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Application of a Meta-heuristic optimization algorithm Motivated by a Vision Correction Procedure for Civil Engineering Problems

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第7期22卷 2623-2636页

作者： Lee, Eui Hoon Lee, Ho Min Yoo, Do Guen Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 02481 South Korea Univ Suwon Dept Civil Engn Suwon 18323 South Korea Korea Univ Sch Civil Environm & Architectural Engn Seoul 02481 South Korea

In this study, a metaheuristic optimization algorithm inspired by a vision correction procedure is applied to civil engineering problems. The Vision Correction algorithm (VCA) has the ability to solve various problems related to mathematical benchmark functions and civil engineering. Vision correction processes have three main steps: myopic/hyperopic correction, brightness adjustment/compression enforcement, and astigmatic correction. This procedure is essential for increasing the usability of glasses and obtaining high-quality vision in humans. Unlike conventional meta-heuristic algorithms, VCA automatically adjusts the global/ local search probability and global search direction based on accumulated optimization results. In VCA, all decision variables have their own search probabilities and require different processes according to whether a global search or local search is required. The proposed algorithm is applied to representative optimization problems, and the results are compared with those of existing algorithms. In civil engineering problems including design problem of water distribution network, VCA shows respectable results compared with those of existing algorithms. In all benchmark problems and civil engineering problems, VCA shows good results and it showed the applicability to other civil engineering problems.

关键词： vision correction optimization algorithm metaheuristic global search local search

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m-MBOA: a novel butterfly optimization algorithm enhanced with mutualism scheme

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SOFT COMPUTING 2020年第7期24卷 4809-4827页

作者： Sharma, Sushmita Saha, Apu Kumar Natl Inst Technol Dept Math Agartala 799046 Tripura India

The simplicity and effectiveness of a recently proposed metaheuristic, butterfly optimization algorithm (BOA) have gained huge popularity among research community and are being used to solve optimization problems in various disciplines. However, the algorithm is suffering from poor exploitation ability and has a tendency to show premature convergence to local optima. On the other hand, the mutualism phase of another popular metaheuristic symbiosis organisms search (SOS) is known for its exploitation capability. In this paper, a novel hybrid algorithm, namely m-MBOA is proposed to enhance the exploitation ability of BOA with the help of mutualism phase of SOS. To evaluate the effectiveness of m-MBOA, thirty-seven (37) classical benchmark functions are considered and the performance of m-MBOA is compared with the performance of ten (10) state-of-the-art algorithms. Statistical tools have been employed to observe the efficiency of the m-MBOA qualitatively, and obtained results confirm the superiority of the proposed algorithm compared to the state-of-the-art metaheuristic algorithms. Finally, four real-life optimization problem, namely gear train design problem, gas compressor design problem, cantilever beam design problem and three-bar truss design problem are solved with the help of the newly proposed algorithm, and the results are compared with the obtained results of different popular state-of-the-art optimization techniques and found that the proposed algorithm is more efficient than the compared algorithms.

关键词： optimization algorithm Butterfly optimization algorithm (BOA) Mutualism phase Symbiosis organisms search (SOS) m-MBOA Hybrid method Benchmark function

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Recent Studies on Chicken Swarm optimization algorithm: a review (2014-2018)

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第3期53卷 1737-1765页

作者： Deb, Sanchari Gao, Xiao-Zhi Tammi, Kari Kalita, Karuna Mahanta, Pinakeswar Indian Inst Technol Ctr Energy Gauhati India Univ Eastern Finland Sch Comp Kuopio Finland Aalto Univ Dept Mech Engn Espoo Finland Indian Inst Technol Dept Mech Engn Gauhati India

Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm optimization algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.

关键词： Chicken Swarm optimization algorithm Nature inspired intelligence optimization algorithm Applications Review

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Band-gap engineering in CIGS solar cells using Nelder-Mead simplex optimization algorithm

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THIN SOLID FILMS 2006年 511卷 60-65页

作者： Cernivec, G. Krc, J. Smole, F. Topic, M. Univ Ljubljana Fac Elect Engn Ljubljana 1000 Slovenia

Band-gap grading in a CIGS absorber and a conduction band offset at n/p hetero-interface are two important parameters of band-gap engineering aiming at high efficient CIGS solar cells. To obtain optimal CIGS absorber's band-gap grading profile an automatic optimization loop based on Nelder-Mead simplex optimization algorithm has been implemented. The optimization problem is described with an objective function, which-by varying the input parameters-is minimized or maximized. In our study two types of objective functions are used;optical and electrical. As the most optimal profile a parabolic, double graded band-gap profile with a positive or nearly zero conduction band offset at n/p hetero-interface is calculated. Structures with different CIGS absorber thicknesses and bulk and/or hetero-interface recombination lifetimes are examined and their optimized parameters are discussed in the light of experimental achievements. (c) 2005 Elsevier B.V All rights reserved.

