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Improvement And Application Of Sine-cosine Optimization Algorithm

Posted on:2022-06-21Degree:MasterType:Thesis
Country:ChinaCandidate:R ZhangFull Text:PDF
GTID:2518306482493644Subject:Master of Engineering
Abstract/Summary:PDF Full Text Request
One of the most important methods for solving optimal optimization problems is the swarm intelligence algorithm,which is widely used in a variety of real-life problems.The Sine Cosin Algorithm(SCA)is a novel group intelligence algorithm,which is based on the properties of sine and cosine trigonometric functions and is mainly used to solve global optimisation problems.This paper presents a novel improvement to the Sine Cosine Algorithm that improves solution utilisation and reduces the overflow of diversity present in the classical SCA search equations,the proposed algorithm is referred to as ISCA.the key features of the algorithm are the combination of crossover operators with the best state of a single solution and the integration of self-learning,global search mechanisms as well as greedy selection mechanisms.To evaluate the performance of the ISCA algorithm,the ISCA algorithm was tested on a set of classical functions and several engineering problems as well as a multi-level threshold image segmentation problem.The test results show that(1)the improved ISCA algorithm of this study outperforms the original SCA algorithm in terms of exploration and exploitation ability during iterations in the classical 23 classical functions test results.The new algorithm has the characteristics of high accuracy in seeking,small amount of computation required,fast convergence and robustness,and can solve each classical function problem well;(2)In the test results of five complex nonlinear constrained optimization engineering problems.The improved ISCA algorithm outperforms the comparison algorithm in solving all five classical engineering problems.This demonstrates the feasibility of the improved ISCA algorithm in solving complex non-linear constrained optimization problems.It also lays the foundation for multi-objective optimization using the improved ISCA algorithm in this study;(3)In the test results of the multi-level threshold image segmentation problem.The improved ISCA thresholding method was tested on a set of benchmark images,and two sets of experiments were designed in the results section for comparison.Based on the experimental results of statistical analysis,convergence behaviour analysis and performance index analysis,the ISCA algorithm's can fully perform the multi-level thresholding image segmentation task mentioned in the study.The effectiveness,accuracy and robustness of the algorithm in this research problem are demonstrated.In summary,numerical experiments and analysis on classical functions,engineering problems and multilevel threshold segmentation show that the proposed algorithm(ISCA)can effectively solve real-life optimisation problems.
Keywords/Search Tags:Sine and cosine algorithm, Crossover operator, Greedy selection mechanism, Multilevel threshold partitioning, Global optimization
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