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A novel chaotic chimp sine cosine algorithm Part-I: For solving optimization problem

CHAOS SOLITONS & FRACTALS 2023年第1期173卷

作者： Vigya Raj, Saurav Shiva, Chandan Kumar Vedik, Basetti Mahapatra, Sheila Mukherjee, V. Indian Inst Technol Indian Sch Mines Dept Elect Engn Dhanbad Jharkhand India Inst Chem Technol Mumbai Marathwada Campus Jalna India SR Univ Warangal Dept Elect & Elect Engn Warangal 506371 Telangana India Alliance Univ Bangalore 562106 India

In the realm of solving complex optimization problems, it becomes crucial to fulfill multiple objectives to achieve optimal system performance while adhering to various conditions and limitations. Traditional optimization methods often face challenges such as sluggish convergence speed and confinement to local optima, making it difficult to find satisfactory solution. To tackle this challenge, the development of multi-objective programming is directed towards satisfying physical constraints. In line of the above problems and limitations, this work aims to contribute by proposing a hybrid optimization technique that can effectively address such optimization issues. The proposed algorithm, called the chaotic chimp sine cosine (C-CHOA-SC) algorithm, combines the chimp optimization algorithm (CHOA) and the sine cosine algorithm (SCA) to overcome problems of slow convergence and getting stuck in local optima. It does this by using the SCA after the CHOA and incorporating chaos to explore and exploit the search space effectively. This hybrid approach aims to improve the algorithm's performance (offers enhanced exploration capabilities, efficient exploitation of the search space, and improved convergence speed) and find better solutions to optimization problems. These features make the C-CHOA-SC algorithm wellsuited for solving complex optimization problems with multiple objectives, providing a contemporary and effective solution. To evaluate the performance of the proposed C-CHOA-SC algorithm, three phases of analysis are conducted. In the first phase, the exploration, exploitation, and avoidance of local optima are tested using the CEC 2019 benchmark problems. The second phase involves tracking and validating the performance of C-CHOASC using multiple performance metrics on two-dimensional test functions. Finally, non-parametric alternative test i.e., the Wilcoxon rank sum test and Friedman test are performed. The results demonstrate that the proposed technique exhibits

关键词： CEC 2019 Chaotic Chimp optimization algorithm sine cosine algorithm Friedman test Wilcoxon rank-sum test

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A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期164卷 107212-107212页

作者： Zhong, Mingyang Wen, Jiahui Ma, Jingwei Cui, Hao Zhang, Qiuling Parizi, Morteza Karimzadeh Southwest Univ Coll Artificial Intelligence Chongqing 400715 Peoples R China Defense Innovation Inst Beijing Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250399 Shandong Peoples R China Tech & Vocat Univ TVU Fac Shahid Chamran Dept Comp Engn Kerman Branch Kerman Iran

The sine cosine algorithm (SCA) is an outstanding optimizer that is appreciably used to dissolve complicated real-world problems. Nevertheless, this algorithm lacks sufficient population diversification and a sufficient balance between exploration and exploitation. So, effective techniques are required to tackle the SCA's fundamental shortcomings. Accordingly, the present paper suggests an improved version of SCA called Hierarchical Multi-Leadership SCA (HMLSCA) which uses an effective hierarchical multi-leadership search mechanism to lead the search process on multiple paths. The efficiency of the HMLSCA has been appraised and compared with a set of famous metaheuristic algorithms to dissolve the classical eighteen benchmark functions and thirty CEC 2017 test suites. The results demonstrate that the HMLSCA outperforms all compared algorithms and that the proposed algorithm provided a promising efficiency. Moreover, the HMLSCA was applied to handle the medicine data classification by optimizing the support vector machine's (SVM) parameters and feature weighting in eight datasets. The experiential outcomes verify the productivity of the HMLSCA with the highest classification accuracy and a gain scoring 1.00 Friedman mean rank versus the other evaluated metaheuristic algorithms. Furthermore, the proposed algorithm was used to diagnose COVID-19, in which it attained the topmost accuracy of 98% in diagnosing the infection on the COVID-19 dataset, which proves the performance of the proposed search strategy.

关键词： sine cosine algorithm COVID-19 Data classification Feature weighting Support vector machine

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Hybrid sine cosine algorithm with Integrated Roulette Wheel Selection and Opposition-Based Learning for Engineering Optimization Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 171-171页

作者： Pham, Vu Hong Son Dang, Nghiep Trinh Nguyen Nguyen, Van Nam Vietnam Natl Univ VNU HCM Ho Chi Minh City Univ Technol HCMUT Fac Civil Engn Ho Chi Minh City Vietnam

The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), is introduced. In this advanced model, the roulette wheel selection (RWS) mechanism and opposition-based learning (OBL) techniques are integrated to augment its global optimization capabilities. A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale optimization algorithm (WOA), in addition to the original SCA. This comparative analysis was conducted across a wide array of 23 classical test functions and 29 CEC2017 benchmark functions, thereby facilitating a comprehensive assessment. Further validation of nSCA utility has been achieved through its deployment in five distinct engineering optimization case studies. Its effectiveness and relevance in addressing real-world optimization issues have thus been emphasized. Across all conducted tests and practical applications, nSCA was found to outperform its competitors consistently, furnishing more effective solutions to both theoretical and applied optimization problems.

