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Multi-area economic environmental dispatch using multi-objective squirrel search algorithm

EVOLVING SYSTEMS 2022年第2期13卷 183-199页

作者： Sakthivel, V. P. Sathya, P. D. Govt Coll Engn Dept Elect & Elect Engn Dharmapuri 636704 Tamil Nadu India Annamalai Univ Dept Elect & Commun Engn Fac Engn & Technol Chidambaram 608002 Tamil Nadu India

The main objective of multi-area economic/environmental dispatch (MAEED) problems is the effective management of electrical energy generation by minimizing the total fuel cost and emissions of a multi-area power generation units, while satisfying various system and operating constraints. This paper bestows a new swarm intelligence approach, multi-objective squirrel search algorithm (MOSSA) to solve the MAEED problem. In the suggested approach, an exterior elitist vault system, crowding distance and fuzzy clustering principles are acquainted to procure a uniformly spaced Pareto optimal front curve. In addition, the MAEED problem is unraveled by squirrel search algorithm based weighted sum approach (SSA-WSA) with price penalty factors, artificial bee colony (ABC) and exchange market algorithm (EMA). The adequacy of the suggested strategy is approved on 40 generating units with single and multi-area power systems. The numeric results exhibit that the suggested MOSSA strategy accomplished much preferred trade-off solutions over the SSA-WSA, ABC, EMA and different strategies surfaced in the literature. The result transmuted evinces the desirability of MOSSA to solve the MAEED problem.

关键词： Economic and environmental dispatch Fuzzy clustering mechanism Multi-area power dispatch Pareto optimal front squirrel search algorithm

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Fault detection based on squirrel search algorithm and support vector data description for industrial processes

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SOFT COMPUTING 2022年第24期26卷 13639-13650页

作者： Alejandro Navarro-Acosta, Jesus Garcia-Calvillo, Irma D. Resendiz-Flores, Edgar O. Univ Autonoma Coahuila Ctr Invest Matemat Aplicadas Unidad Saltillo Saltillo 25280 Coahuila Mexico Tecnol Nacl Mexico IT Saltillo Div Estudios Posgrad & Invest Blvd V Carranza 2400 Col Tecnol Saltillo 25280 Coahuila Mexico

This paper proposes a novel fault detection system by the combination of Support Vector Data Description and squirrel search algorithm. This approach is capable to deal with processes or machines where the number of fault observations is small or not even available for training phase. In this work the use of classic Support Vector Data Description as well as its fast version with two kernel functions is proposed. The experimental results showed that the proposed system exhibits suitable capabilities for fault detection in complex industrial processes such as the one presented in this research. Moreover, a nonparametric statistical analysis is also included in order to compare the considered strategies and enhance the efficiency of the presented approach. Finally, a comparison with genetic algorithm approach and the one-class classifier based on support vectors is carried out which shows that the proposed algorithm outperforms traditional techniques.

关键词： Support Vector Data Description squirrel search algorithm Fault detection One-class classification

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Adaptive mutation quantum-inspired squirrel search algorithm for global optimization problems

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ALEXANDRIA ENGINEERING JOURNAL 2022年第9期61卷 7441-7476页

作者： Zhang, Yanan Wei, Chunwu Zhao, Juanjuan Qiang, Yan Wu, Wei Hao, Zifan Taiyuan Univ Technol Coll Informat & Comp Daxue St 209 Jinzhong 030600 Shanxi Peoples R China Shanxi Prov Peoples Hosp Shuangtasi 29 Taiyuan 030012 Shanxi Peoples R China

This paper presents a novel Adaptive Mutation Quantum-inspired squirrel search algorithm (AM-QSSA). Firstly, based on the population mutation, a location-update of quantum state correlative and attractor method is proposed. By introducing a random process to modify the sliding factor of the local attractor, the inevitable lack of diversity in the population renewal method is solved. The premature convergence problem of adaptive mutation rate improvement algorithm based on squirrel position update mode is introduced. Meanwhile, the paper decomposes the location update process of the SSA, and improves it with quantum-behavior. Furthermore, it proposes a novel quantum-inspired squirrels search algorithm. This method finds the complementary effect between quantum behavior and squirrel search algorithm, and solves the problem of premature convergence probability of SSA. In addition, it improves population diversity, and achieves the balance between global and local search. The efficiency of the proposed AM-QSSA is evaluated using exploitation analysis, exploration analysis, success rate analysis, convergence rate analysis on classical benchmark functions as well as Congress on Evolutionary Computation (CEC) 2017 test functions. For further study, AM-QSSA optimizes an image registration problem for an extensive study to check its applicability. The results reveal that AM-QSSA is more efficient and stable than SSA. And it is comparable to the most advanced optimization algorithms.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： Metaheuristic method squirrel search algorithm (SSA) Quantum theory Unconstrained optimization Self-adaptive mutation

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Physical Layer Security using squirrel search algorithm 56

