This paper proposes a spherical evolution algorithm with two -stage search. spherical search and hypercube search are combined to achieve individuals' evolution. A self -adaptive Gaussian scale factor and a variab...
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This paper proposes a spherical evolution algorithm with two -stage search. spherical search and hypercube search are combined to achieve individuals' evolution. A self -adaptive Gaussian scale factor and a variable scale factor are designed to adaptively control individuals' spherical and hypercube search area according to their search situations. Two search stages frequently switch with four search cases to enhance the balance between exploration and exploitation processes. A directed adjacency matrix is devised to analyze the relationship among individuals from the perspective of graph theory. Experiments compare the proposed algorithm with five algorithms with distinctive search patterns on twenty nine CEC2017 benchmark functions. The diversity analysis and graph theory analysis show the good population diversity and effective information spreading of the proposed algorithm. Twenty two real -world problems evaluate the practicality and optimization ability of the proposed algorithm. Finally, the computational time complexity demonstrates that the proposed algorithm is more efficient than the original spherical evolution algorithm.
Nature-inspired metaheuristic algorithms are often based on the first-order difference hypercube search style to search for optimum solutions. In contrast, the spherical evolution algorithm (SE) employs a spherical se...
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Nature-inspired metaheuristic algorithms are often based on the first-order difference hypercube search style to search for optimum solutions. In contrast, the spherical evolution algorithm (SE) employs a spherical search style. SE is very effective;however, there is still room for improvement. In this study, we added a chaotic local search (CLS) to the SE to improve its performance. This CLS uses information from several chaotic maps and records each instance of success. The recorded historical success information guides the CLS to choose the chaotic map for the next iteration. In our experiment, we compare the chaotic spherical evolution algorithm (CSE) with the original SE and other metaheuristic algorithms. The test set consists of 29 benchmark functions from the CEC2017 benchmark set and 22 real-world optimization problems from the CEC2011 set. Additionally, the new parameter introduced in the CSE has also been briefly discussed. Experimental results indicate that the proposed CSE significantly performs better than its competitors.
In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed ...
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In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this *** them,the sphericalevolutionary algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization ***,it tends to stagnate prematurely to local optima in solving some specific ***,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called *** this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of *** objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with *** final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real ***,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.
How to effectively realize the simulation, evaluation, and control of the photovoltaic (PV) system established on the actual measured voltage and current PV cells and module data has attracted widespread attention. Th...
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How to effectively realize the simulation, evaluation, and control of the photovoltaic (PV) system established on the actual measured voltage and current PV cells and module data has attracted widespread attention. The original SE possesses the disadvantages of slow convergence and poor accuracy in the parameter identification of PV cells and modules. This paper proposes an enhanced spherical evolution algorithm (SE) based on a novel dynamic sinecosine mechanism (DSCSE). The introduction of the dynamic sine-cosine mechanism significantly promotes the information communication of disparate individuals and increases the diversity of diverse populations. To assess the performance of DSCSE, it is compared with ten comparative algorithms to estimate unknown parameters of PV cell and module at fixed and varying temperature and light conditions, including single diode model (SDM), double diode model (DDM), three diode model (TDM) and PV module. The experimental results indicate that the root mean square of the error (RMSE) gained by DSCSE outperforms most competing algorithms. The results of RMSE by DSCSE for SDM, DDM, and TDM of commercial solar cells R.T.C. France and PV module of Photowat-PWP201 is the percentage of improvement of 48.45%, 6.85%, 11.81%, and 4.73% compared to SE, respectively. Furthermore, for three manufacturers, including Mono-crystalline (SM55), Thin-film (ST40), and Multi-crystalline (KC200GT), the results of RMSE by DSCSE harvest the maximum and minimum increase of 96.1% and 31.36%. Therefore, DSCSE is expected to become a novel promising technology to estimate the parameter of PV cells and modules. (C) 2021 Published by Elsevier Ltd.
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