关键词： Evolutionary algorithm Stochastic optimization sine cosine algorithm Roulette wheel selection Opposition-based learning

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Solving Multi-Task Optimization Problems using the sine cosine algorithm 2022

Solving Multi-Task Optimization Problems using the Sine Cosi...

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11th International Conference on Software and Computer Applications, ICSCA 2022

作者： Zamli, Kamal Z. Abdul Kader, Md. Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia

ISBN: (纸本)9781450385770

Optimization problems relate to the problem of finding minimum or maximum values from a large pools of solutions whereby exhaustive search is practically impossible. Often, optimization problems are solved using metaheuristic algorithms which provide good enough solution within reasonable execution time and limited resources. Recently, much research focus in the literature is devoted on a new kind of optimization problem, called multi-task optimization (MTO). This paper highlights our on-going work dealing with MTO problem. More precisely, our work investigates the adoption of partitioned population based on sine cosine algorithm for dealing with MTO problem. We took the team formation problem from IMDB dataset as our case study based on two objectives, minimizing team costs and team load distribution. © 2022 ACM.

关键词： Multi-task Optimization sine cosine algorithm

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A Modified sine cosine algorithm for Numerical Optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2024年第3期23卷

作者： Xiong, Yan Cheng, Jiatang Guilin Univ Technol Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Adv Mfg & Automat Technol Guilin 541006 Peoples R China Guilin Univ Technol Coll Mech & Control Engn Guilin 550001 Peoples R China

sine cosine algorithm (SCA) is a recently developed meta-heuristic method based on the characteristics of sine and cosine functions. However, SCA algorithm may suffer premature convergence in solving complex optimization problems. To mitigate this limitation, a modified SCA (MSCA) is developed to achieve an effective balance between exploration and exploitation. Specifically, the conversion parameter can be configured adaptively by using the individual fitness information in the evolution process to guide the individual search behavior. After that, an inertia weight is embedded in the position updating equation to further improve the search accuracy and accelerate the convergence speed. The performance of MSCA algorithm is investigated on 13 benchmark functions and the chaotic time series prediction problem. Experimental results demonstrate that MSCA algorithm is more effective than the other optimization methods.

关键词： sine cosine algorithm parameter adjustment inertia weight optimization

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A Levy Flight sine cosine algorithm for Global Optimization Problems

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2021年第1期12卷 49-66页

作者： Li, Yu Zhao, Yiran Liu, Jingsen Hena Univ Inst Management Sci & Engn Kaifeng Peoples R China Henan Univ Sch Business Kaifeng Peoples R China Henan Univ Inst Intelligent Network Syst Kaifeng Peoples R China Henan Univ Software Sch Kaifeng Peoples R China

The sine cosine algorithm (SCA) is a recently proposed global swarm intelligence algorithm based on mathematical functions. This paper proposes a Levy flight sine cosine algorithm (LSCA) to solve optimization problems. In the update equation, the levy flight is introduced to improve optimization ability of SCA. By generating a random walk to update the position, this strategy can effectively search for particles to maintain better population diversity. LSCA has been tested 15 benchmark functions and real-world engineering design optimization problems. The result of simulation experiments with LSCA, SCA, PSO, FPA, and other improvement SCA show that the LSCA has stronger robustness and better convergence accuracy. The engineering problems are also shown that the effectiveness of the levy flight sine cosine algorithm to ensure the efficient results in real-world optimization problem.

关键词： Engineering Design Problem Function Optimization Global Optimization Problem Levy Flight Simulation Experiment sine cosine algorithm Statistical Test Swarm Intelligence algorithm

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Deep Appearance Model and Crow-sine cosine algorithm-Based Deep Belief Network for Age Estimation

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INTERNATIONAL JOURNAL OF AMBIENT COMPUTING AND INTELLIGENCE 2021年第3期12卷 185-207页

作者： Shejul, Anjali A. Kinage, K. S. Reddy, Eswara B. MIT Coll Engn Pune Maharashtra India Pimpari Chinchwad Coll Engn Pune Maharashtra India Jawaharlal Nehru Technol Univ Anantapuramu India

Age estimation has been paid great attention in the field of intelligent surveillance, face recognition, biometrics, etc. In contrast to other facial variations, aging variation presents several unique characteristics, which make age estimation very challenging. The overall process of age estimation is performed using three important steps. In the first step, the pre-processing is performed from the input image based on Viola-Jones algorithm to detect the face region. In the second step, feature extraction is done based on three important features such as local transform directional pattern (LTDP), active appearance model (AAM), and the new feature, deep appearance model (Deep AM). After feature extraction, the classification is carried out based on the extracted features using deep belief network (DBN), where the DBN classifier is trained optimally using the proposed learning algorithm named as crow-sine cosine algorithm (CS).

