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IBMSMA: An Indicator-based Multi-swarm Slime Mould Algorithm for Multi-objective truss optimization Problems

Journal of Bionic Engineering 2023年第3期20卷 1333-1360页

作者： Shihong Yin Qifang Luo Yongquan Zhou College of Artificial Intelligence Guangxi University for NationalitiesNanning530006China Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis Nanning530006China Xiangsihu College of Guangxi University for Nationalities Nanning530225China

This work proposes an improved multi-objective slime mould algorithm, called IBMSMA, for solving the multi-objective truss optimization problem. In IBMSMA, the chaotic grouping mechanism and dynamic regrouping strategy are employed to improve population diversity;the shift density estimation is used to assess the superiority of search agents and to provide selection pressure for population evolution;and the Pareto external archive is utilized to maintain the convergence and distribution of the non-dominated solution set. To evaluate the performance of IBMSMA, it is applied to eight multi-objective truss optimization problems. The results obtained by IBMSMA are compared with other 14 well-known optimization algorithms on hypervolume, inverted generational distance and spacing-to-extent indicators. The Wilcoxon statistical test and Friedman ranking are used for statistical analysis. The results of this study reveal that IBMSMA can find the Pareto front with better convergence and diversity in less time than state-of-the-art algorithms, demonstrating its capability in tackling large-scale engineering design problems.

关键词： Slime mould algorithm Shift-based density estimation Multi-swarm strategy Multi-objective optimization truss optimization

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An Improved JSO and Its Application in Spreader optimization of Large Span Corridor Bridge

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2357-2382页

作者： Shude Fu Xinye Wu Wenjie Wang Yixin Hu Zhengke Li Feng Jiang Fujian Key Laboratory of Digital Simulations for Coastal Civil Engineering School of Architecture and Civil EngineeringXiamen UniversityXiamen361005China Xiamen Transportation Infrastructure Intelligent Management and Maintenance Engineering Technology Research Center Xiamen361005China Fuzhou Company of China Communications Construction Co. Ltd.Fuzhou350005China

In this paper,given the shortcomings of jellyfish search algorithmwith low search ability in the early stage and easy to fall into local optimal solution,this paper introduces adaptive weight function and elite strategy,improving the global search scope in the early stage and the ability to refine the local development in the later stage.In the numerical study,the benchmark problem of dimensional optimization with a 10-bar truss structure and simultaneous dimensional shape optimization with a 15-bar truss structure is adopted,and the corresponding penalty method is used for constraint treatment.The test results show that the improved jellyfish search algorithm can provide better truss sections as well as weights.Because when the steel main truss of the large-span covered bridge is lifted,the site is limited and the large lifting equipment cannot enter the site,and the original structure does not meet the problem of stress concentration and large deformation of the bolt group,so the spreader is used to lift,and the improved jellyfish search algorithm is introduced into the design optimization of the spreader.The results show that the improved jellyfish algorithm can efficiently and accurately find out the optimal shape and weight of the spreader,and throughMidas Civil simulation,the spreader used canmeet the requirements of weight and safety.

关键词： truss optimization improved JSO size optimization shape optimization

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Coronavirus Mask Protection Algorithm:A New Bio-inspired optimization Algorithm and Its Applications

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Journal of Bionic Engineering 2023年第4期20卷 1747-1765页

作者： Yongliang Yuan Qianlong Shen Shuo Wang Jianji Ren Donghao Yang Qingkang Yang Junkai Fan Xiaokai Mu School of Mechanical and Power Engineering Henan Polytechnic UniversityJiaozuo454003China School of Mechanical Engineering Dalian University of TechnologyDalian116024China School of Software Henan Polytechnic UniversityJiaozuo454003China

Nowadays,meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems.In this paper,a COVID-19 prevention-inspired bionic optimization algorithm,named Coronavirus Mask Protection Algorithm(CMPA),is proposed based on the virus transmission of COVID-19.The main inspiration for the CMPA originated from human self-protection behavior against COVID-19.In CMPA,the process of infection and immunity consists of three phases,including the infection stage,diffusion stage,and immune stage.Notably,wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves,which are similar to the exploration and exploitation in optimization algorithms.This study simulates the self-protection behavior mathematically and offers an optimization algorithm.The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions,CEC2020 suite problems,and three truss design problems.The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms.Further,the CMPA is performed to identify the parameters of the main girder of a gantry crane.Results show that the mass and deflection of the main girder can be improved by 16.44%and 7.49%,respectively.

关键词： Coronavirus Mask Protection Algorithm Bionic algorithm Metaheuristic algorithm optimization algorithm truss optimization Parameter identification

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