex problems.As a result,there is a growing tendency among scientists to enhance existing nature-inspired algorithms using various evolutionary strategies and to develop new nature-inspired optimization methods that can properly explore the feature space.The recently designed nature-inspired meta-heuristic,named the golden jackal optimization(GJO),was inspired by the collaborative hunting actions of the golden jackal in nature to solve various challenging problems.However,like other approaches,the GJO has the limitations of poor exploitation ability,the ease of getting stuck in a local optimal region,and an improper balancing of exploration and exploitation.To overcome these limitations,this paper proposes an improved GJO algorithm based on multi-strategy mixing(LGJO).First,using a chaotic mapping strategy to initialize the population instead of using random parameters,this algorithm can generate initial solutions with good diversity in the search space.Second,a dynamic inertia weight based on cosine variation is proposed to make the search process more realistic and effectively balance the algorithm's global and local search capabilities.Finally,a position update strategy based on Gaussian mutation was introduced,fully utilizing the guidance role of the optimal individual to improve population diversity,effectively exploring unknown regions,and avoiding the algorithm falling into local optima.To evaluate the proposed algorithm,23 mathematical benchmark functions,CEC-2019 and CEC2021 tests are employed.The results are compared to high-quality,well-known optimization methods.The results of the proposed method are compared from different points of view,including the quality of the results,convergence behavior,and robustness.The superiority and high-quality performance of the proposed method are demonstrated by comparing the results.Furthermore,to demonstrate its applicability,it is employed to solve four c

关键词： golden jackal optimization Chaotic mapping Dynamic inertia weight Dimensional Gaussian variation Muskingum

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An Efficient Multilevel Threshold Image Segmentation Method for COVID-19 Imaging Using Q-Learning Based golden jackal optimization

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Journal of Bionic Engineering 2023年第5期20卷 2276-2316页

作者： Zihao Wang Yuanbin Mo Mingyue Cui School of Artificial Intelligence Guangxi Minzu UniversityNanningChina Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Guangxi Minzu UniversityNanning530006China

From the end of 2019 until now,the Coronavirus Disease 2019(COVID-19)has been rampaging around the world,posing a great threat to people's lives and health,as well as a serious impact on economic development.Considering the severely infectious nature of COVID-19,the diagnosis of COVID-19 has become crucial.Identification through the use of Computed Tomography(CT)images is an efficient and quick means.Therefore,scientific researchers have proposed numerous segmentation methods to improve the diagnosis of CT images.In this paper,we propose a reinforcement learning-based golden jackal optimization algorithm,which is named QLGJO,to segment CT images in furtherance of the diagnosis of COVID-19.Reinforcement learning is combined for the first time with meta-heuristics in segmentation problem.This strategy can effectively overcome the disadvantage that the original algorithm tends to fall into local optimum.In addition,one hybrid model and three different mutation strategies were applied to the update part of the algorithm in order to enrich the diversity of the population.Two experiments were carried out to test the performance of the proposed algorithm.First,compare QLGJO with other advanced meta-heuristics using the IEEE CEC2022 benchmark functions.Secondly,QLGJO was experimentally evaluated on CT images of COVID-19 using the Otsu method and compared with several well-known meta-heuristics.It is shown that QLGJO is very competitive in benchmark function and image segmentation experiments compared with other advanced meta-heuristics.Furthermore,the source code of the QLGJO is publicly available at https://***/Vang-z/QLGJO.

关键词： COVID-19 Bionic algorithm golden jackal optimization Image segmentation Otsu and Kapur method

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