关键词： Active Appearance Model Crow Search algorithm Deep Appearance Model Deep Belief Network sine cosine algorithm

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An adaptive quadratic interpolation and rounding mechanism sine cosine algorithm with application to constrained engineering optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷

作者： Yang, Xiao Wang, Rui Zhao, Dong Yu, Fanhua Huang, Chunyu Heidari, Ali Asghar Cai, Zhennao Bourouis, Sami Algarni, Abeer D. Chen, Huiling Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun 130022 Jilin Peoples R China Changchun Normal Univ Coll Comp Sci & Technol Changchun 130032 Jilin Peoples R China Changchun Coll Elect Technol Ctr Modern Educ Technol Changchun 130032 Jilin Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Taif Univ Coll Comp & Informat Technol Dept Informat Technol PO POB 11099 Taif 21944 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia

The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension-compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://***/YangXiao9799/paper_ARSCA.

关键词： Meta -heuristic algorithm sine cosine algorithm Practical engineering problem Optimization Evolutionary algorithm Metaheuristic

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Improved sine-cosine algorithm for the Optimization Design of Truss Structures

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KSCE JOURNAL OF CIVIL ENGINEERING 2024年第2期28卷 687-698页

作者： Zhou, Huanlin Yang, Xiaomeng Tao, Ran Chen, Haolong Hefei Univ Technol Coll Civil Engn Hefei 230009 Peoples R China Hefei Univ Technol Sch Civil Engn Hefei 230009 Peoples R China

The sine cosine algorithm (SCA) is a new meta-heuristic algorithm. It has been proven to be very competitive compared with other meta-heuristic algorithms and has attracted significant attention from researchers in different fields. At present, there is no research on the application of SCA in the truss structure. In order to solve the truss optimization problem, the following improvements are made to SCA in this paper. Firstly, a non-linear conversion parameter is set to replace the linear conversion parameter, so that the exploration area is better in line with the characteristics of the actual optimization situation. Secondly, the Levy flight is employed to enhance the algorithm's global search ability. Thirdly, a novel elite guidance strategy taking advantage of memory data is proposed to strengthen the local search ability. Finally, a greedy selection strategy is adopted for the selection of the candidate. Combining the finite element method, we apply ISCA to optimize the size, shape and topology of truss structures. The examples show that ISCA can use less calculation time, get better results, and also better stability. So, the ISCA is effective to solve the size, shape and topology optimization problems of truss structures.

关键词： sine cosine algorithm Truss structures Size optimization Shape optimization Topology optimization

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A new global sine cosine algorithm for solving economic emission dispatch problem

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INFORMATION SCIENCES 2023年第1期648卷

作者： Liu, Jingsen Zhao, Fangyuan Li, Yu Zhou, Huan Henan Univ Henan Int Joint Lab Intelligent Network Theory Kaifeng 475004 Peoples R China Henan Univ Key Technol Kaifeng 475004 Peoples R China Henan Univ Coll Software Kaifeng 475004 Peoples R China Henan Univ Inst Management Sci & Engn Kaifeng 475004 Peoples R China Henan Univ Business Sch Kaifeng 475004 Peoples R China

To explore a better method to solve the economic emission dispatch (EED) problem and enhance the optimization performance of the sine cosine algorithm (SCA), we propose a novel, efficient update of the sine cosine algorithm (ECDSCA) with three improvement strategies. First, in the phase of particle position updates, an enhanced elite leadership strategy is introduced to effec-tively adjust the global search and local exploitation capabilities of SCA. Second, a strategy combining crossover and optimal selections is designed to prevent the algorithm from falling into local extremes. Third, a dimension-by-dimension variation strategy is adopted to enrich the population diversity and improve SCA's optimization accuracy. Theoretical analysis demonstrates that ECDSCA has the same time complexity as SCA. The probability measure method is utilized to prove that ECDSCA is a global convergence algorithm. To evaluate the optimization ability of ECDSCA, it is compared with six representative algorithms on the IEEE CEC2017 test suite. The test results reveal that the optimization ability, convergence rate, and robustness of ECDSCA are improved significantly. Finally, ECDSCA is used to solve the EED problem. The test is carried out on two cases and compared with several algorithms. The comparison results show that ECDSCA significantly outperforms the other comparison algorithms.

关键词： sine cosine algorithm Enhanced elite leadership Dimension -by -dimension variation CEC2017 test suite Economic and emission dispatch

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