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首页> 外文期刊>Advances in Engineering Software >Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems
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Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems

机译：基于学生心理学的优化算法：基于新的群体优化算法来解决优化问题

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摘要

In this article, a new metaheuristic optimization algorithm (named as, student psychology based optimization (SPBO)) is proposed. The proposed SPBO algorithm is based on the psychology of the students who are trying to give more effort to improve their performance in the examination up to the level for becoming the best student in the class. Performance of the proposed SPBO is analyzed while applying the algorithm to solve thirteen 50 dimensional benchmark functions as well as fifteen CEC 2015 benchmark problems. Results of the SPBO is compared to the performance of ten other state-of-the-art optimization algorithms such as particle swarm optimization, teaching learning based optimization, cuckoo search algorithm, symbiotic organism search, covariant matrix adaptation with evolution strategy, success-history based adaptive differential evolution, grey wolf optimization, butterfly optimization algorithm, poor and rich optimization algorithm, and barnacles mating optimizer. For fair analysis, performances of all these algorithms are analyzed based on the optimum results obtained as well as based on convergence mobility of the objective function. Pairwise and multiple comparisons are performed to analyze the statistical performance of the proposed method. From this study, it may be established that the proposed SPBO works very well in all the studied test cases and it is able to obtain an optimum solution with faster convergence mobility.
机译：在本文中，提出了一种新的成群质优化算法（命名为，学生心理学的优化（SPBO））。所提出的SPBO算法基于学生的心理，试图提供更多努力，以便在课堂上成为最佳学生的审查中的绩效。在应用算法求解十三50维基准功能以及十五CEC 2015基准问题时，分析了所提出的SPBO的性能。将SPBO的结果与十个其他最先进的优化算法进行比较，如粒子群优化，教学学习优化，杜鹃搜索算法，共生有机体搜索，协助矩阵适应与演变战略，成功史基于自适应差分演进，灰狼优化，蝴蝶优化算法，差和丰富的优化算法和晶格交配优化器。为了公平分析，基于所获得的最佳结果以及基于目标函数的收敛迁移性，分析所有这些算法的性能。进行成对和多次比较以分析所提出的方法的统计性能。从本研究中，可以确定建议的SPBO在所有研究的测试用例中运行得很好，并且能够获得更快的收敛迁移率的最佳解决方案。

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