Physical Layer Security using Squirrel Search Algorithm

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56th IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Murthy, T. S. N. Sekhar, P. Chandra Guntur, Srinivasa Sastry JNTU GV Dept ECE CoE A Vizianagaram Andhra Pradesh India CBIT A Dept ECE Hyderabad Telengana India Rivian Automot LLC Funct Safety 11023 Wood Corner CT Charlotte NC 28277 USA

ISBN: (纸本)9781665451093

Data security is crucial since information privacy and confidentiality are major issues in all communication systems. The goal of the study presented in this article is to enhance Physical Layer Security (PLS) by diverting eavesdroppers (Eav) from the source of information using artificial noise (AN). Artificial Noise (AN) is added using the "squirrel search algorithm (SSA)", which mingles with the original signal and causes a full departure from Eav. The system model under consideration is created utilizing a few well-known 5G technologies, including non-orthogonal multiple access (NOMA) and massive multi-input-multi-output (mMIMO), which improves Physical Layer Security (PLS). The impact of small-scale Rayleigh fading channels and Large-Scale path loss are considered here. The enhancement of secrecy performance in terms of strictly positive secrecy rate and secrecy outage probability vs SNR is verified by simulations.

关键词： Physical Layer Security squirrel search algorithm NOMA mMIMO Eavesdropper AN Precoding

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A Robust Video Watermarking Scheme with squirrel search algorithm

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Computers, Materials & Continua 2022年第5期71卷 3069-3089页

作者： Aman Bhaskar Chirag Sharma Khalid Mohiuddin Aman Singh Osman A.Nasr Mamdooh Alwetaishi Computer Science and Engineering Lovely Professional UniversityPunjab144411India Department of Management Information Systems King Khalid UniversityGuraigerAbha62529Saudi Arabia Department of Civil Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia

Advancement in multimedia technology has resulted in protection against distortion,modification,and *** implementing such protection,we have an existing technique called watermarking but obtaining desired distortion level with sufficient robustness is a challenging task for watermarking in multimedia *** the paper,we proposed a smart technique for video watermarking associating meta-heuristic algorithms along with an embedding method to gain an optimized *** main aim of the optimization algorithm is to obtain solutions with maximum robustness,and which should not exceed the set threshold of *** represent the accuracy of the proposed scheme,we employ a popular video watermarking technique(DCT domain)having frame selection and embedding method for watermarking.A squirrel search algorithm is chosen as a meta-heuristic algorithm that utilizes the stated fitness *** results indicate that quality constraint is fulfilled,and the proposed technique gives improved robustness against different attacks with several quality *** proposed technique could be practically implemented in several multimedia applications such as the films industry,medical imagery,OOT platforms,etc.

关键词： Meta-heuristic algorithm constrain optimization problem fitness fiction frame selection squirrel search algorithm

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Simulated squirrel search algorithm: A hybrid metaheuristic method and its application to steel space truss optimization

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STEEL AND COMPOSITE STRUCTURES 2022年第4期45卷 579-590页

作者： Pauletto, Mateus P. Kripka, Moacir Univ Passo Fundo Dept Civil Engn Campus 1 BR-99001970 Passo Fundo RS Brazil

One of the biggest problems in structural steel calculation is the design of structures using the lowest possible material weight, making this a slow and costly process. To achieve this objective, several optimization methods have been developed and tested. Nevertheless, a method that performs very efficiently when applied to different problems is not yet available. Based on this assumption, this work proposes a hybrid metaheuristic algorithm for geometric and dimensional optimization of space trusses, called Simulated squirrel search algorithm, which consists of an association of the wellestablished neighborhood shifting algorithm (Simulated Annealing) with a recently developed promising population algorithm (squirrel search algorithm, or SSA). In this study, two models are tried, being respectively, a classical model from the literature (25-bar space truss) and a roof system composed of space trusses. The structures are subjected to resistance and displacement constraints. A penalty function using Fuzzy Logic (FL) is investigated. Comparative analyses are performed between the squirrel search algorithm (SSSA) and other optimization methods present in the literature. The results obtained indicate that the proposed method can be competitive with other heuristics.

关键词： fuzzy logic simulated annealing space trusses squirrel search algorithm structural optimization

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A Novel Positioning Accuracy Improvement Method for Polishing Robot Based on Levenberg-Marquardt and Opposition-based Learning squirrel search algorithm