关键词： CIGS solar cell graded absorber window/absorber hetero-interface optimization algorithm

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Cooperative positioning of UAV internet of things based on optimization algorithm

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WIRELESS NETWORKS 2024年第5期30卷 4495-4505页

作者： Hu, Hao Chen, YuLin Peng, Bao Li, Zhenjun Wu, Qibao Lin, ZiRan Wang, Xiaolei Bingtuan Xingxin Vocat & Tech Coll Urumqi 830023 Peoples R China South China Normal Univ Guangdong Prov Key Lab Opt Informat Mat & Technol South China Acad Adv Optoelect Guangzhou 510006 Peoples R China South China Normal Univ Inst Elect Paper Displays South China Acad Adv Optoelect Guangzhou 510006 Peoples R China Shenzhen Inst Informat Technol Shenzhen 518172 Peoples R China Shenzhen Inst Technol Shenzhen 518116 Peoples R China Guangdong Prov Engn Ctr China Made High Performan Shenzhen 518172 Peoples R China Tongji Univ Shanghai 200092 Peoples R China Shenzhen Polytech Shenzhen 518055 Peoples R China

Unmanned Aerial Vehicle (UAV) Internet of things have been widely used in military and civilian fields such as rescue, disaster relief, urban planning. Positioning service is the core technology for UAVs to perform various tasks. However, the UAV may be attacked by external conditions, resulting in its inability to obtain self-location information during mission. For the positioning problem of UAV signal interference, this paper proposes a cooperative positioning of UAV based on optimization algorithm. In order to solve the difficulty of UAV positioning, we propose the following solutions. Firstly, we construct different numbers of beacon nodes by using the flight information of UAVs in different cycles. Secondly, the unknown number of the positioning to be solved of the UAV is reduced to improve the accuracy and speed of the subsequent optimization algorithm. Thirdly, A multi-objective optimization model is established of the UAV motion parameters under inequality constraints. And we utilize a penalty function to convert the optimization model into a minimal value solution problem under no constraints. Finally, the positioning results of each UAV are obtained by the optimization algorithm.

关键词： UAV positioning Collaborative positioning Three-dimensional positioning optimization algorithm

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Tasmanian Devil optimization: A New Bio-Inspired optimization algorithm for Solving optimization algorithm

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IEEE ACCESS 2022年 10卷 19599-19620页

作者： Dehghani, Mohammad Hubalovsky, Stepan Trojovsky, Pavel Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic Univ Hradec Kralove Fac Sci Dept Appl Cybernet Hradec Kralove 50003 Czech Republic

In this paper, a new bio-inspired metaheuristic algorithm called Tasmanian Devil optimization (TDO) is designed that mimics Tasmanian devil behavior in nature. The fundamental inspiration used in TDO is simulation of the feeding behavior of the Tasmanian devil, who has two strategies: attacking live prey or feeding on carrions of dead animals. The proposed TDO is described, then its mathematical modeling is presented. TDO performance in optimization is tested on a set of twenty-three standard objective functions. Unimodal benchmark functions have analyzed the TDO exploitation capability, while high-dimensional multimodal and fixed-exploitation multimodal benchmark functions have challenged the TDO exploration capability. The optimization results indicate the high ability of the proposed TDO in exploration and exploitation and create a proper balance between these two indicators to effectively solve optimization problems. Eight well-known metaheuristic algorithms are employed to analyze the quality of the obtained results from TDO. The simulation results show that the proposed TDO, with its strong performance, has a higher capability than the eight competitor algorithms and is much more competitive. For further analysis, TDO is tested in optimizing four engineering design problems. Implementation results show that TDO has an effective performance in solving real-world applications.