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2024年第1期110卷 8-8页

作者： Deng, Yonghong Hou, Xi Li, Bincheng Wang, Jia Zhang, Yun Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Univ Elect Sci & Technol China Sch Optoelect Sci & Engn Chengdu 610054 Peoples R China Chinese Acad Sci Natl Key Lab Opt Field Manipulat Sci & Technol Chengdu 610209 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Achieving high-precision manufacturing of optical components requires improving the absolute positioning accuracy of the robot to the highest possible level. Identifying the robot's kinematic parameters and compensating for kinematic errors are effective methods for improving the robot's positioning accuracy. This paper proposes a hybrid algorithm that combines the Levenberg-Marquardt algorithm and an opposition-based learning squirrel search algorithm to identify the kinematic parameters of a polishing robot. Firstly, the Levenberg-Marquardt algorithm is utilized to solve the suboptimal values of kinematic parameter deviations. Secondly, an opposition-based learning strategy is integrated into the standard squirrel search algorithm to increase the diversity of the population and prevent the population from getting stuck in local optima. The suboptimal values obtained by the Levenberg-Marquardt algorithm are subsequently used as the central values to generate the initial population for the opposition-based learning squirrel search algorithm, which helps identify more accurate kinematic parameter deviations. Ultimately, the kinematic parameters of the robot are effectively calibration. The calibration experimental results showed that the proposed method achieved a high level of calibration accuracy, resulting in a 62.61% improvement in absolute positioning error compared to before calibration. Offline machining experiments have validated the effectiveness of LM-OBLSSA in reducing deviations in the dwell points of optical components during the machining process.

关键词： Polishing Robot Kinematic parameters calibration Positioning accuracy Levenberg-Marquardt squirrel search algorithm

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Surgical cases assignment problem using a multi-objective squirrel search algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 235卷

作者： Zhu, Lei Zhou, Yusheng Jiang, Ronghang Su, Qiang Tongji Univ Sch Econ & Management Shanghai 201800 Peoples R China Nanjing Univ Sch Informat Management Nanjing 210023 Peoples R China

Surgical cases assignment problem (SCAP) is among the most investigated interests in operating room planning. The studies related to the SCAP are mainly focused on the single objective optimization. To concern multiple criteria in the management, this paper reformulates a multi-objective SCAP with minimizing total operating cost and maximizing scheduled surgeries number, simultaneously. Although dozens of multi-objective methods have been introduced in recent years, these cannot be applied into the multi-objective SCAP directly considering the characteristics of the problem. To tackle the multi-objective SCAP, a problem-specified multi-objective squirrel search algorithm (MOSSA) is presented. First, a targeted single-list scheme is devised to encode individuals and an efficient decoding scheme integrating a repair strategy is proposed to construct feasible scheduling plan. Second, two simple heuristics are incorporated into the algorithm to imitate the behaviors of squirrels and improve the search ability. Third, an addition archive is employed to store the non-dominated solutions. Moreover, a DOE method is performed to investigate the influence of parameter settings. The performance of the MOSSA is assessed by a representative benchmark dataset with three different evaluating indicators. Extensive experiments and analysis results demonstrate the effectiveness of the proposed algorithm and the superiority over compared approaches in addressing the multi-objective SCAP.

关键词： Surgical cases assignment Multi-objective optimization squirrel search algorithm Healthcare management

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A dimensional learning squirrel search algorithm based on roulette strategy

A dimensional learning squirrel search algorithm based on ro...

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Asia Conference on algorithms, Computing and Machine Learning (CACML))

作者： Wen, Daiquan Huo, Lin Guangxi Univ Sch Comp Elect & Informat Nanning Guangxi Peoples R China Guangxi Univ Int Coll Nanning Guangxi Peoples R China

ISBN: (数字)9781665482905

ISBN: (纸本)9781665482905

squirrel search algorithm (SSA) is an effective intelligent optimization algorithm, but it also has the problems of slow convergence speed and easy to fall into local optima. To solve these problems, a dimensional learning squirrel search algorithm (DLSSA) based on roulette strategy is proposed. Roulette strategy was introduced to update the position of squirrels on normal trees to increase the diversity of the algorithm and avoid falling into local optimal solution. Using the method of dimensional learning to guide the location update of squirrels on oak trees, the excellent information of hickory tree can be accurately obtained to increase the efficiency of information flow transmission. Cauchy mutation was performed on squirrel on hickory tree to accelerate the convergence of the algorithm. Optimization experiments are carried out on 10 benchmark functions. Compared with other 7 algorithms, DLSSA has obvious advantages in the success rate of optimization and convergence speed.

关键词： squirrel search algorithm roulette strategy dimensional learning Cauchy mutation function optimization

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An Improved squirrel search algorithm for Optimization 41

An Improved Squirrel Search Algorithm for Optimization

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第41届中国控制会议

作者： Tingting Yuan Jie Ding Taotao Tu School of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

squirrel search algorithm(SSA) is a novel meta-heuristic optimization algorithm which is more appropriate for solving unimodal,multimodal,and compound ***,SSA has shortcomings such as limited accuracy and slow *** order to improve its global search ability and convergence speed,this paper proposes an improved squirrel search algorithm(ISSA).First,the social part of the particle swarm algorithm is introduced into the position update formula on the ordinary tree to improve the search range and convergence speed of ***,a new location update condition is added,which can balance between exploration and exploitation of ***,in order to prevent falling into a local optimal solution,an adaptive strategy of the minimum seasonal constant is *** proposed ISSA has been tested by 12 benchmark functions and compared with the results of six other intelligent optimization *** effectiveness and superiority of ISSA are shown in average convergence value and convergence speed.

关键词： squirrel search algorithm Social part Adaptive strategy

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