关键词： Metaheuristics Search problems Mathematical models Games Statistics Sociology Problem-solving Bio-inspired exploitation exploration feeding optimization optimization algorithm Tasmanian devil

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Parallel chaos optimization algorithm with migration and merging operation

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APPLIED SOFT COMPUTING 2015年 35卷 591-604页

作者： Yuan, Xiaofang Zhang, Ting Xiang, Yongzhong Dai, Xiangshan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

Chaos optimization algorithm (COA) utilizes the chaotic maps to generate the pseudo-random sequences mapped as the decision variables for global optimization applications. A kind of parallel chaos optimization algorithm (PCOA) has been proposed in our former studies to improve COA. The salient feature of PCOA lies in its pseudo-parallel mechanism. However, all individuals in the PCOA search independently without utilizing the fitness and diversity information of the population. In view of the limitation of PCOA, a novel PCOA with migration and merging operation (denoted as MMO-PCOA) is proposed in this paper. Specifically, parallel individuals are randomly selected to be conducted migration and merging operation with the so far parallel solutions. Both migration and merging operation exchange information within population and produce new candidate individuals, which are different from those generated by stochastic chaotic sequences. Consequently, a good balance between exploration and exploitation can be achieved in the MMO-PCOA. The impacts of different one-dimensional maps and parallel numbers on the MMO-PCOA are also discussed. Benchmark functions and parameter identification problems are used to test the performance of the MMO-PCOA. Simulation results, compared with other optimization algorithms, show the superiority of the proposed MMO-PCOA algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Chaos optimization algorithm (COA) Parallel chaos optimization algorithm (PCOA) Parameter identification

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Shape and size determination of plasmonic nano particles using particle swarm optimization algorithm based absorption coefficient

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OPTIK 2017年 130卷 44-49页

作者： Goodarzi, M. Fakharzadeh, A. J. Shiraz Univ Technol Dept Appl Math Shiraz Iran

Plasmonic nano particles can be greatly enhancing the optical absorption coefficient spectrum. Since optical properties of these particles strongly depends on the size and shape of the nano particles, in this paper particle swarm optimization algorithm (PSO) is used to optimize the nano particles shape and size in order to amplification of the absorption coefficient. In PSO a swarm consists of a matrix with decimal numbers, controls the particles shape and size in order to increase the absorption coefficient in the visible part of light spectrum. It is found that significant plasmonic enhancement of above 100000 can be obtained by optimize selection of particle shapes and sizes. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Plasmonic nano particle optimization algorithm PSO algorithm

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A feature fusion optimization algorithm for predicting the remaining useful life of lithium-ion batteries

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ENERGY REPORTS 2023年 9卷 142-153页

作者： Zhang, Xinghong Xu, Yi Gong, Zehao Chongqing Univ Technol Sch Liang Jiang Int Chongqing 401135 Peoples R China

As the percentage of lithium-ion batteries as in a system for storing energy gradually rises, accidents brought by the deterioration of battery performance continue to occur. The solution to guaranteeing the steady operation of this system is learning how to precisely forecast the lithium-ion batteries' remaining useful life (RUL). A prediction framework that combines elements of incremental capacity analysis (ICA) and electrochemical impedance spectroscopy (EIS) is proposed to address issues of RUL. The framework initially examines the charging and discharging features of the battery before establishing a mapping association between fusion features and RUL using convolutional neural network (CNN) and improved long short-term memory network (ILSTM). The parameters of the improved particle swarm optimization algorithm (IPSO) are optimized to build the IPSO-CNN-ILSTM model by modifying updating rules of the inertia weight and learning factor of the particle swarm optimization (PSO) algorithm to improve its optimization ability. Lastly, numerical outcomes of the NASA PCoE datasets confirm this method's applicability and efficacy.(c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Lithium -ion battery Remaining useful life Health feature optimization algorithm

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Clouded Leopard optimization: A New Nature-Inspired optimization algorithm

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IEEE ACCESS 2022年 10卷 102876-102906页

作者： Trojovska, Eva Dehghani, Mohammad Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic

This paper proposes a new nature-inspired metaheuristic algorithm called Clouded Leopard optimization (CLO), which mimics the natural behavior of clouded leopards in the wild. The fundamental inspiration of CLO is derived from two ways of natural behaviors of the clouded leopard, including hunting strategy and daily resting on trees. CLO is mathematically modeled in two phases of exploration and exploitation, based on the simulation of these two natural behaviors. CLO performance is evaluated in solving sixty-eight benchmark functions, including unimodal, multimodal, CEC 2015, and CEC 2017 types. The performance of CLO in solving optimization problems is compared with the performance of ten famous metaheuristic algorithms. The simulation results show that the proposed CLO approach with high ability in exploration, exploitation, and balancing between them has a high capability in optimization applications. Simulation results show that CLO performs better in most test functions than competitor algorithms. In addition, the implementation of CLO on four engineering design issues demonstrates the capability of the proposed approach in real-world applications.

关键词： Metaheuristics Behavioral sciences Search problems Mathematical models Biological system modeling Statistics Social factors Clouded leopard optimization nature-inspired optimization algorithm metaheuristic exploration exploitation

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